\r\n\tSome studies should be linked to the late-stage tumorigenesis promoting metastasis in cancer. In addition, deregulated cellular processes such as cell proliferation, apoptosis, and differentiation as related to different tumor types should be investigated in this book. Besides tumorigenesis, spontaneous tumor regression and its potential formation mechanisms should be reviewed or researched. In addition, the role of the deregulated immunity in tumorigenesis should be explored. The drug targets and treatment alternatives in various cancer types should be described or investigated in some studies. The studies relating to the laboratory tests used as diagnostic and prognostic in cancer patients should also be presented. Consequently, this book may include but is not limited to these topics.
",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:null,priceUsd:null,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"46d3363b21f482c9a22ba72cca9ec4c0",bookSignature:"Dr. Nevim Aygun",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/6919.jpg",keywords:"Tumorigenesis,clinical significance, biological/genetic features, genomic/chromosomal instability, prognosis, prognostic factors, tumor suppressor genes, promotion of metastasis, spontaneous regression, tumor stages, tumor types/subtypes, signaling pathways, signaling networks, deregulated cellular processes, immunity, diagnosis, laboratory tests, treatment , oncogenes, primary tumor progression",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 26th 2018",dateEndSecondStepPublish:"April 16th 2018",dateEndThirdStepPublish:"June 15th 2018",dateEndFourthStepPublish:"September 3rd 2018",dateEndFifthStepPublish:"November 2nd 2018",remainingDaysToSecondStep:"3 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"195365",title:"Dr.",name:"Nevim",middleName:null,surname:"Aygun",slug:"nevim-aygun",fullName:"Nevim Aygun",profilePictureURL:"https://mts.intechopen.com/storage/users/195365/images/system/195365.jpeg",biography:"Nevim Aygun received her Medical Biology and Genetics Ph.D. in Health Sciences. She is interested in cancer, molecular biology, human genetics, cytogenetics, molecular cytogenetics, genomics, and bioinformatics. She has participated in many research projects on neuroblastoma, human gross gene deletions, non-B DNA-forming sequences, solid tumors, HCV, and leukemia, resulted in six articles, one book chapter, and numerous reports. She performed many molecular biological methods: PCR, real-time PCR, bacterial transformation, plasmid vector transfection, RNA interference, fluorescence in situ hybridization (FISH), cytogenetic, DNA sequencing, and cell culture. She also performed genomics and biostatistics analyses using some bioinformatics tools and SPSS program. She reviewed several manuscripts for some medical, genetics, and genomics journals. She is the Managing Editor of a special issue in Frontiers in Bioscience now.",institutionString:"Independent Scientist",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"6",title:"Biochemistry, Genetics and Molecular Biology",slug:"biochemistry-genetics-and-molecular-biology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"177731",firstName:"Dajana",lastName:"Pemac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/177731/images/4726_n.jpg",email:"dajana@intechopen.com",biography:"As a Commissioning Editor at IntechOpen, I work closely with our collaborators in the selection of book topics for the yearly publishing plan and in preparing new book catalogues for each season. This requires extensive analysis of developing trends in scientific research in order to offer our readers relevant content. Creating the book catalogue is also based on keeping track of the most read, downloaded and highly cited chapters and books and relaunching similar topics. I am also responsible for consulting with our Scientific Advisors on which book topics to add to our catalogue and sending possible book proposal topics to them for evaluation. Once the catalogue is complete, I contact leading researchers in their respective fields and ask them to become possible Academic Editors for each book project. Once an editor is appointed, I prepare all necessary information required for them to begin their work, as well as guide them through the editorship process. I also assist editors in inviting suitable authors to contribute to a specific book project and each year, I identify and invite exceptional editors to join IntechOpen as Scientific Advisors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"54014",title:"Positive Indirect Interactions in Marine Herbivores and Algae",doi:"10.5772/67343",slug:"positive-indirect-interactions-in-marine-herbivores-and-algae",body:'\nIn this chapter, we synthesise current information and case examples of defined patterns of positive indirect interactions in marine herbivores. These types of interactions occur when one species causes a change in a second species, which successively affects a third species and where at least one species is benefited and neither is harmed [1]. Herbivores in marine ecosystems have the ability to drastically modify the biogenic structure of habitats. To date, most of the ecological literature on marine herbivory has focused on negative effects arising from the overharvest of predators or shifting environmental conditions, which can lead to a loss of structural habitat. This chapter highlights the diverse roles that herbivorous grazers can play in directly and indirectly enhancing species diversity. The importance of multispecies interactions involving herbivores has recently been recognised. We highlight that greater survivorship of contributing species inside such associations, as well as behavioural habitat selection, is important in the establishment of such interactions and that food provision is an important driver in their maintenance in marine systems. This chapter concludes with an emphasis on the importance of understanding multispecies interactions in successful management of marine ecosystems. In order to accurately predict the impact of potential perturbations and for mitigation of effects, future research should refocus on the entire ecosystem framework to capture potentially important positive indirect effects that might further define species relationships.
\nThe importance of positive interactions between species is increasingly acknowledged in contemporary ecological theory [1–3]. Such interactions occur between two or more species when at least one participant benefits and neither are harmed and take place, simply, as either commensal (+, 0) or mutual effects (+, +) [1, 2, 4, 5]. A species that has a positive effect on another is referred to as a facilitator [2, 6]. Facilitative or positive interactions tend to be most common in environments with high physical stress and/or where strong consumer pressure exists [3, 4]. Here, facilitators play a positive role by ameliorating environmental stress and by creating complex habitat that can lessen the effects of competition and/or predation [1]. Relationships between facilitators and associated organisms may be obligate or facultative, depending on the level of risk to survival for the associated species outside of the relationship [4].
Interactions between species can be both direct and indirect, yielding both positive and negative results [7–9]. A direct effect occurs as a result of a physical interaction between two species [10] and includes processes such as predation, interference competition, inhibition of recruitment, inhibition of feeding, enhancement of recruitment and provision of habitat or shelter [8]. Indirect effects occur in multispecies assemblages when the action of one species causes a change in a second species, subsequently impacting on a third species [11, 12]. This type of interaction includes processes such as keystone predation, tritrophic interactions, exploitation competition, apparent competition, indirect mutualism, indirect commensalism, habitat facilitation and associational resistance [13].
\nIndirect effects occur when a species is involved in a series of strong pairwise interactions that are not independent of other species [13]. Indirect effects generally occur in a system via two ways [13]. The first is referred to as an interaction chain where species C indirectly changes the abundance of species A by changing the abundance of an intermediary species, species B, which interacts with both [13]. The second is termed either interaction modification or higher order modification and occurs more commonly. It occurs when the abundance of species C changes, causing an indirect effect on the abundance of species A by affecting the interaction between species A and species B [13] (Figure 1).
Two fundamental ways in which indirect effects can occur within an ecosystem (adapted from [13]).
Indirect effects also arise through changes in a physical and/or chemical component of the environment, as well as through another species [14]. For example, the effects of nutrient addition to a plant-endophage-parasitoid trophic chain can result in two types of indirect effects [15]. Fertilised plants (Chromolaena squalid) produce larger flower heads that act as a shelter against endophageous insects [15], representing an interaction modification [16]. Concurrently, this fertilisation results in an increase in the nutritional quality of the plant, which in turn increases the quality of the endophages as hosts to parasites [15] representing an interaction chain [16]. A similar example involves fish foraging, which causes a direct increase in sedimentation. This in turn has an effect on the abundance of invertebrates consuming primary producers [17–19]. Chemical cues and chemical communication can also indirectly mediate behaviour and strongly affect community structure [20]. For example, when given a choice between different cue sources, fish (Lipophrys pholis) and crabs (Carcinus maenas) that consume snails (Littorina obtusata) are more attracted to algae (Ascophyllum nodosum) that have recently been grazed to algae that have not [21]. It is thought that this indirect effect may have evolved in algae as a mechanism for protection against attacking herbivores [20].
\nIndirect effects within ecosystems may have important implications. It is thus important to understand and identify such effects for the purpose of predicting system responses to certain perturbations [13]. For example, human-induced perturbations to environments, such as replacement of natural marine habitats with artificial structures such as piling, marinas and seawalls, can have extensive direct and indirect repercussions on the abundances of biota within the ecosystem, and it is important for us to be able to identify such processes [22]. Environmental impacts such the introduction, increase, reduction or extinction of species can have widespread repercussions for the rest of an ecosystem [23]. Categorisation of organisms into separate trophic levels according to their feeding preference provides a useful foundation with which to understand ecological systems [23]. Relationships between producers and consumers can be examined in this way to determine which trophic level, if removed, may control community composition [24].
\nDetection of indirect effects is, however, sometimes more complex than this, as indirect effects can be masked as direct effects within manipulation experiments. For example, when avian predation pressure was experimentally manipulated within an intertidal community, both direct and indirect effects were found [25]. An increase in predatory gulls reduced the density of the limpet Lottia digitalis [10]. The seemingly direct effect of foraging gulls on limpet abundance was later found to be partial due to an indirect effect involving a change in the abundance of the cryptic goose barnacle (Pollicipes polymerus), which comprised the habitat in which the limpet (L. digitalis) preferentially colonised [10]. As a direct result of gull predation, the area covered by the cryptic goose barnacle was dramatically reduced, thus increasing the area covered by the habitat-forming mussel, Mytilus californianus. A reduction in the preferred cryptic habitat meant an increased risk of predation for L. digitalis and thus a reduction in its abundance [6]. This released the limpet L. strigatella from exploitative competition with L. digitalis, and thus an increase in the abundance of the former was observed [10]. Results of this experiment reveal that gull predation, in fact, indirectly decreases the abundance of the limpet L. digitalis, which in turn increases the abundance of the limpet L. strigatella, via a decrease in the preferred cryptic habitat of L. digitalis, causing a reduction in the strength of exploitative competition between the two species [10]. This example demonstrates the importance of long-term experimental manipulations that consider the full complexities of the community of interest, for the purpose of detecting the underlying indirect effects. It also shows that conclusions from short-term experimental manipulations that simplify systems to direct interactions between species pairs can give questionable results [25].
\nMany direct effects within marine communities have been investigated in detail. Indirect effects, however, especially those that yield positive results, are less studied [10, 11]. The majority of indirect effects have been inferred from manipulative experiments that were designed to test other interactions rather than having been tested directly (e.g., [8, 26]). This may be due to the logistic difficulties in observing indirect effects within the marine environment or the difficulty in distinguishing between the effects of indirect and direct processes within multispecies interactions [8, 11, 12]. Nevertheless, there is little doubt that positive indirect effects are more common than historically thought and a growing body of work has revealed the importance of such effects within marine communities. Whilst there are almost an infinite number of associations involving indirect interactions between organisms, this chapter focuses on the current trends and significance of positive indirect effects that have shown to be ecologically important within benthic marine communities.
Food webs are crucial elements of community ecology as they describe the flow of energy and materials from one trophic (consumer) level to another [7, 8, 24, 27–30]. Species interactions within food webs are important when considering species demography and community structure across different habitats [23, 24]. In several cases, removal or introduction of a predatory trophic level can cause a cascading effect on other trophic levels [7, 10, 24, 31–34]. Such trophic cascades are simple indirect effects that occur as a result of consumer-resource interactions [13]. The most studied and classic marine example is the north-eastern Pacific trophic cascade involving sea otters, sea urchins and kelp [32]. Revival of the sea otter Enhydra lutris population had positive indirect effects on the near-shore benthic community structure [32] via a decrease in sea urchin Strongylocentrotus polyacanthus herbivory, which in turn caused an increase in kelp Laminaria spp. cover and habitat, as well as changes to the physical parameters of the environment (e.g., water flow, light penetration) [32, 35, 36].
