\r\n\tthe dental (malocclusions) and facial deformities (midfacial deformities), dental anomalies (number and shape anomalies), orthodontic treatment applications before lip and palate surgery for early normalization of infant cleft defect (latham appliance, nazoalveolar molding, Hotz appliance), primary alveolar grafting, gingivoperiosteoplasty, secondary alveolar grafting (timing, goals, evaluation of success) primary lip and palate surgical approach, orthodontic treatment (during mixed dentition, adolescence period), distraction osteogenesis, orthognathic surgery, language and speech disorders and treatment, velofarengeal insufficiency and treatment and secondary aesthetic surgical procedures.
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She worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. She work as Cranoiofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Univeristy of Gazi",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:{id:"291188",title:"Dr.",name:"Elcin",middleName:null,surname:"Esenlik",slug:"elcin-esenlik",fullName:"Elcin Esenlik",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Dr. Elçin Esenlik graduated in 1997 from Faculty of Dentistry, University of Ankara and did postgraduate program in the same university. She worked at Süleyman Demirel University in Isparta Turkey. 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Chan and Manoj Kumar Tiwari",coverURL:"https://cdn.intechopen.com/books/images_new/3794.jpg",editedByType:"Edited by",editors:[{id:"252210",title:"Dr.",name:"Felix",surname:"Chan",slug:"felix-chan",fullName:"Felix Chan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"65399",title:"Introductory Chapter: Reconciling Neurobioethics through Nature’s Lens - Metaphysical Determinants of Subjectivity",doi:"10.5772/intechopen.83502",slug:"introductory-chapter-reconciling-neurobioethics-through-nature-s-lens-metaphysical-determinants-of-s",body:'No more than 5 decades ago, bioethics formally took the stage as an independent discipline. Intended to normatively frame the rapidly growing knowledge of biological function, the appearance of the discipline constituted a normative response to what was viewed as a morally agnostic and scientifically aseptic attitude to the investigative and utilitarian ends of biological research [1]. Inspired by a broader recognition of a science isolated from its “ought to do” dimension, highlighted in contemporaneous debates on nuclear power by such literary elites as Huxley [2], the biological emphasis sought to address a spectrum of concerns, from ecological destruction to biological weaponry and genetic engineering, among others. In coining the term bioethics, accordingly, Van Rensselaer Potter emphasized its scope as the “science of survival” that required the bridging of two cultures, one scientific and the other humanistic and moral. Given the historical context of the Cold War era, Van Rensselaer’s proposal resonated broadly in its public recognition, launching bioethics as a widely motivated and widely disciplined undertaking [3].
Nonetheless, and despite the persistent public engagement with issues of ecological misuse and military use of biological capabilities, bioethics has since and relatively quickly come to be viewed as a normative extension of clinical practice. In the evolution introduced by the Dutch obstetrician Andre Hellegers, the object of bioethics was conceived as forging an ethical structure that would give intellectual scope to the ethical dimension already implicit in medical practice. In Hellegers’ scheme [4], the science of bioethics was to discover and compile values in a dialog that encircled medicine, philosophy, and ethics, which would take into account the ongoing stream of information emerging from biological discovery and medical technology.
As an intellectual stepchild of ethical practices surrounding the health care of the human being, bioethics is heir to a normative tradition extending to antiquity. Premised on the recognition of the unique value within each individual, medical care has traditionally been guided by principles meant to ensure the preservation of this recognition even in circumstances of significant health risk. While this historical legacy has clearly influenced the modern understanding of bioethics, its recent emergence as an independent discipline underscores its distinction from the former and raises questions regarding underlying factors that have driven the need for its emergence. Beyond issues of the rapidity and magnitude of information acquired at ever-grander scales—which must be adequately assimilated before any therapeutic intervention—emerging core ethical concerns have been especially influenced by their contingency to philosophical conceptions that have become increasingly pluralistic. Among these are included an increasing power over the regulation of organismal performance; an evolving conception of intervention as a therapeutic undertaking; supraphysical notions of organismal, organizational reality; and ontological and anthropological conceptions of the physical basis of human nature. A recurring theme raised by ever-greater knowledge and technical prowess over organismal regulation, for example, is the manipulation by and interaction with technological devices that bear not only on limiting their use but also on the very nature of technology and its relation to the human being. New interventional notions thus not only need to include the traditional principles of malfeasance, beneficence, autonomy, and justice but must also incorporate what is meant by health and disease, normality and deviance. Crucially, across a wide swath of the physical sciences, fundamental questions on the nature of physical reality are assuming greater prominence as the recognition of the insufficiency of compositional approaches, which have dominated scientific exploration since Descartes and Newton, impels the consideration of a more synthetic understanding of material reality, like that of entities and their relation to properties. Finally, and critically for the ethics of applied neuroscience, are ontological and anthropological concerns related to human subjectivity and its relation to the objective reality of his corporal presence in the world, concerns that have become especially acute since Heideggerian revisions of metaphysical understanding [5].
Accordingly, bioethics and medical ethics remain the scholarly objects of a philosophical tension introduced and exacerbated by the restless expansion of biological knowledge. This tension originates in the need to appropriate a philosophical conception of physical reality that can then be normatively evaluated. However, with the assimilation of pluralistic notions on the physical reality of the body—which is the direct object of medical or biological intervention—normative principles and ethical praxis themselves remain varied. That is, while ethical praxis is contingent on some action taken toward the corpus, such praxis acquires normative significance only within a conceptual framework of the contingent material reality.
The effect on ethical praxis of assimilating the current conceptual ambiguity is most acute in issues concerned with neural intervention. Indeed, ontological and anthropological tensions uniquely characterize neuro(bio)ethics as a normative discipline, which must confront concerns over the impact of intervention on global and organismal regulations, conceptually addressed in philosophy of science accounts. The uncertainty between how the individual is understood and the physical features inherent in the neural activity of the brain that enable the expression of these human features has marked the field, particularly in its development of metaethical principles that correspondingly evolve neuroethical praxis [6, 7]. This text, especially, proposes just such an illustration of the current ambiguity. Accordingly, the following discussion will seek to address this ambiguity by grounding the philosophical accounts in fundamental features of natural reality, articulated through the metaphysical understanding of the ontological subject. This grounding then engages a dialectic with the issues of praxis presented in subsequent chapters.
The absence of a reconciliation between philosophical conceptions of physical reality and the neural activity of the brain suggest that interventional praxis may best be related to medical notions of normality and disease that entail empirically accessible parameters. Neurological impairment, especially, is a significant domain of research, with an expanding fund of knowledge on the etiology of various cognitive diseases. Accordingly, in the absence of philosophical reconciliation, viewing neural intervention by the yardstick of disease replication offers a pragmatic means of arriving at normative conclusions.
Implicit in the appropriation of a disease model, nonetheless, is a conceptual interpretation of the disease state, with its understanding of normality and deviation. According to this conception, attributions about disease etiology frequently view disease as malfunction [8] and well being as a commonly observed biological order. Such functionalist notions derive their sense from what is understood of the role of a component system in “normal” operation, where disease etiologies reflect the component system’s incapacity to function according to biologically ascertained standards. Normality and variance are therefore set in the context of the malfunctioning of a component’s operation, for example, a cardiovascular or retinal lesion where affected anatomical and physiological zones are clearly demarcated.
According to this epistemological approach, a disease is conceived as having a causal origin affecting a specific anatomical or functional domain and generating one or more symptomatic features indicative of the disease state. Hepatitis, for example, is “caused” by the hepatitis virus, which localizes to the liver, where it displays symptoms of fibrosis. Disease states are understood to bridge two domains, one involving empirical judgments about human physiology and another concerned with normative judgments about human well-being [9]. Stated otherwise, notions of normality are articulated through the window of empirical assessment, which is used to delimit functional adequacy. Such judgments thus evoke definitions of normality and deviation that are locally applied to the affected zone. Normative judgments, on the other hand, must be elicited on the empirical judgments to ascertain whether these constitute circumstances that are undesirable or that diminish the capacity for flourishing. Normative conclusions, accordingly, constitute value judgments that are meted out with respect to an objectively accepted value standard for an empirically circumscribed zone. In adopting an analogous approach to the neurobioethics of interventions, there is thus appropriated an empirical methodology used to delimit the range of processes for which normative conclusions may be drawn.