\nThe potential for human-induced trophic cascades has become more apparent in recent years [9, 34]. Introduction of ‘no take’ marine reserves has reduced the impacts of humans on predatory levels in specific areas, resulting in positive indirect effect within these marine communities than can be observed for the first time [37]. A reversal in community structure was observed within Leigh Marine Reserve in New Zealand as a result of the elimination of fishing since 1976 [37]. Herbivory and the density of sea urchins declined with an increase in predation, which in turn increased the biomass of primary producers and altered seaweed community structure [37]. When comparisons were made between the area within the reserve and the area adjacent to the reserve for the 4–6 m depth zone, a marked distinction could be made between urchin-induced barrens (areas devoid of kelp) as the dominant habitat outside the reserve and the complex kelp habitat that was dominant within the reserve [37].
\nPredator diversity can strengthen positive trophic cascades by further reducing herbivory and increasing plant biomass [38]. Interspecific competition among predators is considered pivotal in maintaining food web dynamics, community structure and ecosystem functioning within marine systems [38–40]. For instance, an increase in predator diversity is believed to increase the likelihood of keystone predation or facilitation within the predatory assemblage, thus enhancing the efficiency of prey consumption [41]. Predators can affect plant biomass through ‘density-mediated indirect interactions’ (DMII), by reducing herbivore abundances, or through ‘trait-mediated indirect interactions’ (TMII) by reducing parameters such as the foraging period of herbivores [42]. Interestingly, Bruno and O’Connor [34] found that inclusion of omnivores in predator assemblages could reverse predicted positive indirect relationships between predator diversity and plant biomass. Through direct consumption of algae, omnivores effectively by-passed the trophic cascade. Thus, the magnitude and direction of changes in this community structure were due to changes in predator diversity. Cascades can sometimes be difficult to predict due to the multiple counteracting interactions that occur, especially when more generalist feeders like omnivores are included [38]. A review by Duffy et al. [31] came to a similar conclusion. Whilst horizontal predator diversity has indirect effects on primary production, the strength and sign of such effects depend on the diversity of prey types consumed (omnivore versus predator) and of course prey behaviour [43].
Indirect mutualisms can be defined as the shared indirect positive effects that one species has on another [44, 45]. They occur when the benefit exceeds the cost for both participants within an interspecific interaction (+, +) [46]. Positive interactions within the marine environment, especially mutualisms, are surprisingly widespread and play a critical role in shaping ecosystems [5]. Indirect mutualisms can arise through a number of mechanisms but typically involve a consumer-resource interaction linked with competitive interactions and are more likely to occur if the competitive relationship between resource species is strong [13]. In the presence of a competitive hierarchy between resource species, the interaction may become a direct commensalism (+, 0) [47].
\nFoundation species provide structure to the community and include groups such as kelp, coral and seagrass [5]. Mutualistic interactions frequently occur between foundation species and their residents whereby both resident and foundation species benefit [5]. This process, also known as indirect facilitation [1], will be discussed in more detail later in this chapter. Perhaps the most well-studied mutualistic interaction involving a foundation species within a marine community is that between corals and their photosynthetic dinoflagellate symbionts, zooxanthellae [5]. Photosynthesis by zooxanthellae provides the coral host with carbohydrates, whilst the resident zooxanthellae receive nutrients via nitrogenous waste from the prey of their carnivorous coral host [5]. The carbohydrates are used by the coral for calcification and growth, allowing them to grow at a rapid rate, which is necessary for survival [5]. Whether such rapid growth will be enough to ensure coral survival in many regions under rapid sea level, change is still unknown. Survival of one of the most biologically diverse ecosystems in the world would certainly be severely compromised without this mutualistic interaction [5].
\nCorals persist in tropical environments due, in part, to the efficient grazing activity of herbivores that prevent overgrowth by fouling algae [48]. Within temperate marine communities, however, fewer species of coral survive due to the competitive advantage that algae have over corals, where herbivory is less intense [48]. Contrary to this trend, the coral Oculina arbuscula persists in temperate waters off North Carolina despite the prevalence of macroalgae due to a mutualistic relationship with the omnivorous crab Mithrax forceps [46]. The coral harbours the crab, which consumes all types of algae and invertebrates inhabiting the coral. The crab uses the coral for protection from predators and gains a dietary advantage from the coral by consuming the lipid-rich coral mucus [48]. This mucus may also attract symbionts that further protect the coral from predation [48].
\nA negative consumer-resource interaction can flip to a positive interaction through changes to mutualistic effects [43]. Coralline algae, for example, are typically consumed by molluscs that scrape them from the rocks they inhabit with their hardened radulae [49]. Within the Belize Barrier Reef, approximately half of the diet of the herbivorous chiton, Chonoplax lata, is made up of its preferred coralline algal host Porolithon pachydermum [49]. Feeding by the chiton c burrows and excavates into the coralline algae, causing damage to the host [49]. When the chitons are experimentally removed, however, the coralline algae become extensively fouled by epiphytic algae, which attract deep biting by powerful herbivorous fish, including parrot fish. This form of herbivory causes substantially more damage to the coralline algae than that caused by the chiton [47]. Thus, removal of the chiton caused an increase in grazing damage rather than a decrease. Herbivorous damselfish can form similar mutualisms with algae. By protecting their food source, less grazing activity occurs to the algal mats on which they feed [50]. As a result, these algal mats are far more species rich and occur in greater biomass than those subjected to all types of grazing [50]. In fact, when damselfish are experimentally removed, these algal mats are consumed entirely within hours [50, 51].
\nMutualists in one ecological context may be adversaries in another ecological context [5]. Whilst indirect mutualism yields positive results by definition, this type of effect is often linked with negative interactions, such as exploitative competition [13]. When two competing species are considered in a community context, the effects of a nearby competitor can sometimes counterbalance the negative effects of competition by lessening physical stresses or preventing attacks by enemies [5]. A classic example is where the addition of a seastar within an intertidal community directly decreases the abundance of the resident mussels (Mytilus), which in turn makes space for competitively inferior sessile species [52]. A similar example is described by Wulff [53] whereby particular species of sponges grew better when surrounded by other species of sponges than when grown with conspecifics or when grown alone. This is thought to be due to a nearby competitor lessening the impacts of predation, acting as a positive trade-off to the negative effects of competition [5].
\nMutualistic interactions have long been considered a coevolved trait, involving species that are coupled consistently in space and time; however, this is not always the case [5]. Some interactions that appear to have coevolved do not have an obvious coevolutionary history [54, 55], suggesting that their occurrence may have arisen as an incidental benefit [56]. For example, damselfish seek refuge from predators by hiding within branching coral [5]. The damselfish benefits mutualistically the coral by providing nutrients whilst in hiding, via excretion, thus allowing the coral to grow at a faster rate [57]. Extensive branching on this type of coral is thought to have evolved in response to feeding and reproductive needs rather than to take up nutrients provided by the damselfish [5]. Similarly, growth of the brown encrusting alga Pseudolithoderma sp. is increased through uptake of ammonium by overlying live honeycomb barnacles (Chamaesipho columna) [58]. Occurrence of the alga on the barnacles is most likely due to a refuge from herbivory, and it is thought that the alga reduces the impact of desiccation for the barnacles during low tides [58, 59].
Associational resistance occurs when an organism takes refuge from predation by associating with a habitat-forming competitor (+, +) or (+, 0) [60]. Palatable marine plants, for example, are more vulnerable to herbivory when occurring alone, but herbivory is reduced and growth enhanced, when the same species grows interspersed with algae that are unpalatable to herbivores [61–63]. This is a facilitative-commensalistic (+, 0) example of associational resistance whereby the palatable plant has a clear benefit by association; however, the unpalatable plant neither benefits nor suffers [1]. Such an interaction can become antagonistic (+, −) if the palatable plant outgrows the unpalatable plant, making the unpalatable plant more attractive to herbivory [1]. In this instance the relationship could also be considered parasitic [1]. When the unpalatable plant remains dominant in the community, however, species growth and diversity can increase significantly by providing a safe haven for the palatable species [63]. This example highlights the transient nature of some associations over time, such that interactions can flip from being positive to negative and potentially back again, given particular biotic and abiotic circumstances [63].
\nMobile organisms, often herbivorous, can also take refuge from predation by association with seagrasses, kelps, corals and other sessile or less mobile organisms that provide structural and morphological defences [1]. Smaller marine invertebrates can shelter within the structurally complex habitat formed by seagrass, kelp and corals for protection from predators using their host as both food and habitat [1]. Whilst structural complexity can play a large role in providing safe havens from predation, the chemical makeup of plants can also deter larger consumers [1]. Some marine invertebrates inhabit plants that contain noxious antipredator chemicals and feed on species other than their host [1]. In such situations the benefit of refuge is thought to outweigh the importance of the quality of the food. For example, the juvenile sea urchin Holopneustes purpurascens inhabits the chemically defended foliose red alga Delisea pulchra [64]. H. purpurascens exhibits a diel pattern of movement on its host plant. It remains wrapped within its host during the day, when predation is greatest, and is more exposed at night, for purposes thought to include nutritional gain, reproduction, avoidance of photo damage and microenvironmental variation associated with the host alga [65]. When H. purpurascens reaches a certain size, it moves to a new host plant, the kelp Ecklonia radiata, on which it feeds [64]. At this point in its life history, it is thought that the benefit of a more nutritious and easily accessible food source outweighs the benefit of refuge via a chemically noxious host [64].
\nThe decorative behaviour of certain crab species with chemically defended plants is a similar scenario. The decorator crab Libinia dubia camouflages itself by covering its carapace with the chemically noxious brown alga Dictyota menstrualis [66]. The diterpene alcohol produced by the brown alga deters predators by making the alga unpalatable [66]. The diterpene alcohol also acts as a cue for the crab to commence decorative behaviour [66]. Studies have shown that without this behavioural adaptation, L. dubia would most likely become extinct [66]. It is thought that the relationship between the decorator crab L. dubia and the brown alga D. menstrualis may well be mutualistic, whereby the alga benefits though and through reduced herbivory via the consumption of amphipods by the crab [66] and gains nutritionally via crab excretion, as in the relationship between the brown alga Pseudolithoderma sp. and the barnacle Chamaesipho columna [58].
\nAssociational resistance can also occur between invertebrates. For example, less mobile sea urchins (Parechinus angulosus) provide a stable habitat for juvenile abalone that are at risk of predation by crayfish [67]. Experimental removal of urchins indirectly affected recruiting abalone by causing an increase in sediment. McClintock and Janssen [68] document a similar occurrence whereby an amphipod increased its chances of survival by capturing a chemically defended pteropod, effectively exploiting the pteropod’s chemical defence for its own protection.
\nAssociational resistance is sometimes considered facilitative when the species that provides the associational resistance is facilitated by the association. For example, an Antarctic sea urchin facilitates dispersal of chemically defended seaweeds that have become detached during storms [69]. The sea urchin exhibits a similar decorative behaviour where it collects reproductively viable individuals for camouflage to deter predation whilst also preventing the seaweed from being carried ashore or below the photic zone [69]. This example could also be defined as mutualistic.
Facilitation cascade is another example of a positive indirect effect and is commonly observed in marine herbivores and macroalgae. Within a facilitation cascade, the basal habitat former facilitates an intermediate habitat former, which in turn facilitates a focal species. In marine environments, where predation is often intense and waves and currents produce abiotically stressful conditions, positive interactions among species, such as facilitation cascades, are expected to play a particularly important role in the structure and organisation of ecological communities [1, 4, 6, 70, 71].
\nMarine benthic communities inhabit highly dynamic environments [72]. Storm surges, wave action, tides and currents, as well as biotic factors related to food web dynamics; all contribute to the dynamics of this environment [73]. Facilitator species within these systems include benthic species such as kelps [24], seagrasses [74] and mangroves [75]. These mitigate environmental stressors for associated species through substrate formation [76, 77]; enhancement of larval settlement [78]; provision of food [79]; shelter from physical forces such as wave action, tides and currents [80]; and refuge from predation [81]. These species often form large aggregations whereby facilitation of generally smaller species, often herbivores, occurs through the creation of habitat heterogeneity [76].