In a Boorsian [8] conception of disease as malfunction, notably, disease features are highly territorialized in their causal structure and zone of influence. Value judgments that are contingent to such narrowly defined empirical assessments, therefore, are restricted to normative judgments on physiological normality, that is, they are primarily conditioned by the normative valuation given to attributions of functional adequacy. In bodily domains outside the nervous system, such as the liver, and even in some brain-based regions such as stroke-related lesions, this value attribution is essentially valid. However, its invocation for many other cognitive diseases, perhaps most, must confront an intrinsic, global regulatory role of the nervous system that is required to regulate organismal properties that define the individual’s ontological features.
Accordingly, normative judgments that are narrowly defined by a functionalist interpretation of the disease state, and the ethical praxis that devolves from this understanding, are insufficient for evolving metaethical principles suited to cognitive intervention. Functionalist approaches to cognitive diseases thus lend themselves with difficulty to the elaboration of a comprehensive, neurobioethical praxis, due to the broader organismal role with which the nervous system is associated.
This broader role pertains, minimally, to capacities for unifying organismal operation and goal orientation, that is, integral and teleological features intrinsic to the ontological status of the organisms as a whole. Indeed the widespread recognition of the unique and irreplaceable role of the nervous system in mediating organismal unity has constituted an empirical pillar for philosophical conceptions of bodily integration that underwrite clinical ethics in death determinations [10]. As a fundamental capacity for goal seeking, integration is crucial to human flourishing. Hence, impairing these mechanisms can be expected to diminish this capacity and so evoke normative concern. Cognitive diseases, as mentioned, are especially prone to impairments of these mechanisms, and interventions reproducing effects of the cognitive disease states, either whole or in part, are likely to deleteriously influence them. Accordingly, they are likely to be physical conditions that would be ethically probative.
This is manifestly evident in the limiting case of bodily death, life being predicated on the body’s organismal integration. With death, mechanisms of integration are no longer operative, and organismal unity is thereby destroyed. As a conceptual position universally recognized across religious, cultural, and secular scholarship [11], the loss of all organismal unity constitutes a probative, ethical imperative of ultimate and universal significance. This is also to say that while the events of death and the organismal mechanisms that work to unify the organism are physically instantiated, it is in view of the conceptual validity of organismal unity that the normative imperative is validated. By extension, factors that diminish but do not wholly void bodily integration also lessen individual well-being. A reduced capacity for intentional self-action, that is, a hallmark of several widely prevalent cognitive diseases, for example, diminishes autonomy and the satisfaction of individual need. Disturbances of self, for instance, traditionally mark the diagnostic evaluation of the schizophrenia patient [12], seen in an abnormal sense of ownership of the body, loss of ego boundary, and confused sense of self-agency. Such reduced phenomenological capacities have been shown to have their counterpart in physical features of cognition. Imaging modalities reveal, for example, a consistently high correspondence between fMRI modules and those of diffusion imaging in normal individuals, whereas those from schizophrenia patients exhibit both decreases in overall modularity and in correspondence of networks [13]. These diseases illustrate that not only the absence but also the partial impairment of physical processes for organismal integration significantly impact individual flourishing.
Taken together, meta-principles premised on disease and notions of malfunction have a practical but restricted role for evolving neuroethical praxis in the absence of philosophical judgments on global, physical attributes of the individual, of which the integrative and unitive dimension is paramount.
Normative conclusions that relate to a global organizational order, on the other hand, resemble ethical approaches that generalize to the individual as a whole, that is, not as an epistemological abstraction only but as a metaphysical conclusion on the natural reality of the individual, who is epistemically evident. These approaches thus distinguish themselves from those that define the human being functionally and that emphasize properties to the exclusion of their source, like that premised on the “stream of consciousness” [7] or that of delocalized essence, like extended mind theory [6]. They are thus also distinguished from an ethical pragmatism that is contingent to notions of disease as malfunction.