\nHerbivores in marine ecosystems have the ability to drastically modify the biogenic structure of habitats. Sea urchins, for example, are major grazers in rocky reef ecosystems, often maintaining areas devoid of macroalgae, namely, ‘urchin barrens’ [82]. To date, most of the ecological literature has focused on the cascading negative effects of increasing herbivore abundance arising from the overharvest of their predators or shifting environmental conditions, which can lead to a loss of structural habitat [32, 83–87]. However, some herbivores can have positive effects on particular associated species. These positive effects most likely occur at smaller scales than the negative effects associated with large-scale herbivory and often within facilitation cascades, whereby complex systems of direct and indirect pathways make them more difficult to uncover.
\nPerhaps the most common and simplest way that a herbivore can mediate a facilitation cascade is by providing shelter for other small invertebrates [88–91]. In mangrove forests, for example, marine invertebrates such as sponges and barnacles are directly facilitated by the mangroves in which they inhabit and, in turn, indirectly facilitate the mangroves by providing physical barriers, thus protecting them from wood-boring isopods [92]. Within the lagoons of French Polynesia, gammarid amphipods and chaetopterid polychaetes induce the growth of branch-like ‘fingers’ on corals through nutrient provisioning, which in turn facilitate the abundance and diversity of fishes [93]. In intertidal cobblestone beaches, cordgrass beds provide habitat for mussels, which in turn create crevice space a shelter to an array of other marine invertebrates [77]. Thomsen [94] conceptualises a specific type of facilitation cascade, described as a ‘habitat cascade’. This type of interaction is characterised when a basal habitat former, typically a large primary producer, creates space for an intermediate habitat former to live, that in turn creates habitat for the focal organism.
\nOne example of a habitat cascade mediated by a marine herbivore is that between the common kelp Ecklonia radiata, the sea urchin Holopneustes purpurascens and the gastropod Phasianotrochus eximius. Within this relationship, the intermediary species, the short-spined urchin, H. purpurascens, uses its tube feet to wrap itself in the laminae of the kelp [36, 64]. It also preferentially consumes the kelp [95]. The focal organism, the gastropod P. eximius, resides with H. purpurascens in the temporary shelter the urchin builds within the fronds of the kelp [65]. The relationship is considered facultative, as P. eximius can survive in different types of habitats but is most abundant on E. radiata plants with H. purpurascens throughout the year [96]. Due to its small size, P. eximius is likely to be vulnerable to predation outside of its preferred complex habitat structure. The modified habitat in which both species exist is thought to benefit the sea urchin by providing it with a shelter from predation but also from abrasion by kelps and other objects ‘whipping’ by in the water due to adverse abiotic factors such as wave action, tides and currents [65].
\nCovering behaviour in other species of sea urchins has also been considered an adaptation to avoid surge [97]. The sea urchin Toxopneustes roseus covers itself in shell fragments and foliose algae in areas of high surge throughout the Gulf of California [97]. It is possible that H. purpurascens has adapted in a similar way to T. roseus by covering itself to mitigate wave action within the exposed environment in which it inhabits [36]. It is highly likely, therefore, that P. eximius also benefits from inhabiting the shelter built by H. purpurascens.
\nImpacts on one species within a facilitation cascade can profoundly change the balance of the relationship. Recently, H. purpurascens in this region has been associated with the outbreak of a disease caused by the opportunistic pathogen Vibrio anguillarum [98]. The disease reduces the capacity of the urchin to wrap algae around itself and ultimately leads to death of the urchin. The disease is water-borne, and prevalence of the disease is exacerbated by increases in water temperature, such as those associated with climate change [98]. Whilst the impact of the urchin disease on the health and demography of both kelp and gastropod is currently unknown, it is highly likely that both may suffer through prolonged contact with diseased urchins. P. eximius may also face reduced availability of habitat formed by H. purpurascens should the abundance of urchins be dramatically impacted.
\nPlants often mediate facilitation cascades. These interactions typically occur in temporally separated, spatially separated or taxonomically distinct species [99–101]. Thomsen [94] investigated one particular example whereby small herbivorous marine invertebrates facilitate habitat for seaweeds, which in turn facilitate habitat for focal species of invertebrates and epiphytes. Other examples involve two levels of plant facilitation. For example, the seaweed Hormosira banksii provides habitat for the obligate epiphyte Notheia anomala, which in turn facilitates species richness and diversity of mobile invertebrates [102]. Similarly, temperate Australian mangrove forests facilitate free-living algae, which in turn facilitate a dense and diverse assemblage of epifaunal molluscs [103].
\nFor small marine herbivores, associations with larger, habitat-forming herbivores can be driven by a range of environmental obstacles that need to be efficiently overcome to survive [104, 105]. These not only include the need for shelter but also finding a reliable and nutritious food source and access to mates, the former two being generally considered the most important driving factors in habitat and/or host choice [79, 104–106]. Ideally, an individual will choose a habitat or host that provides all of these attributes [16].
\nBy investigating both the direct and indirect effects of species interactions, often a seemingly simple association will be based on more complex foundations. For example, grazing sea urchins and gastropods are directly facilitated by mussel beds by feeding on attached algae; the mussels are indirectly facilitated by the grazers that keep them free from algal growth and reduce the potential for mussel dislodgement by up to 30-fold [107]. Similarly, juvenile abalone that recruit to the underside of the sea urchin Parechinus angulosus [67] receive protection by the urchin but also provision of food via drift algae that the urchin captures on its spines for its own consumption [67]. Another example can be observed between the isopod Dulichia rhabdoplastis and sea urchin Strongylocentrotus franciscanus, which appears to be indirectly mediated [108]. Within this relationship, the isopod builds strings of detritus made from its own faecal pellets that it connects to the spines of the sea urchin [108]. The strings are colonised by a rich layer of diatoms, which the isopod subsequently consumes [108]. Here, the sea urchin indirectly facilitates the isopod by providing it with a habitat that it uses to capture its prey [108]. This species may also benefit directly by using the spines of the sea urchin as refuge when needed.
\nFacilitation cascades are not exclusive to herbivores. An invasion by non-native bullfrogs has been facilitated by the coevolved non-native sunfish, where the sunfish increased bullfrog tadpole survival by consuming dragonfly nymphs that preyed on the tadpoles [109]. Such an interaction between two non-native species also has the potential to exacerbate impacts of species invasion [109].
Positive interactions involving marine herbivores and algae have been increasingly recognised for their importance in the structure and functioning of ecosystems [94]. However, studies focusing on the role of negative species interactions in shaping ecosystems such as over harvest of predators or shifting environmental conditions, which can lead to loss of structural habitat, still far outweigh those focusing on the importance of positive effects [32, 83–87]. Herbivores in marine ecosystems have the ability to drastically modify the biogenic structure of habitats. Indirect effects add to the complexity with which ecosystems function and are intrinsically difficult to quantify, often requiring long-term and manipulative experiments [101]. Whilst interest in indirect effects has recently grown, there is still a gap in our understanding of the roles that individual indirect effects have and their importance within many systems [16]. An understanding of positive interactions, and both the direct and indirect pathways of occurrence, is essential to predict accurately the impact of potential perturbations for successful management of ecosystems. Greater survivorship of contributing species inside such associations as well as behavioural habitat selection is important in the establishment of such interactions, and food provision is an important driver in their maintenance in marine systems. Whilst difficult, future research should focus on the entire framework of these ecosystems to capture potentially important cascading effects that might further define species relationships. Experiments should centre on the effects of feeding behaviour and the nutritional benefits of association, the role of predation and the risks herbivores face beyond the association as well as environmental stressors such as wave action and climate change on the survival of associates within and outside of preferred habitats.
\nThroughout the past 50–100 years, human impacts on marine ecosystems (such as overfishing) have resulted in a downturn in the abundance of species that prey on herbivores in some areas [110]. Within such areas this has caused an increase in the abundance of herbivorous species and in turn is likely to have had a positive effect on species that associate with sea urchins [111]. Recently, however, direct threats on herbivores by humans, such as harvesting for food [112], creating suboptimal conditions that, increased sedimentation [113] and ocean acidification [114] on local to regional scales, have increased, which in turn will negatively impact on the species with which the herbivores facilitate. This issue has been identified as particularly relevant to commercially harvested species that rely on herbivore for survival, such as the abalone H. midae, which depends on the sea urchin P. angulosus throughout its juvenile stage for both food and shelter in South Africa. Depletion of sea urchin stocks in this location has seen a decline in abalone recruits, which have had significant impacts on the abalone industry in this region [67]. This chapter highlights the diverse roles that herbivorous grazers play in directly and indirectly enhancing species diversity. Unfortunately, however, the relatively unstudied nature of many species interactions within the marine environment means that many of these types of associations may disappear before we have the opportunity to understand their importance within ecosystem functioning. With a greater level of understanding of the important roles that herbivores play within various marine ecosystems, the cascading effects as a result of threats to herbivores can be managed appropriately, for the purpose of maintaining future biodiversity.
The great journey of building rational scientific knowledge includes observing, making conjectures, and severely verifying them for flaws, limitations, and errors. When conjectures falter, scientists revisit, revise, abandon, start afresh, search for alternatives, etc. They seek unity in diversity or generalize to include diversity, with the knowledge that “truth” is not knowable. In this journey, they seek to be rational, parsimonious1 in making conjectures, and methodical, open, transparent, and consistent when sharing them. Conjectures are deemed scientifically valid only if there is potential scope of finding an error [1]. “Though [a mistake] stresses our fallibility it does not resign itself to scepticism, for it also stresses the fact that knowledge can grow, and that science can progress—just because we can learn from our mistakes” [1]. The process is criticism controlled.
\nIn the last few decades, technology has provided some remarkable tools to accelerate, not merely speed up, this process, and these tools have tremendous potential of becoming even more versatile. In the context of synthetic biology, the tools include the triad: clustered regularly interspaced short palindromic repeats (CRISPR) gene editing technology in genetic engineering, artificial intelligence (AI), and quantum computing (QC). There is also a torrential gathering of data since the Human Genome Project [2] published a draft sequence and initial analysis of the human genome in February 2001 [3]. The new sources include data flowing from the Human Cell Atlas project, which plans to identify and locate every type of cell we possess [4], and various brain projects initiated in the US, Europe, Japan, and Korea, and privately funded Allen Institute for Brain Science. China and Taiwan are also getting in the fray [5]. To make sense of the growing mountains of data in terms of finding “the molecular logic of the living state” in a timely manner rather than drowning in it will require data curation and analysis tools and resources that presently only CRISPR, AI, and QC can provide. This appears fortuitous since we anticipate a catastrophic speciation of the Homo sapiens to occur soon because of a rapidly changing environment that will likely lead to its decimation unless synthetic biology comes to the rescue.
\nThis chapter is therefore written for the millennials on whose shoulders will fall the responsibility of navigating through a socioeconomic epochal change that is already under way—the emerging postindustrial era—and a possibly unanticipated speciation of the Homo sapiens. The aim is to show that the time is ripe for synthetic biology, AI, and QC to join hands and form a purposeful, integrated discipline to further explore the secrets of life, create new life, and find harmonious ways by which the Homo sapiens can speciate in a controlled manner.
\nBiology is a game of creation, survival by adaptation, and annihilation; it is a game that is “red in tooth and claw”. Survival of the fittest (also called natural selection) means survival of those best able to adapt to the environment they are in. This is not about individual survival but of cohesive groups belonging to a species capable of exchanging genes or interbreeding. Natural selection is an ultraslow process in which sudden, dramatic changes in the environment generally mean sudden decimation of species living in it. Homo sapiens already find themselves in this unenviable but self-created situation that includes climate change (that also brings deadly heat, spreads diseases, overwhelms hospitals2), epidemics, automation initiated unemployment, large-scale immigration due to ism-related (e.g., political, religion related dogmatism) strife, concentration of information and wealth in the hands of fewer and fewer people, depletion of natural resources faster than its replenishment by Nature, the rising irrelevance of rote education, the escalating cost and deterioration of health care, a rising global population that embeds a disproportionately rising population from the less developed countries (see Figure 1), the rapidly rising population of the aged whose needs must be paid for by a shrinking, less fecund, younger working population (itself worried about an insecure and financially bleak future), etc. Each by itself is a major stress creator; collectively, they are approaching a crescendo portending an environmental catastrophe that leads to speciation or extinction, and destruction of the biosphere’s existing order.