Such holistic routes to metaethics typically value the individual as a normative locus that is operative in the world. By virtue of an intrinsic metaphysical unity, they then extend value contingency to the whole of the individual. Neo-Thomistic developments in the twentieth century, like that of Etienne Gilson [14], for example, draw normative value from metaphysical conclusions, prioritizing the notion of presence as action in philosophies of being. Karol Wojtyla’s metaphysical approach to ethics [15], for instance, anchors the personalist dimension of intentionalized action in the unitary reality of the whole individual. As a metaethical principle, this dimension appeals to a dual normative contingency present within the individual. The personalist subject is considered, first, as an agent of ethical activity and, second, as an end for the pursuit of the good, that is, as a value contingent locus. Here the appeal is chiefly theoretical and conditioned by the analysis, since bridging these contingencies is the experience of morality in action. Consequently, as a metaethical “object” for ethical praxis, the individual capacity for moral behavior validates the acquisition of a wholly unique, value-laden referential status for the person. Kant, significantly, adopts a strikingly similar perspective, identifying the individual exclusively as an end and not as a means.
In Wojtyla’s theoretical exploration, the specific focus entails the phenomenal experience of subjectivity, that is, a cognitive and conscious dimension unique to each individual. His ethical analysis, accordingly, experientially and superficially, resembles ethical approaches that are phenomenologically and functionally driven. Unlike these approaches, however, Wojtyla explicitly views these as epistemological features only and so merely outward indicators of an inner and integral unity that he terms the “human suppositum,” that is, a metaphysical essence that is subjectively constituted and phenomenologically manifested.
For ethical praxis this is significant for linking all dimensions of the individual to an integral reality that is phenomenologically expressed. In fact, the absence of such a unifying dynamic leaves ethical praxis inchoate, without either a contingent locus for value or a medium for its execution. Accordingly, the identification of the subject as a metaethical principle thereby extends value to the cognitive dynamics and physical organization of the neural architecture also. Indeed, it is on the basis of the integral unity of the individual that he later cautions in Veritatis Splendor [16] “against a manipulation of corporeity that would alter its human meaning.” For neuroethics, the utility of this metaphysical conclusion thus relates directly to the contribution of the nervous system to the unity of the person, that is, as a corporal manifestation that is enabling to a human ontological, subjective, and integrative order. In other words, by invoking the unity of the uniquely human subject, the metaphysical subject identifies in the neural operation a normative terrain.
The reality of the metaphysical subject is evident through the objective manifestation of the phenomenal subject; that is, it is a reality apparent through epistemological inference. Importantly, the absence of direct empirical confirmation does not imply the absence of the subject’s reality, which can be seen in the variety of human functions that are nonetheless united in each individual. The subject’s epistemic appearance thus reveals the role of the metaphysical subject to be the physical realization of the integral and uniquely human subject.
This role is apparent first in a unified organizational order that is operationally confined, which is to say that the metaphysical subject is seen through the reality of organismal integration. Its dynamic unity, for instance, is a fundamental feature shaped by evolutionary forces [17]. As one entity in an adaptive space, the organism constitutes a “unit of interaction” [18] where the whole organism is molded by evolutionary pressures to acquire a suite of behavioral features maximizing its fitness.
The subject’s neural “manifestation,” accordingly, is not autonomously determined but is shaped by an extrinsic metaphysical order that is determinative for its expression. Indeed, it is generally recognized that material reality is subject to immaterial priors, for example, organizational principles. Recognition of these externally imposed orders can be seen in the need to invoke non-causal explananda in natural design, like the accounts used to explain the design of flagellar motors [19]. These immaterial determinants are even more apparent in the case of neural operation, where dynamic brain activity is necessarily linked to a system-wide network that subsumes regional activity to global performance [20].
Because such metaphysical determinants are only epistemically evident, however, empirically elucidating the physical mechanisms of integration becomes key to a principled neuroethical praxis. In consequence, praxis remains subject to both empirical and philosophy of science accounts for its evolution. For integration, the reconciliation of these accounts has been the subject of much debate. Although the reality of integration is evident in the natural world, its conceptual articulation through philosophy of science accounts has restricted the choice of hypothetical presuppositions used to define empirical resources. This has exposed current accounts of integration to factual inconsistencies and delayed the evolution of more realistic and comprehensive frameworks.
The somatic integrity thesis, for example, which has served as the conceptual platform for clinical determinations of death, invokes a causal, brain-directed model of integration, through which the functioning of the body’s varied physiological systems is coordinated [21]. According to this understanding of integration, ethical practice is contingent on the empirical demonstration of an irreversible loss of the capacity to maintain cohesive and coordinative function, the causal origin of which is identified with the brain. Loss of brain function is therefore equated with loss of the capacity. This conception now constitutes the philosophical linchpin for what has become a global clinical praxis. Probative actions, in consequence, such as the removal of vital organs, are defined in reference to the loss of a single organ, the brain.
Its validity, however, is challenged by a number of empirical observations following a diagnosis of brain failure, including continued heart and whole body circulation [22], wound healing, temperature regulation, and even pregnancies [23]. These apparent contraindications, claims of technological artifacts notwithstanding, thus raise the issue of the nature of the brain’s relation to bodily processes and so how this relation impacts the physical conception of the death event.
The challenge to the somatic integrity thesis, in fact, retrieves a systemic notion of integration, where the source of integration is understood to be delocalized and distributed within and throughout the body rather than being confined to a single anatomical region. Such a conception of unity substantially differs from the strict causal notion of imposed control used to achieve an aggregate coordinative order. By siting its origin to a single organ within the individual, the latter notion has the conceptual and diagnostic effect of segregating the brain from the body’s remainder, physically, hierarchically, and functionally. Normatively, this division has created a chasm between the brain and body where the brain has acquired a valued status and the body’s remainder has been relegated to a dependency on the brain’s vital operation. The empirical contraindications thus evidence a form of integration that more closely resembles an integral unity shared equally by all material components and processes of the body,that is, a form of integration more closely corresponding with the metaphysical notion of unity invoked by Wojtyla. This altered conception has the important normative consequence of valuing the individual as a whole. Importantly, it reveals how the understanding of normative value is itself influenced by the epistemic order of the material body.
Considerable neuroscientific evidence favoring a systemic model of bodily integration has in fact now been gathered, particularly with regard to motor dynamics. Existing studies reveal, for instance, that peripheral and central nervous system activities mutually and reciprocally contribute to integration at multiple levels. These largely plastic influences have been shown to be progressively and hierarchically scaled within the nervous system to (1) shape inward and outward flow between the brain and body, (2) generate stable representations of bodily interaction with the world, and (3) yield a dynamic, bodily integrated performance unit.
These studies underscore the unity of the body by showing that bodily sensory input molds connectivity patterns in the brain to shape the brain’s responses to afferent input, that is, the body is responsible for configuring the brain’s reaction to sensory information; the functional outcome of this molding is to modulate the brain’s “perception” of the world as a function of the body [24], that is, to unify bodily responses to external events with respect to the whole individual.
The generation of the bodily percept appears to unify the body for performance [25]; that is, the percept is generated to unify action as originating from a single source. Accordingly, the dynamic nature of this process precludes the functional segregation of the events of the body from those of the brain. The need to achieve unity in performance, accordingly, implies that the perception of the world through the body requires the integration that is effected by the dynamical and reciprocal relations between the body and brain, that is, a delocalized source of unity, which relates the body to the world and which is fundamental to its interaction with it.
Nonetheless, the delocalization that distinctively characterizes systemic forms leaves unexplained the presence of goal-directed behavior that is essential to autonomous living and the relation of such behavior to the mediation of systemic unity. Notions of integration premised on a systemic model, notably, fail to account for higher order (i.e., organismal and not merely cellular and organic, organizational, and behavioral) properties constitutive of multicellular organisms generally and of humans with highly evolved nervous systems, in particular. This is also to say that while systemic models are consonant with the holistic character of living organisms [19], they do not account for autonomous behavior [26] and so are unable to account for a material realization of ontology. Such an explanation is crucial for neurobioethics in order to identify an empirically salient source of material processes undergirding ontology and structuring a systemic model of integration. The account for the “emergence” of ontology in fact is likely to conciliate with intrinsic metaphysical features of natural reality, like the relational and communicative features described by Etienne Gilson [15], that is, these intrinsic features are fundamental determinants for the ontological form that is generated. In particular, they yield the most advanced expression of physical reality, the subjective entity, which, accordingly, is constituted as a metaphysical reality, as noted by Wojtyla. Indeed, neuroscientific evidence on the phenomenal subject is consonant with a role for their metaphysical evocation.