\n(Left) World population growth. (Right) World population growth, 1950–2050. Source: World Population Prospects: The 2010 Revision, United Nations, 2011, http://www.un.org/en/development/desa/population/publications/pdf/trends/WPP2010/WPP2010_Volume-I_Comprehensive-Tables.pdf Note the rapidly increasing population size in the less developed countries.
With the benefit of hindsight, we can discern the heralding signs of speciation that went unnoticed. In the rapidly growing global population (presently at 7.7 billion plus), the collective population of the more developed countries (characterized by high living standards and education, and low birth rate) since the last several decades has stabilized to about 1.3 billion (including immigrants), while that of the less developed countries (with opposite characteristics) is steadily rising. Concurrently, globally wealth has concentrated into fewer and fewer hands. In January 2018, Oxfam reported that “82% of all wealth created in the last year went to the top 1%, and nothing went to the bottom 50%”, that the wealthiest 42 people now had as much wealth as the poorest half, and two-thirds of billionaires wealth come from inheritance, monopoly, and cronyism [7, 8]. The environment for the poorest (hence unfittest) is already brutal.
\nWhen natural speciation starts, its largest and earliest victims will come from the less developed countries before it hits the developed ones. In this respect, Africa appears to be highly vulnerable; it “has become the source of some of the greatest threats to the global economic order. Rather than capitalizing on opportunities, international engagement is increasingly focused on mitigating risks” [9]. When speciation begins, these risk mitigation efforts will be in vain because it is the global socioeconomic structure itself that will be disintegrating. The Homo sapiens’ incommensurate brain power will then make it vulnerable to extinction. The historical legacy of the Homo sapiens will not be its fossil record, but its amazing science, technology, engineering, and mathematics (STEM) record for successor species, if any, to peruse.
\nSpeciation is about adapting to the environment. Homo sapiens is the only known species to have developed substantial capacity to change the environment to its needs. Thus, it reduced the pressure for speciation since the agricultural era by adopting a socioeconomic structure built around division of labor and a tolerable taxation dogma of “from each according to his ability, to each according to his need” to temper Nature that is “red in tooth and claw”. That dogma is increasingly unsustainable because of an escalating need to subsidize the less well off. The affluent 1.3 billion can no longer subsidize the life span of the rest of the unemployable world. But there is the tantalizing possibility that since synthetic biology is ultrafast in editing DNA (deoxyribonucleic acid) and with advancing AI and QC, it will be even faster and better as compared to natural mutation and it may enable the Homo sapiens to initiate its own speciation in a programmed manner and survive extinction. What we cannot predict and may even fail to control once initiated are the unintended consequences that will certainly follow. If Ray Kurzweil’s prediction about the future capabilities of AI machines (“By 2029, computers will have human-level intelligence” [10]), turn out to be reasonably true, and genetic engineering continues at its present rate of development aided by advances in QC and in understanding RNA (ribonucleic acid)-mediated cellular activity using AI, artificially induced speciation of Homo sapiens by the end of this century may become possible before natural selection steps in anger.
\nKurzweil also forecasts that the future will provide opportunities of unparalleled human-machine synthesis:
\n2029 is the consistent date I have predicted for when an AI will pass a valid Turing test and therefore achieve human levels of intelligence. I have set the date 2045 for the ‘Singularity’ which is when we will multiply our effective intelligence a billion-fold by merging with the intelligence we have created. [11]
\nKurzweil’s forecasts are based on his “law of accelerating returns” that enunciates that fundamental measures of information technology follow predictable and exponential trajectories seemingly unaffected by dramatic socioeconomic events such as war or peace, and prosperity or recession, paralleling Moore’s law in computer technology—the number of transistors on integrated circuit chips doubles approximately almost every 2 years. Indeed, it turns out that once a technology becomes de facto information technology, it comes under the grip of the law of accelerating returns because computer simulation of any technology is all about mathematics and computation. The exponential change is the inevitable effect of our ability to conceptualize in larger and larger conceptual blocks by aggregating and augmenting smaller conceptual blocks discovered earlier. This simple mechanism enables the human mind to deal with and find solutions to more complex problems by using the same number of but more versatile concepts rather than an unmanageably larger number of simpler concepts. The method is no different than what mathematicians do. We were first exposed to this method when we studied Euclidean geometry in school. Mathematicians start with simple, primitive concepts they call axioms and build more and more complex theorems as they go along. It works if the axiomatic system is consistent because once a theorem is proven, its validity can be taken for granted even by those who know nothing about mathematics, for example, by machines. This is how machines acquire “intelligence.”
\nDuring the industrial era just behind us, most people reached their peak capacity to educate and skill themselves in activities (including earning a living) that required mechanizable “intelligent” rote education. That AI machines, in principle, can far surpass humans in such activities had become evident when Alan Turing showed how arithmetical calculations can be mechanized [12] and Gödel had earlier shown that any axiomatic system can be arithmetized [13]. This meant that any form of rational knowledge could be axiomatized and rote education programmed into computers. While creating new knowledge would still require human creativity, once that knowledge had matured and was formalized into an axiomatic system, it would be mechanizable and expandable. It would then be a matter of time that humans would increasingly face competition from machines and eventually be overwhelmed by them. Kurzweil’s prediction that this would happen during 2029–2045 is bolstered by recent advances in AI. Ongoing advances in deep learning by machines indicate that through self-learning they can become highly creative and creators of original technology (the patent system will go for a toss) and scientific discoveries without human intervention may well become the norm [14]. Of some 150 predictions since the 1990s, Kurzweil claims an 86% accuracy rate [11, 15, 16]. Since synthetic biology has now come under the grip of mathematics, its exponential development is certain. Synthetic biology is now a part of information technology.
\nIt is only in the last few years that the enormous significance of the exponential growth property of the law of accelerating returns has sunk in the minds of people. As one can see from Figure 2 (left), till one reaches the vicinity of the knee of the curve, the curve looks deceptively linear with a mild slope. This allows human minds to extrapolate into the future from gathered knowledge and experience. At the knee, the curve bends upward so rapidly that the human mind cannot respond fast enough to absorb, assess, contemplate, and react rationally. Knee-jerk reaction is about the best humans are capable of in such a situation. Homo sapiens now find themselves in an environment which they neither understand nor have the intellectual ability to rationally cope with. This is germane for triggering a speciation event.
\nExponential growth. Source: (top left) Author. Nature of exponential growth. (Top right) Steve Jurvetson. An updated version of Moore’s Law (based on Kurzweil’s graph). Wikimedia Commons. https://commons.wikimedia.org/wiki/File:Moore%27s_Law_over_120_Years.png
Once AI breaches a certain threshold, one should expect a runaway technological growth resulting in a phase transition in human civilization, including perhaps the speciation of the Homo sapiens. A likely component of the phase transition may well be that AI enters self-improvement cycles (feedback loops) that eventually cause it to evolve into a powerful level of superintelligence that would qualitatively surpass intelligence levels of all Homo sapiens. The accelerating progress of STEM in concert has also brought about commensurate changes in our lifestyle, expectations from life, and erosion of superstition and belief in religion. In the last few decades, the exponential nature of these changes has become noticeable and taxing enough even for the socioeconomic upper strata Homo sapiens to cope with. When, to mitigate their anxiety about AI, people claim that AI cannot do this or that which humans can, they often forget to ask if those tasks are worth doing.
\nExponential growth in AI has advanced the possibility that artificially induced speciation of Homo sapiens may occur by the end of this century. Recent findings show that Homo sapiens evolved about 300,000 years ago [17, 18].3 In recent times, their socioeconomic environment too has changed dramatically. Billions face the prospect of AI machines depriving them of sustainable livelihood and a dignified existence in society. Under such dramatic conditions of environmental change, Nature will force speciation toward life forms with an evolved brain far superior to that of the Homo sapiens. The very process may start too late and move too slowly and lead to the extinction of the Homo sapiens. Artificially induced speciation may therefore be the only means that may allow the Homo sapiens to transition to a new species in a controlled manner. On the flip side, one or more renegade group of Homo sapiens may strategize to surreptitiously create a colony of new species with the aim of dominating the Earth and decimating the Homo sapiens as an unnecessary burden on Earth.
\nThe language of information now pervades molecular biology—genes are linear sequences of bases (like letters of an alphabet) that carry information (like words) to produce proteins (like sentences). For the process of going from DNA sequences to proteins, we use words like “transcription” and “translation,” and of passing genetic “information” from one generation to another. It is rather uncanny that molecular biology can be understood by ignoring chemistry and treating the DNA as a computer program (with enough input data included) in stored memory residing in a computer (the cellular machinery). It is this aspect that bioinformatics exploits. It is analogous to viewing Euclidean geometry not in terms of drawings but in terms of algebra.
\nIn a sense, in the DNA sequences in our cells, written using an alphabet of only four letters, lies hidden the story of who we are and where we come from. For all we know, it might even tell us where we might be going. Albert Lehninger wrote:
\n… living organisms are composed of lifeless molecules … that conform to all the laws of chemistry but interact with each other in accordance with another set of principles—the molecular logic of the living state. [19]
\nIt is this “molecular logic of the living state” that is yet to be completely understood, and therein may lie our ability to understand emotion, cognition, and intelligence. So, in a deep sense, the DNA is the master molecule of life. A marvelous thing about cells is that they are so designed that for many purposes one can totally ignore their chemistry and think just about their logic. The fact that one can get away with this is one of the most elegant aspects of molecular biology. The algorithmic side of molecular biology is bioinformatics, the study of information flows in living matter. Bioinformatics is about the development and application of algorithms and methods to turn biological data into knowledge of biological systems. Of fundamental interest is the organization and control of genes in the DNA sequence, the identification of transcriptional units in the DNA, the prediction of protein structure from sequence, and the analysis of molecular function. If there is mathematical logic in living things, then one naturally seeks to determine the formal mathematical system that governs life, that is, how information in the DNA is stored and used by the rest of the cell’s machinery to do the myriad of things that it does.
\nWe already know that a DNA molecule—a genotype—is converted into a physical organism—a phenotype—by a very complex process, involving the manufacture of proteins, the replication of the DNA, the replication of cells, the gradual differentiation of cell types, and so on. This epigenetic process is guided by a set of enormously complex cycles of chemical reactions and feedback loops. By the time the full organism appears, there is no discernible similarity between the physical characteristics of the organism and its genotype. Yet molecular biologists attribute the physical structure of the organism to the information encoded in its DNA, and to that alone. This is because there is overwhelming experimental evidence that only DNA transmits hereditary properties. The genotype and the phenotype are isomorphic. However, this isomorphism is so complex that so far it has not been possible to divide the phenotype and genotype into parts, which can be mapped onto each other directly, unlike as, say, in the case of a music record and a record player where portions of a record’s track can be easily mapped to specific musical notes [20]. One hopes that AI and QC together will enable us to find this complex mapping. It is all about information processing.
\nBy information we mean the precise determination of sequence, either of bases in the nucleic acid or of amino acid residues in the protein. We gain knowledge of biological systems when we can interpret information in some “meaningful” way without it being easily refuted. That is, we make conjectures and put them through rigorous tests of refutations. Molecular biologists are becoming increasingly sure that “life is a partnership between genes and mathematics” [21]. Indeed, we increasingly tend to believe as Max Tegmark does about the Universe itself:
\nOur reality isn’t just described by mathematics – it is mathematics … Not just aspects of it, but all of it, including you. [In other words,] our external physical reality is a mathematical structure. [22]
\nOne can well imagine the enormous strides synthetic biology will make when researchers get a deeper understanding of the Book of Life, with AI software becoming their research assistant, and quantum computers executing all the computing required by the AI software.