Critically, empirical studies indicate that higher-order properties emerge from the corpus as a whole and that these properties implement organismal integration, here understood as an outcome of intentional, goal-oriented behavior. Accordingly, the integral unity of the individual is directly attributed to the autonomy of the intentional subject. Drawing from Mossio and Moreno’s theoretical account of organismal autonomy, notably, human ontological faculties share a profound intimacy with the body [27] both mediating bodily integration [28, 29] and sustaining life. As predicable properties of the whole, that is, emerging for the “good of the whole organism” [17], such properties are intimately linked to processes both influencing and influenced by its extended organizational form, and so are manifest in the mutually constraining influences of the peripheral and central nervous system. In other words, higher-order properties emerge from the body as a whole where they unify the body through intended global actions, including self-identity, agency, and consciousness, and so mediate a delocalized, systemic mode of integration.
The need for the emergence of these properties from the body can be seen in the case of self-identity and understood as an ability to differentiate the physical breadth that is subsumed by processes belonging to itself from those of the contiguous environment. An organism like Caenorhabditis elegans, for example, must identify this range through the dynamical operation of its neural architecture [30], which regulates individual motor movements in reference to this global activity. In humans this perception of self has also been shown to be a process arising from afferent, somatotopic input of the whole body [27]; indeed, in the body’s absence, there would be no percept.
Similarly, the ability to initiate actions by oneself requires that these be stably linked to the self-percept [25] now known to entail a neural dynamic termed the motor image [31]. As currently understood the motor image constitutes a covert action undertaken only mentally and as a simulation of a non-executed action. That is, the motor image contains the feature elements of a motor trajectory and so contains the projected series of motions that are prospective for execution. Insights drawn from the motor image reveal that bodily representation is a key feature that frames the elements of the plan as teleologically oriented, that is, one that inscribes actions linking an agent with an objective destination. So inscribed, actions are thereby executed as a coherent and coordinated dynamical ensemble, which have a causal origin linked to the whole individual. Accordingly, features of the motor plan entail mutual though distinct contributions from peripheral as well as central origins, underscoring the essential unity of dynamic performance even in its covert formulation, and directing it toward a unique goal.
Consciousness, likewise situates as a global property enable both responsible action and the execution of higher faculties. Current insights suggest a decentralized physical origin [32], where the body contributes to the emergence of consciousness in at least two ways, by (1) creating a generalized platform that sustains a phenomenological background of mental awareness and (2) stimulating its focal emergence. Together these results argue for a complex but nonetheless shared participation of brain and body in eliciting and sustaining all higher order properties, that is, a unified and delocalized source of bodily emergence.
As noted, for Wojtyła, it is the dimension of metaphysics that situates the ethics of the personalist subject, where the person “constitutes a privileged locus for the encounter with being, and hence with metaphysical inquiry.” In the Wojtyłan formulation, the normative value of the personalist subject thus emerges from its metaphysical and immaterial mooring, constituting the ground for its physical instantiation and the essential metaethical dimension for neuroethical praxis. Accordingly, it grounds his claim against “dehumanizing” corporal intervention. In doing so it has a direct bearing on the construction of ethical standards that are probative, that is, the construction of normative statements that pertain to actionable standards that would or would not infringe on a specifically human meaning.
By contrast, prevailing models of the subject that are a legacy of Cartesian metaphysics, challenge, the specifically human meaning of the personalist subject that flows from his ontological primacy in the order of being. It is a challenge, moreover, also directed to the understanding of material reality. As Gillett has pointed out [33], what is evident in current debates over the nature of material reality is the extent to which the Cartesian segregation of immaterial and material dimensions and the invocation of a strictly causal model of relations suffice for ontic adequacy, that is, whether materialism alone or dualism offer adequate explananda to account for the material order. The debate on physical reality has significant repercussions in the ethical sphere, with normative consequences that impact neuroethical praxis and leads, increasingly, to dehumanizing tendencies.