\nThe time has come for synthetic biology, AI, and QC to join hands and form a purposeful, integrated discipline to further explore the secrets of life, create new life, and find harmonious ways by which Homo sapiens can speciate. The main responsibility will fall on the shoulders of the millennials. The technology triad (CRISPR, AI, and QC) share some important properties, the ability to create, share, process, and communicate information in digital form. This means they can be supported and integrated with the full power of mathematics and physics. As Richard Feynman notes:
\nMathematics is a language plus reasoning; it is like a language plus logic. Mathematics is a tool for reasoning. … [I]t is impossible to explain honestly the beauties of the laws of nature in a way that people can feel, without their having some deep understanding of mathematics. [23]
\nMathematics is the lingua franca of the physicists because a formal mathematical statement to be of any value is either true or false; it cannot be true to some and false to others. This is the reason why knowledge based on any axiomatic system, that is, a consistent system in which every valid statement or query has a “yes” or “no” answer, can be arithmetized (i.e., translated into arithmetical statements), encoded in a binary string, and processed in a digital computer. Mathematics is the language that binds men and machine together in a rational dialog. In short, axiomatic systems permit men and machines to mutually communicate without ambiguity or confusion. This is the foundation on which artificial intelligence (AI) rests. It is why Pierre Simon Marquis de Laplace did not even acknowledge God as the creator of the Universe in his mathematical magnum opus on celestial mechanics [24]. He famously told Napoleon Bonaparte, “I had no need of that hypothesis” [25].
\nCreating and advancing rational knowledge, inter alia, requires an ability to communicate thoughts concisely, precisely, and accurately apart from refining knowledge by trial and error, that is, by making our conjectures fitter and fitter for survival. Benjamin Lee Whorf (1897–1941) said, “Language shapes the way we think, and determines what we can think about.” And Ludwig Wittgenstein (1889–1951) said, “The limits of my language mean the limits of my world.” Mathematics provides fewer limitations than any other language known to Homo sapiens. The power of mathematics lies in its ability to extract unity from diversity by abstraction, that is, by eliminating unnecessary context; it helps in discovering group properties (abstract or otherwise) common to all members of the group, for example, the DNA of a species.
\nBoth AI and QC are inseparable from mathematics; they are powerful means of processing and interpreting information (e.g., in the DNA) as well as aiding in inventing novel DNA for specific purposes. Both support and are supported by 3D printing that began by making plastic widgets, but now make guns, houses, prosthetic limbs, vehicle parts, etc. from inanimate matter. The day is not far off when it will advance to printing living, breathing, bio-organs, such as hearts [26] and kidneys, using nanotechnology, computer-aided engineering, and inanimate biodegradable or biocompatible materials and chemicals to build stem cells. Replicating and growing cells, say, in petri dishes is well established, and such cells are already in use as bioink in bioprinters. 3D printing offers the possibility of printing an entire organ, along with a system of arteries, capillaries, and veins that can support it [27, 28]. A major issue in developing this technology is to make it immune-system friendly, since the body may reject organs or cells thus produced, something that can occur even when tissue from one area of the body is put into another.
\nCRISPR technology has enabled a simple and affordable method of manipulating and editing DNA that has radically changed the ambitions of synthetic biologists. The technology promises to revolutionize how Homo sapiens may deal with the world’s biggest problems, for example, finding cures for cancer, blindness, and Alzheimer’s disease, improving food and eliminating food shortages, fulfilling organ transplant needs, and producing fuel and manufacturing chemicals. Biotechnologists are racing to develop the most efficient, precise, versatile, affordable, and commercially viable genome-editing tools possible. This will be a long and exciting race that may eventually lead to the Homo sapiens creating a super species that far exceeds them in the evolutionary path in a controlled manner.
\nCRISPR is a series of short repeating DNA sequences with “spacers” separating them. The CRISPR technology harnesses an ancient bacteria-based defense system. Bacteria use these genetic sequences to “remember” the viruses that have attacked them by the simple mechanism of incorporating the virus’ DNA into their own bacterial genome. The viral DNA thus resides as spacers in the CRISPR sequence as identification tags the bacteria can use to mount an attack if the virus attacks again. Accompanying the CRISPR are locally stationed genes called Cas (Crispr-associated) genes. Once activated, these genes produce enzymes that act as “molecular scissors” that can cut into DNA with specificity. The significance is that in subsequent virus attacks, the bacteria can recall the virus signature and send RNA and Cas to locate and destroy the virus. Among the Cas enzymes derived from bacteria, Cas9 is the best-known molecular scissors enzyme for cutting animal and human DNA. Although the CRISPR sequence was first discovered in 1987, its function was discovered only in 2012.
\nThe ability to cut DNA allows one to either knock out, say, an unwanted disease-causing gene, or splice a “fixed” version of a gene into the DNA. This is analogous to the “Find & Replace” function in text editing software. Indeed, CRISPR technology has advanced so rapidly beyond the Find & Replace function that by December 2017, the Salk Institute had designed a version of the CRISPR-Cas9 system that could switch on or off a targeted gene without even editing the gene. The basic ingredients of gene editing are (1) a piece of RNA, called the guide RNA, that locates the targeted gene, (2) the scissors (the CRISPR-associated protein 9), and (3) the desired DNA segment for insertion after the break. Once the guide RNA locates the targeted gene, Cas9 makes a double-stranded break in the DNA carrying the targeted gene and replaces it with the desired DNA segment. A quick tutorial on CRISPR is available at [29].
\nCRISPR-based therapies are still nascent. As expected, single-gene disorders are among the best understood because of their simple inheritance patterns (recessive or dominant) and relatively simple genetic etiology (cause). Such disorders include cystic fibrosis, hemochromatosis, Tay-Sachs, and sickle cell anemia. For example, cure for sickle cell disease (an inherited form of anemia in which distorted red blood cells—rigid, sticky, and shaped like sickles—are present in such numbers as to prevent adequate oxygen supply throughout the body) has gained prominence because it is related to an abnormal hemoglobin molecule, which comes from a well-understood genetic mutation. Hence efforts are concentrated on creating therapeutic strategies for fixing the mutated gene. Online Mendelian Inheritance in Man® (OMIM®) provides an Online Catalog of Human Genes and Genetic Disorders, a comprehensive database provides information about the etiology, clinical symptoms, and a bibliography of thousands of genetic conditions.4
\nThe true test of intelligence is not how much we know how to do, but how we behave when we don’t know what to do. [30]
\nThis behavior is a product of the brain-mind system an individual is born with and the environment it finds itself in. From conception to death, behavior and intelligence evolve through intimate interaction between the individual and the environment where the individual essentially tries to coexist with the environment by exploring networking strategies, inter alia, based on its information gathering and processing abilities (see Section 5.2). In the past few decades, in an ongoing process, the Homo sapiens using technology they have intelligently developed have already acquired massive amounts of information and placed it in easily accessible public repositories along with some sophisticated automated information processing services. This has happened unexpectedly, suddenly, and on a massive scale at an exponential rate in multiple disciplines (including molecular biology) due to breakthroughs in communication and computing technologies engineered by an exceptionally intelligent group of Homo sapiens. This development is well on its way to dwarfing the intellectual abilities of almost all Homo sapiens. In comparison, individual human brain capacity to understand, assimilate, create, and deal with knowledge appears pathetic and along with it, its ability to find gainful employment in the future. Machines are rapidly learning to create and deal with knowledge. On the positive side,
\nThere is a paradox in the growth of scientific knowledge. As information accumulates in ever more intimidating quantities, disconnected facts and impenetrable mysteries give way to rational explanations, and simplicity emerges from chaos. [31]
\nIt is this scientific knowledge ferreted out by a few geniuses among the Homo sapiens, which has allowed the species to extend their life span and improve their lifestyle by not just adapting to an environment but also by aiding the environment to adapt to humans. Along the way, Claude Shannon provided a mathematical theory that highlighted an important aspect of how data can be condensed and communicated efficiently in binary bitstreams. This was an important step in handling data by finding structure in data to reduce redundancy in data representation [32].
\nBig data revolution, development and deployment of wearable medical devices, and mobile health applications have provided new powerful tools to the biomedical community for applying AI and machine learning algorithms to vast amount of data. Its impact in predictive analytics, precision medicine, virtual diagnosis, patient monitoring, and drug discovery and delivery is already being felt. More powerful advances are anticipated in the near future. Even at this early stage, AI excels even human experts in certain well but narrowly defined tasks. AI is at a stage where basic building blocks are being built. Soon we will learn to network these blocks and build increasingly powerful systems and subsystems that will solve increasingly complex problems and even create new knowledge. We already have a glimpse of it in Alphabet’s AlphaGo Zero’s ability to learn complex decision-making from scratch [33, 34]. “Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0” [34]. It acquired this ability within 40 days of self-training in an essentially iterative manner. The key here is the iterative strategy it used. Indeed, Homo sapiens too acquire knowledge iteratively but slowly over years and generations, collaboratively across space and time with other Homo sapiens, by making conjectures and refutations. It is rather uncanny that the essence of the process and its unusual power is mathematically captured by the Mandelbrot set in fractal geometry (see Section 5.3).
\nNotwithstanding AlphaGo’s success, many real-life problems are still far too difficult not just for current AI systems but also for the vast-vast majority of Homo sapiens. The competition is really between two classes of geniuses: Homo sapiens who create ab initio knowledge and Homo sapiens who develop AI. Eventually, the latter is expected to win even if they must create an artificial brain using synthetic biology and place it in a humanoid! The task is enormously complex but not out-of-reach, in principle. What is needed is the ability to automate the task of observing and collecting data about the world and about us, create categories, data structures, and algorithms that would enable the collected data to be condensed into a computer program that can calculate the observations. This necessarily means that the size of the computer program (say, as represented by a binary string) must be as compact as possible (an index of the AI system’s intelligence) compared to the collected data (also represented by a binary string). Till this is accomplished, the collected data would remain incomprehensible, that is, algorithmically random, theory-less, unstructured, and irreducible [35]. This is what Homo sapiens in the genius class devote themselves to. As Oren Etzioni notes, machine learning is still 99% human work:
\nThe equation for AI success is to take a set of categories (for example, cats and dogs) and an enormous amount of data (that is labeled as to whether it is a cat or a dog), and then feed those two inputs through an algorithm. That produces the models that do the work for us. All three of those elements—categories, data, algorithm—are created through manual labor. [36]
\nThe solution to eliminating manual labor may well be the creation of an artificial brain using synthetic biology. For the present, AI serves mainly by “augmenting human intelligence”. But then automation too had begun by augmenting brawn (muscle) power to eventually become the superbrawn power during the industrial revolution. It only required the Homo sapiens to intelligently harness and control steam by first connecting water, heat, and work and then creating the thermodynamics, the science that would allow machines to make human brawn power look insignificant. Today’s augmented intelligence appears destined to become superintelligence. We have learnt to harness and control reasoning by first connecting logic, axiomatic systems and theorem proving. We are now advancing rapidly into understanding information theory so that quantum computers can become information engines to do intelligent work. It is interesting that the concept of entropy appears fundamental both in thermodynamics and information theory. Both are offsprings of rational thought in physics, and both are intimately related.5
\nQuantum mechanics deals with the world inhabited by photons, electrons, protons, atoms, molecules, etc. and how they interact among themselves to create larger matter entities. It is an incredibly mysterious world understood only in the language of advanced mathematics. This is the part of physics that tells us how atoms congregate into molecules by adjusting the electrons they carry into configurations that we call chemical bonds, how strong or weak those bonds will be or whether they will bond at all, what a congregation’s physical and chemical properties will be. It has led to many technical innovations and many more are expected, for example, in synthetic biology. The success of quantum mechanics in using mathematical abstractions is such that to a lay person it appears mystical, which even religious mystics cannot understand! Its remarkable success comes even though we still do not know what is meant by measurement in the quantum world and how the measurement process captures the information it outputs and why it releases information in a randomized way. Yet its success is undeniably visible:
\nQuantum mechanics is an immensely successful theory. Not only have all its predictions been experimentally confirmed to an unprecedented level of accuracy, allowing for a detailed understanding of the atomic and subatomic aspects of matter; the theory also lies at the heart of many of the technological advances shaping modern society – not least the transistor and therefore all of the electronic equipment that surrounds us. [38, 39]
\nUnderstanding quantum mechanics is out of reach except for a few thousand people in the world at any given time! This should immediately alert us to the fact that human intelligence needed to cope with AI-QC combination in the future will be very high and successor species of the Homo sapiens must evolve in the direction of better and smarter brains rather than any other physical trait. Computation, comprehension, and cognition are all a part of the brain’s activity, and we may assume that a sharper brain will come with a sharper mind. And we may further assume that comprehension and cognition are driven by computation in addition to using intuition, serendipity, flashes of inspiration, and inputs from the environment, etc. The keys are computation, problem-solving algorithms, and rational decision-making processes. These can be simulated by a classical computer, which itself has an abstract mathematical description we call the universal Turing machine (UTM) [12].
\nComputing technology has now advanced to a stage where quantum computers can do everything that a UTM can do, and some more. A quantum computer’s phenomenal computing power comes from the extraordinary laws of quantum mechanics that include such esoteric concepts as superposition of quantum states, entanglement (“spooky action at a distance”), and tunneling through insulating walls, which, though highly counterintuitive, play extremely useful roles in understanding Nature at subatomic levels. However, it is not clear if these concepts can be ignored in biology and living processes in the way they are ignored in the design of cars and airplanes. May be not because there are areas in biology where quantum effects have been found, for example, in protein-pigment (or ligand) complex systems [40]. Thus, while the role of quantum mechanics is clear in quantum computing and hence in advancing both AI and synthetic biology research, it is not yet known if in the design of DNA, knowledge of quantum mechanics is required or that natural selection favors quantum-optimized processes. Essentially, we do not know if any cellular DNA maintains or can maintain sustained entangled quantum states between different parts of the DNA (even if it involves only atoms in a nucleotide). But we cannot rule out the possibility that sporadic random entanglements do occur that result in biological mutations or that researchers will not be able to achieve it in the laboratory and find novel uses for it in synthetic biology [41]. For example, in principle, it is possible to design molecular quantum computers, insert them in cells that can observe cellular activity, and activate select chemical pathways in the cell in a programmed manner. There is increasing speculation that some brain activity, for example, cognition, may be quantum mechanical [42].
\nA combination of emerging technologies such as CRISPR, AI, and QC; new delivery models for products and services that form the core around which Homo sapiens organize themselves through collaborative division of labor; and talent migration, driven not by rote education but by innate creativity and global opportunities for employment open to them is disrupting and changing the character of the global talent pool that society needs today. Globalization has created opportunities for the talented to reach the skies, but in a resource-constrained world, it also means that many others must be or feel deprived. Sections 5.2 and 5.3 provide some glimpses of the dynamics of this situation captured in mathematics. Because mathematics is abstract, the depicted dynamics apply to entities and situations whether they are animate or inanimate. A resource-constrained world provides ample scope for adversarial dynamics in which some are predators and others are preys. Globalization has accentuated the problem at all levels of social structure, and since speciation is triggered by a changing environment, it affects the DNA. This has created survivability demands on the Homo sapiens. As this pressure mounts beyond endurance, Homo sapiens will face speciation by natural selection with uncertain outcomes. However, in the case of Homo sapiens, this process too may face a disruptive change because the highly intelligent among them may boldly initiate speciation using upcoming advances in synthetic biology, perhaps after perfecting their techniques by creating humanoids (a hybrid creation of life with embedded intelligent machinery). This will be a watershed event where a species takes on the task of speciation on itself. This remarkable possibility arises because Homo sapiens created and mastered mathematics, rational thought, computing machinery, and eventually deep data analytics so that life could be designed by them in the laboratory to create superior species.
\nSynthetic biology, using methods and rational knowledge of molecular biology, physical sciences, and engineering, aims to design and construct novel biological parts, artificial biological pathways, devices, organisms, and systems for useful purposes. This will also permit us, at all levels of the hierarchy of biological structures (molecules, cells, tissues, and organisms), to redesign existing natural biological systems and may even help us recreate certain extinct species (if we can also recreate the environment, they had adapted to). It is not surprising that an extinct species has never revived itself since speciation and environment go together. Successes of synthetic biology will change the face of human civilization and almost certainly bring in new elements into play when Homo sapiens eventually speciate by playing an active role in it.
\nSince the discovery of the double-helix structure of cellular DNA by James Watson and Francis Crick in 1953 [43] and its significance that the “precise sequence of the bases is the code which carries the genetical information …” (emphasis added) [44], the jargon and theory of information has invaded molecular biology (see Section 3). This enriched biotechnology and computational biology with nomenclature, definitions, concepts, and meanings. This also facilitates integration of synthetic biology with AI and QC. DNA is an information-carrying polymer. It is an organized chemical information database that inter alia carries the complete set of instructions for making all the proteins a cell will ever need.
\nJust 20 years after Watson and Crick, in 1973 Cohen and Boyer published their pioneering work in recombinant DNA [45] and gave birth to genetic engineering and the biotechnology industry based on their patents [46] under liberal licensing terms. The next landmark was the creation of a bacterial cell controlled by a chemically synthesized genome by Craig Venter and his group in 2010 [47]. In 2014, Floyd Romesberg and colleagues [48] reported the creation of a semisynthetic organism with an expanded genetic alphabet by creating artificial nucleotides not found in Nature. Since its discovery in 2012 [49, 50, 51], CRISPR gene editing technology pioneered by Jennifer Doudna and Emmanuelle Charpentier, and Feng Zhang has come to occupy center stage in molecular biology as a new way of making precise, targeted changes to the genome of a cell or an organism. It has set the stage for major advances in synthetic biology (see Section 4.1). Another major advance was reported by Venter and his research group in March 2016 following their successful creation in 2010 of a bacterial cell controlled by a chemically synthesized genome noted above. In fact, they succeeded in creating a bacterium that contains the minimal genetic ingredients needed for free living. The genome of this bacterium consists of only 473 genes, including 149 whose precise biological function is unknown. It is a minimalist version of the genome of Mycoplasma mycoides [52, 53].
\nSynthesis capabilities have developed at a pace where DNA synthesis is now automated. All one needs to do is to provide the desired DNA sequence to a vendor. Researchers in synthetic biology are now inching toward anticipating and preempting evolutionary events that if left to themselves would perhaps take a few million years to occur, and of even resurrecting extinct species. The time is ripe to integrate synthetic biology with AI and QC with a common language to enable seamless communication among them, connect with, and discover conceptual similarities for consistent integration of subsystems and validation of the whole system. That common language is mathematics; it comes with the added benefit that it can be used to also communicate between humans and machines. It is fortuitous that the DNA serves as the “Book of Life” that appears to have structure and grammar amenable to translation into mathematics. Once translated, biologists will discover some amazing patterns that have a direct bearing on life at the molecular level. We introduce a few of these below in brief.
\nAll macromolecules are constructed from a few simple compounds comprising a few atoms. It appears paradoxical that the DNA that serves as the epitome of life is itself lifeless. The molecule conforms to all the physical and chemical laws that describe the behavior of inanimate matter. All living organisms extract, transform, and use energy by interacting with the environment. Unlike inanimate matter, a living cell has the unique capacity, using the genetic information contained completely within itself, to grow and maintain itself and do mechanical, chemical, osmotic, and other types of work. But its most unique attribute is its programmed capacity to self-replicate and self-assemble. The great mystery that engulfs molecular biology is: “How does life emerge from an interacting collection of inanimate molecules that constitute living organisms to maintain and perpetuate life?” Once this is understood, chemical engineers will create a new life industry and commoditize it! Imagine buying customized pets as starters.
\nAs noted in Section 3, the mystery of life is almost certainly encoded in mathematics. The chemical basis of life is one indication because chemistry now has a strong mathematical foundation via quantum chemistry. Even more striking is the fact that all living organisms—bacterium, fish, plant, bird, animal—share common basic chemical features, for example, the same basic structural unit (the cell), the same kind of macromolecules (DNA, RNA (ribonucleic acids), and proteins) built from the same kind of monomeric subunits (nucleotides and amino acids), the same pathways for synthesis of cellular components, the same genetic code, and evolutionary ancestors. The monomeric subunits can be covalently linked in a virtually limitless variety of sequences just as the 26 letters of the English alphabet or the two binary numbers (0, 1) in binary arithmetic can be arranged into a limitless number of strings that stand for words, sentences, books, computer programs, etc.
\nOrganic compounds of molecular weight less than about 500, such as amino acids, nucleotides, and monosaccharides, serve as monomeric subunits of proteins, nucleic acids, and polysaccharides, respectively. A protein molecule may have a thousand or more amino acids linked in a chain, and DNA typically has millions of nucleotides arranged in sequence. Only a small number of chemical elements from the periodic table of chemistry appear in biomolecules. The carbon atom dominates and, by virtue of its special covalent bonding properties, permits the formation of a wide variety of molecules by bonding with itself, and atoms of hydrogen, oxygen, nitrogen, etc. Nature has placed further constraints. DNA is constructed from only four different kinds of subunits, the deoxyribonucleotides; the RNA is composed from just four types of ribonucleotides; and proteins are put together using 20 different kinds of amino acids. The 8 kinds of nucleotides (4 for DNA and 4 for RNA) from which all nucleic acids are built and the 20 amino acids from which all proteins are built are identical in all living organisms. So, at this level, living organisms are remarkably alike in their chemical makeup. This by itself provides a tantalizing hope that the DNA may indeed be completely decipherable as to its grammar and information content.
\nThe above observations strongly suggest the likelihood of an underlying, as yet undiscovered set of “axioms” of life that enforce emergent, organizing principles around which diverse life forms evolve and adapt to the environment at various levels, without transgressing any physical or chemical law. The organizing principles appear to include (1) Nature is red in tooth and claw (species are connected to each other in a predator-prey, food-chain relationship in a sparse resource matrix), (2) rules of genetic inheritance, (3) rules of environmental adaptation, and (4) rules of speciation. At each level, the rules are likely to appear stochastic given that there are innumerable interacting factors ranging from nature to nurture.
\nIn 1960, Erdős and Rényi [54, 55] proved a remarkable result in graph theory, which implies that when a large number of entities (e.g., men, machines, ideas, or arbitrary combinations of them represented by dots) begin to connect (link) randomly, a critical condition arises, following which a phase transition occurs in the way the entities form or reform into clusters of connected entities. The critical condition is reached when in a set of n dots, n/2 random links are made. The phase transition abruptly creates a giant connected component, while the next largest component is quite small. Such giant components then grow or shrink rather slowly with the number of dots as they continue to link or delink. Such behavior is observed in protein interaction networks, telephone call graphs, scientific collaboration graphs, and many others [56]. This immediately suggests an involuntary mechanism by which a society at various levels of evolution, by connections alone, spontaneously reorganizes itself as nodes (people, machines, resources, etc.) link or delink in apparent randomness. It is highly pronounced in an Internet of Things (IoT) connected world where the millennials spontaneously polarize on issue-based networks that concern them on social media.
\nSynthetic biologists must never forget that between the molecular and environmental levels, there are multiple intermediate levels through which regulated command and control communications pass. At all levels, level-related phase transitions and predatory fights for resources can occur and spread to other levels. In fact, the intimately coupled relationship between Homo sapiens and the environment is often overlooked. We rarely note what Richard Ogle has that
\n[I]n making sense of the world, acting intelligently, and solving problems creatively, we do not rely solely on our mind’s internal resources. Instead, we constantly have recourse to a vast array of culturally and socially embodied idea-spaces that populate the extended mind. These spaces … are rich with embedded intelligence that we have progressively offloaded into our physical, social, and cultural environment for the sake of simplifying the burden on our own minds of rendering the world intelligible. Sometimes the space of ideas thinks for us. [57]
\nThe deep significance of this intimate bonding between the Homo sapiens and the environment is that while they are adapting to the environment, they are also helping the environment to adapt to them. When entities connect, they also acquire emergent properties by virtue of the relationships they are bound by. Certain static group properties emerge based on the network’s topology, while dynamic properties emerge depending on the rate at which entities make, break, or modify connections. The fluctuating dynamics witnessed in the social media, for example, is common among the millennials.
\nRapidly increasing connectivity among men and machines has imposed upon the global socio-politico-economic structure, a series of issue-dependent phase transitions. More will occur in areas where massive connectivity is in the offing. Immediately before a transition, existing man-made laws begin to crack, and in the transition, they break down. Posttransition, new laws must be framed and enforced to establish order. Since such a phase transition is a statistical phenomenon, the only viable way of managing it is to manage groups by abbreviating individual rights. The emergence of strongman style of leadership and its contagious spreading across the world is thus to be expected because job-seeking millennials will expect them to destroy the past and create a new future over the rubble. It appears inevitable that many humans will perish during the transition for lack of jobs or their inability to adapt to new circumstances. Robots and humanoids will gain domination over main job clusters, while society undergoes radical structural changes. Ironically, robots neither need jobs, nor job satisfaction, nor a livelihood. There will be ruthlessness in the reorganization.
\nConsider the iteration xn + 1 = r xn (1 – xn), called the logistic map, and a number-pair (r, x0) where r > 0 and 0 < x0 < 1, and plot the points (r, xn → ∞). Note our interest is only in the long-term trajectory of x0 and not in its transitory phase. Note xn + (1 – xn) = 1. The plot (Figure 3) has numerous 2-pronged pitchforks and hence is called the bifurcation diagram. Depending on r, xn may be settled as for 0 < r ≤ 3, and beyond r = 3 migrating from one prong to another of available pitchforks for a given r in the bifurcation diagram. At r = 4 and beyond, migration is chaotic. In between r = 3.5 and 4, there is an intuitively unexpected white band where migration options are few. Such and other unexpected (not discussed here) display of rich complexity tethered to r independent of x0 (i.e., the starting state) caught researchers by great surprise.
\nThe logistic map.
There are countless situations for which the logistic map captures the essence of a situation. For example, in genetics it describes the change in gene frequency in time, or in epidemiology the fraction of the population infected at time t, or in economics it depicts the relationship between commodity quantity and price, or in theories of learning the number of bits of information one can remember after an interval, or in the propagation of rumors the number of people who have heard the rumor after time t, etc. The logistic map allows us to assess the volatility of an adversarial environment by assessing r, that is, the ferocity with which the predators and preys are battling for resources.
\nNow consider the following complex iteration. Given the complex variable z = x + iy, where \n
Mandelbrot set.
In 1981–1982, Adrien Douady and John H. Hubbard [58] proved that the Mandelbrot set is connected. Quite astoundingly, the Mandelbrot set, when magnified enough, is seen to contain rough copies of itself, tiny bug-like objects (molecules) floating off from the main body, but no matter how great the magnification, none of these molecules exactly match any other (see Figure 5 and follow the white-bordered square from left to right). The boundary of M is where a Mandelbrot set computer program spends most of its time deciding if a point belongs there or not. The simplicity of the iterative formula and the complexity of the Mandelbrot set leave one wondering how such a simple formula can produce a shape of great organic beauty and infinite subtle variation.
\nInfinite variations of the Mandelbrot set are embedded in the set itself. Source: Ishaan Gulrajani, A zoom sequence of the Mandelbrot set showing quasi-self-similarity, 01 October 2011, https://commons.wikimedia.org/wiki/File:Blue_Mandelbrot_Zoom.jpg (Placed in public domain).
Since the logistic map and the Mandelbrot set map quadratic functions, and both represent behavior under iteration, it is not surprising that a one-to-one correspondence exists between the constants r and c and that the bifurcations created by r correspond to features that come with changes in c along the real axis where the Mandelbrot set compresses the information in the bifurcation diagram, that is, the map shows the points where the map converges to periodic oscillations and its periodicity, while the Mandelbrot set marks all the points, which end up oscillating, but the periodicity information is encoded in the bulbs of the set (see Figure 6).
\n(Left) Connection between the logistic map and the Mandelbrot set. (Public domain) Source: Georg-Johann Lay, 07 April 2008, at https://commons.wikimedia.org/wiki/File:Verhulst-Mandelbrot-Bifurcation.jpg. (Right) Frank Klemm, Mandelbrot set with periodicity of limiting sequences. 12 August 2017. https://commons.wikimedia.org/wiki/File:Mandelbrot_Set_%E2%80%93_Periodicities_coloured.png licensed under the Creative Commons Attribution-Share Alike 3.0 Unported.
It appears that the Mandelbrot set, inter alia, mimics the working of the mind. Its infinitely many variations embedded within itself seem to say that once the mind latches on to an idea and begins to deeply explore it, it does so by investigating its many variations, often in a random fashion (i.e., choosing c randomly), but does not abandon the core idea (the iterated function, equivalent of a law of Nature). On the other hand, if a mind randomly discovers a few of the dispersed similar looking sets, it begins a search for the mother set, M, itself. Is it then surprising that researchers often tackle new problems through random exploration based on a hunch (the iterated function), and if they are persistent enough, a solution finally emerges if the hunch is right? We see a game of conjectures and refutations at play here. On the other hand, the logistic map appears to work on a species scale where random interactions among minds lead to forming of societies (say, along the lines of the Erdős & Rényi theorem) functioning under constrained resources and an adversarial predator-prey law where the bifurcation points stand for points of speciation (measured in geological time scales).
\nThe pace at which a system is driven through cyclic (iterative, also called self-referential) processes, that is, cycles of construction and destruction constrained by recyclable finite resources, has a profound effect on how the system evolves. A remarkably simple model as the logistic map shows an amazing variety of nonintuitive dynamics that a nonlinear system can display. It too provides a basic involuntary mechanism by which a society spontaneously reorganizes itself. In his seminal paper on the logistic map, Robert May, a theoretical ecologist and former President of the Royal Society (2000–2005) was so struck by the deep relationship between complexity and stability in natural communities that he exhorted:
\nNot only in research, but also in the everyday world of politics and economics, we would all be better off if more people realised that simple nonlinear systems do not necessarily possess simple dynamical properties. [59]
\nWhat lessons can we draw from such simple mathematical models? For one, the logistic map indicates that the Earth’s supply chain (the environment) has been grossly disrupted. In this predator-prey game where some Homo sapiens turn into predators and the rest into preys, a massive capture of supplies by predators results in a massive population of preys, and the preys must mutate or speciate to survive or die. The logistic map decides how the selfish genes play the game while the Homo sapiens mainly decide the value of r. The Mandelbrot set tells us that while the laws of Nature need not change for the environment to change, it does contain enough complexities in the form of fractal structures whereby the environment may change enough to force speciation to take place in niches. In the present innovation-driven environment, speciation will push to enhance the brain-mind system of the Homo sapiens. In the process, synthetic biology may discover life as we do not know it. The survival of the fittest is a statistical law and hence it rests on an ensemble being available. The world’s current population certainly fulfills that.
\nIn the present global environment, saturated by connectivity between humans, machines, and ideas, the largest component emerging in any socioeconomic context is populated by the deprived who cannot fend for themselves. Inter alia, this is highly visible at multiple scales of population size (global, national, provincial, urban, etc.) and context (employment, access to health care, education, skill development, etc.). A wide spectrum of power, opportunities, and assets are grabbed by a minority by simply ignoring the plight of the desperate. This alone enforces a massive decimation of the Homo sapiens’ gene pool. Among the predators, many with inherited wealth (and hence generally lacking survival skills but not the means) too will become preys. In this planetary-scale debacle, a unique minority endowed with an exceptional brain-mind system, perhaps aided by AI and QC, will strive to improve their gene pool by artificial speciation6 using synthetic biology and insulate themselves in an artificially created environment to improve their cognitive abilities, life span, and fecundity. A look at the logistic map shows that as the new species advance even more rapidly, increasingly wild fluctuations in their fortunes will take place within their insulated, resource-constrained environment unless they reduce r by allowing the environment to replenish itself.
\nIn the absence of irreversible ecological damage, it is possible that, in the early stages, replenishment may happen by itself since Nature would have decimated a large component of the population from the less developed countries, thus presenting the survivors with a sudden increase in per capita resources. We may infer by analogy from the Mandelbrot set that once a new species survives long enough to avoid extinction (because it begins with a small population, which needs time to grow into adulthood), even if it is in some remote fringes of the set, it will likely someday reach the main (central) part of the set since the set is connected. Once this happens, the new species will likely continue for a very long time until it is decimated by the Sun entering its dying phase by turning into a giant red star. That will be a few billion years hence.
\nThe way we acquire knowledge is iterative and nonlinear—we conjecture and put our conjectures on trial, that is, put them to severe critical tests (refutations). As the trial progresses, we edit, discard, refine, and add to our conjectures in a pseudorandom manner controlled by criticism, driven by instinct, hunches, inspiration, etc. Conjectures and refutations in scientific research are deemed self- and community-driven adversarial processes. We connect the dots. At every step of linking the dots, we consult the axioms (conjectures) and the rules for deriving conclusions (theorems) to ensure that we are within the axiomatic system we have put on trial. This means that the process leads us to understand the Universe solely based on our chosen beliefs (axiomatic system).
\nAs we learn from our mistakes our knowledge grows, even though we may never know—that is, know for certain. Since our knowledge can grow, there can be no reason here for despair of reason. And since we can never know for certain, there can be no authority here for any claim to authority, for conceit over our knowledge, or for smugness. [1, Preface]
\nAs far as we can tell, creating an axiomatic system is a nonmathematical and a highly intelligent act. Developing a sequence of theorems with a specific nontrivial goal in mind (developing algorithms) is also a highly intelligent act. However, executing an algorithm, once developed, can be mechanized and does not require intelligence, in fact, none at all. If the most useful aspect of intelligence is algorithmic, then it must be mechanizable and converted into computation. We believe the DNA is a book of knowledge about the birth and death of life. In principle, it is in machine-readable form. AI and quantum computing are the most powerful tools we presently have to decipher it. When AI drives our lives, it is the algorithm that really drives us.
\nSome recent bold experiments using CRISPR gene editing have provided glimpses of DNA editing as a new source of creating a variety of biomatter and life forms. For example, experiments are in progress for producing meat (beef, pork, poultry, and sea food) without killing animals by growing meat in the laboratory from cultured stem cells by multiplying them dramatically and allowing them to differentiate into primitive fibers that then bulk up to form muscle tissue. This would substantially reduce environmental costs of meat production and eliminate much of the cruel and unethical treatment of animals [62]. Another example is producing offsprings from same-sex mice parents, again using stem cells and CRISPR gene editing technology [63].
\nIn another development, till recently it was believed that mitochondrion DNA (mtDNA) in nearly all mammals (including humans) is inherited exclusively from the mother. However, recently, Luo et al. [64] have uncovered multiple instances of biparental inheritance of mtDNA “spanning three unrelated multiple generation families, a result confirmed by independent sequencing across multiple unrelated laboratories with different methodologies. Surprisingly, this pattern of inheritance appears to be determined in an autosomal dominant like manner.” Given that the mitochondrion is an energy-producing organelle in the cell, this discovery will have profound implications in synthetic biology and in the design of new drugs.
\nOnce humans master the art of designing DNA for self-replicating, multicellular organisms (we already know how to design cells not found in Nature and edit DNA), they will create living species of their own design. We also anticipate that when AI machines master the art of learning from mistakes (i.e., the art of making conjectures and refuting them in a spiraling process toward better knowledge, a possibility that mathematically exists), they would have taught themselves how to handily beat humans in intelligent activities and thereby break the human monopoly on intelligence. The seeds of this were sown when the AI program called AlphaGo decisively defeated the world’s greatest Go players in 2016 [65, 66]. AlphaGo has achieved what many scientific researchers had dreamed of achieving. It means that a machine can teach itself in a tiny fraction of the time it takes humans to explore ab initio any axiomatic system. The last bastion of human supremacy over all other creatures on Earth in the form of intelligence has been cracked by AI machines. This is the world the millennials have stepped into. We have no idea how AI machines may organize themselves into networks and network with humans and vice versa. Will the future be written and created by humanoids with humans finding themselves relegated to footnotes and appendices once biotechnology and AI integrate? (See, e.g., [14].)
\nSo, what comes after Homo sapiens? Given the accelerating march of AI and computing, everything points to the dominating power of algorithms created and executed by quantum computers. It is a matter of understanding how to create novel DNA sequences and creating an environment for it to thrive. It is about writing lengthy books of life using natural and artificial nucleotides. With AI-embedded quantum computers capable of surpassing human intelligence, and the smartest among them developing Godlike abilities, the raw material they will be hunting for is massive amounts of data and mining that data for usable information for the welfare of one or more new species to whom the Homo sapiens will be ancestors.
\nThe stage appears set for some remarkable advances in synthetic biology including artificial speciation as an alternative to the natural evolution of species. Homo sapiens are now poised to change the evolutionary destiny of life forms (including their own) they choose to target and even design-to-order new life forms. The ramifications are far and wide (see, e.g., [67]). Creating species that can thrive on other planets, colonizing the Moon with single-celled life, etc. are no longer science fiction fantasies.
\nWe, the Homo sapiens,7 have been around for about 300,000 years [17, 18]. Records of our civilization date back approximately 6000 years. Since Homo sapiens are still evolving, speciation may yet produce superior creatures with new attributes that can give them superior knowledge of the Universe and its origin. After all, it is speciation that made the Homo sapiens overwhelmingly superior in intellect from the great apes and our cousins, the chimpanzees with whom we share 96% of our DNA sequence. “Darwin wasn’t just provocative in saying that we descend from the apes—he didn’t go far enough. We are apes in every way, from our long arms and tailless bodies to our habits and temperament.”8 Yet, at an intellectual level, within a span of few centuries, at the knee of the exponential curve that breathed energetic intellectual life into our neural and socioeconomic networks, we have attained such remarkable feats as formalizing and mechanizing axiomatic systems, discovering deep secrets of the Universe, partially mechanizing brain-mind activities, developing technologies that augment, supplement, and amplify our comparatively puny brain and brawn capacities. Within the past century or so, we have fathomed the power and limitations of rational thought and binary arithmetic to express it in, mechanized arithmetical calculations to unimaginable heights, and used this mechanization to develop robotics, 3D precision manufacturing, biotechnology, AI, QC, cloud computing, etc. These developments are now rapidly networking, the scale of which is such that we now see the combined effects of phase transition of graph theory in the Internet of Things (IoT) (creation and destruction of interlinked man-machine-idea components), of the logistic map in the rapidly changing socioeconomic scenarios that have increasingly made predicting the future at all levels of aggregating individuals a game of dice. The relationship between the logistic map and the Mandelbrot set implies that the future of Homo sapiens will indeed be so complex that a new species capable of handling that level of complexity must either evolve or be artificially created.
\nThe raw physical limitations of the Homo sapiens’ brain-mind system is distressingly visible in its waning ability to earn a living. Barring exceptional Homo sapiens, our search for meaning in life is now propelled by search engines roaming the Internet and not by our brains. The World Wide Web (WWW) has changed the way we think, what we think about, and how we communicate our thoughts. The millennials’ cognitive abilities are very different from those they were born with and weaned on before the Internet invaded their lives. They are shaped not just by what they read but by how they read. Not only has their lifestyle changed but also has their thought style. All the work of the mind—deep thinking, exhaustive reading, deep analysis, introspection, etc.—is now delegated to AI machines. Humans have thus relinquished their right to control their individual lives and direct their souls (maybe deep inside they already know there is no soul!). If machines can outdo humans so easily without a soul, then perhaps the soul is holding humans back from reaching their potential. Perhaps it is time, AI machines became our role models and our mentors [14].
\nModern computers have made increasingly powerful and compute intensive mathematical algorithms accessible to even those not trained in science and mathematics for solving complex problems. Rapid advances in artificial intelligence (AI) and quantum computing show an inevitable trend that a vast array of human activities that till now required intelligent Homo sapiens to perform and earn a livelihood will soon be performed by AI-enabled computers, including the design of cellular life forms. When this happens, can human-designed speciation of life forms, its DNA coded for superintelligence, and other designed characteristics be prevented by the Homo sapiens’ instinct for survival? One day, nanotechnology will enable biocompatible, implantable, programmable quantum computers to be embedded into our organs or even introduce specialized new miniature organs, and we will be on our way to creating humanoids. We do not know how this will affect the speciation of the Homo sapiens. But before insight-driven complex experimentation aided by deep computing can happen; AI, new quantum algorithms, and embeddable quantum computers will have to evolve. Some early successes, for example, creation of artificial nucleotides, designed cells, attempts at resurrecting extinct species, etc. in molecular biology, indicate that once we master the biochemistry of very-very large molecules, for example, the DNA, RNA, proteins, by understanding their structure and their chemical-structural dynamics through quantum mechanical models, interactions between living and nonliving matter will undergo a sea change.
\nWe therefore anticipate a forced speciation of the Homo sapiens. It will drastically reduce the emergence time for a new species to a few years compared to Nature’s hundreds of millennia. Accelerated speciation by Homo sapiens via domestication, gene splicing, and gene drive mechanisms is now scientifically well understood. Synthetic biology can advance speciation far more rapidly using a combination of CRISPR technology, advanced computing technologies, and knowledge creation using AI. There is no reason why Homo sapiens themselves will not initiate their own speciation once synthetic biology advances to a level where it can safely modify the brain to temper emotion and enhance rational thinking as a means of competing against AI-embedded machines guided by quantum algorithms.
\nRapidly advancing research in the life sciences, while promising tools to meet global challenges in health, agriculture, the environment, and economic development, some of which are already on the horizon, also raises the specter of new social, ethical, legal, and security challenges. These include the development of ethical principles for human genome editing, establishment of regulatory systems for the safe conduct of field trials of gene drive-modified organisms, and many others. Additional concerns arise since the knowledge, tools, and techniques resulting from such research could easily lead to the development of bioweapons, facilitate bioterrorism, and the extinction of the Homo sapiens themselves. All these concerns are global not merely national [69]. The subject of this chapter goes beyond such concerns because here the concern is the possibility of self-initiated speciation of the Homo sapiens. The ramification of such a self-referential (iterative) process akin to that of the logistic map and the Mandelbrot set involving, in addition, phase transitions seen in graph theory is unknown. The perspective presented in this chapter is vastly different from that of Erwin Schrödinger (among the pioneers of quantum mechanics) expressed in 1944 [70]. Much water has flown under the bridge since then. A decade later, in 1953, when the structure of the DNA and its role in replicating life was discovered by Watson and Crick [43, 44], molecular biology was born. That led to genetic engineering [45] and synthetic biology [47]. As we write, CRISPR-Cas9 has been used to alter the embryonic genes of twin girls born in December 2018 in China [60, 61], which has elicited deep concern in the scientific community and an immediate response from the WHO: “Gene editing may have unintended consequences, this is uncharted water and it has to be taken seriously … WHO is putting together experts. We will work with member states to do everything we can to make sure of all issues—be it ethical, social, safety—before any manipulation is done” [71]. On the heels of this report comes the news that the world’s first baby born via womb transplant from a dead donor has been successfully achieved in Brazil [72]. With CRISPR, AI, and QC, the Homo sapiens are now on the threshold of creating new life forms and initiating even their own speciation.
\nAs an Open Access publisher, IntechOpen is dedicated to maintaining the highest ethical standards and principles in publishing. In addition, IntechOpen promotes the highest standards of integrity and ethical behavior in scientific research and peer-review. To maintain these principles IntechOpen has developed basic guidelines to facilitate the avoidance of Conflicts of Interest.
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\\n\\nEXAMPLES OF CONFLICTS OF INTEREST:
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\\n\\nAuthors are required to declare all potentially relevant non-financial, financial and material Conflicts of Interest that may have had an influence on their scientific work.
\\n\\nAcademic Editors and Reviewers are required to declare any non-financial, financial and material Conflicts of Interest that could influence their fair and balanced evaluation of manuscripts. If such conflict exists with regards to a submitted manuscript, Academic Editors and Reviewers should exclude themselves from handling it.
\\n\\nAll Authors, Academic Editors, and Reviewers are required to declare all possible financial and material Conflicts of Interest in the last five years, although it is advisable to declare less recent Conflicts of Interest as well.
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\\n\\nAuthors should declare if they are board members of an organization that could benefit financially or materially from the publication of their work.
\\n\\nAcademic Editors should declare if they were coauthors or they have worked on the research project with the Author who has submitted a manuscript.
\\n\\nAcademic Editors should declare if the Author of a submitted manuscript is affiliated with the same department, faculty, institute, or company as they are.
\\n\\nPolicy last updated: 2016-06-09
\\n"}]'},components:[{type:"htmlEditorComponent",content:"In each instance of a possible Conflict of Interest, IntechOpen aims to disclose the situation in as transparent a way as possible in order to allow readers to judge whether a particular potential Conflict of Interest has influenced the Work of any individual Author, Editor, or Reviewer. IntechOpen takes all possible Conflicts of Interest into account during the review process and ensures maximum transparency in implementing its policies.
\n\nA Conflict of Interest is a situation in which a person's professional judgment may be influenced by a range of factors, including financial gain, material interest, or some other personal or professional interest. For IntechOpen as a publisher, it is essential that all possible Conflicts of Interest are avoided. Each contributor, whether an Author, Editor, or Reviewer, who suspects they may have a Conflict of Interest, is obliged to declare that concern in order to make the publisher and the readership aware of any potential influence on the work being undertaken.
\n\nA Conflict of Interest can be identified at different phases of the publishing process.
\n\nIntechOpen requires:
\n\nCONFLICT OF INTEREST - AUTHOR
\n\nAll Authors are obliged to declare every existing or potential Conflict of Interest, including financial or personal factors, as well as any relationship which could influence their scientific work. Authors must declare Conflicts of Interest at the time of manuscript submission, although they may exceptionally do so at any point during manuscript review. For jointly prepared manuscripts, the corresponding Author is obliged to declare potential Conflicts of Interest of any other Authors who have contributed to the manuscript.
\n\nCONFLICT OF INTEREST – ACADEMIC EDITOR
\n\nEditors can also have Conflicts of Interest. Editors are expected to maintain the highest standards of conduct, which are outlined in our Best Practice Guidelines (templates for Best Practice Guidelines). Among other obligations, it is essential that Editors make transparent declarations of any possible Conflicts of Interest that they might have.
\n\nAvoidance Measures for Academic Editors of Conflicts of Interest:
\n\nFor manuscripts submitted by the Academic Editor (or a scientific advisor), an appropriate person will be appointed to handle and evaluate the manuscript. The appointed handling Editor's identity will not be disclosed to the Author in order to maintain impartiality and anonymity of the review.
\n\nIf a manuscript is submitted by an Author who is a member of an Academic Editor's family or is personally or professionally related to the Academic Editor in any way, either as a friend, colleague, student or mentor, the work will be handled by a different Academic Editor who is not in any way connected to the Author.
\n\nCONFLICT OF INTEREST - REVIEWER
\n\nAll Reviewers are required to declare possible Conflicts of Interest at the beginning of the evaluation process. If a Reviewer feels he or she might have any material, financial or any other conflict of interest with regards to the manuscript being reviewed, he or she is required to declare such concern and, if necessary, request exclusion from any further involvement in the evaluation process. A Reviewer's potential Conflicts of Interest are declared in the review report and presented to the Academic Editor, who then assesses whether or not the declared potential or actual Conflicts of Interest had, or could be perceived to have had, any significant impact on the review itself.
\n\nEXAMPLES OF CONFLICTS OF INTEREST:
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\n\nNON-FINANCIAL
\n\nAuthors are required to declare all potentially relevant non-financial, financial and material Conflicts of Interest that may have had an influence on their scientific work.
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\n\nAll Authors, Academic Editors, and Reviewers are required to declare all possible financial and material Conflicts of Interest in the last five years, although it is advisable to declare less recent Conflicts of Interest as well.
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\n\nAuthors should declare if they are board members of an organization that could benefit financially or materially from the publication of their work.
\n\nAcademic Editors should declare if they were coauthors or they have worked on the research project with the Author who has submitted a manuscript.
\n\nAcademic Editors should declare if the Author of a submitted manuscript is affiliated with the same department, faculty, institute, or company as they are.
\n\nPolicy last updated: 2016-06-09
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He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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