How metaphysics grounds ethical praxis, accordingly, is a critical dimension often ignored in debates about human nature and its modification that are exacerbated by the advent of neuro and genetic technologies. The culmination of a multistaged metaphysical divorce has transpired since Descartes; however, its current understanding has left efficient causal and mechanistic commitments to drive the prevailing materialism of modern neuroscience, leaving a decompositional and reductive philosophy to determine how brain operation is interpreted for the foreseeable future. Presuppositions invoked by these efforts belie the consilience with neuroscience that is more evident in Wojtyła’s proposal. Crucially, the need to account for the emergence of subjectivity from the material order, that is, the hallmark of the neural architecture, is left unexplained by the Cartesian metaphysical segregation. The ferment in current efforts to explain the reality of the brain and mind, however, indicates that modern metaphysical presuppositions that undergird neuroethics are in a process of flux. The current uncertainty surrounding the metaphysical status of subjectivity, therefore, suggests that the Wojtyłan metaphysical subject may open a new window on the objective reality of the subjective mind that will offer surer philosophical ground for neurobioethics.
Bacteria are the dominant form of life spread across the whole planet. Their biochemistry machinery is well adapted to scarcity conditions; also, they can biosynthesize complex molecules in various environmental conditions. For this reason, the growth and proliferation of bacteria in controlled environments represent an interest of biochemical engineers, microbiologists, and cell-growth enthusiasts since they allow bioprocess simulation and control scheme design. Substrate transformations into cell biomass, organic molecules, therapeutic proteins, biofuels, enzymes, and food additives are of attention since application to actual fields and laboratory experiments are very difficult to scale-up to industrial level with strict and complete control of key variables determined as an ideal process [1]. It is known that the complexity in a mathematical model may increase with the inclusion of environmental conditions such as multisubstrate consumption and product formation, pH change during fermentation, variable temperature, rheological changes in culture media, multiphasic environmental variability, and nonideality of mixing and stirring [2]. The kinetic model had been evolved from simple exponential growth to complex mathematical expressions to predict heterogeneity in single cells, describe multiple reactions, explain internal control mechanisms, and even predict genetic variability between bacterial populations [3]. However, despite the efforts to represent the progress of biological reactions in microbial cultures, the actual application of the model in real production processes is impractical due to a significant amount of information fed to the model [4].
\nMany of the kinetic growth models base their structure on and take information from empirical observations through experimental data. The white box models (WBMs) use information from mass balances in a single stoichiometric equation where inputs, outputs, and the conversion from substrates to products are followed [5]. Despite effectiveness and advanced reaction representation in WBMs, the representation of the reaction advance degree, some information on metabolic flux analysis (MFA) can be obtained. Models based on detailed MFA can be used to define optimal operation conditions based on biochemical pathways. It has been established that kinetic models of biological reactions are more complicated than “common” chemical reaction models. Microbial growth models require specialized knowledge of rapid changes of environmental conditions, stoichiometric individual reactions, and the appearance of new steady states in different culture stages [6]. In many cases, mechanistic models, based on first principles, are ineffective because of metabolic complexity of microorganisms.
\nIn this sense, complex microbial consortium behavior and culture media with different types of substrates are difficult to model. Nonmechanistic models, or black box models (BBMs), or a combination between mechanistic and nonmechanistic models, or gray box models (GBMs), are more suitable to describe them. Kinetic parameter fitting for WBMs requires experimental measurements of multiple variables, and frequently, model validation may be impractical. BBMs and GBMs constitute alternatives which describe the general dynamic behavior of bioreactors, without requiring many experimental measurements of the system. These models do not offer mechanistic information about metabolic phenomenology present in the system, but they can optimize and control without it. Then, models can be classified based on the mathematical formulation of the system (Figure 1). These are classified into mechanistic, empirical, and fermentation models. A mechanistic model is based on deterministic principles. On the other hand, empirical models represent input-output relations without the knowledge of a mechanism. Fermentation process models are usually represented with a combination of both, mechanistic and empirical models.
\nClassification of models as mechanistic and nonmechanistic.
An important characteristic of modeling is the assumption of homogeneous or heterogeneous conditions. In this sense, a homogeneous system is related to a single continuous phase. In most cases, bioreactors are described as single liquid phases. However, if the biofilm is included in the study, a solid or semisolid phase needs to be considered in the model. On the other hand, heterogeneous systems are related to the description of two or more continuous phases and the interactions between them. Complex heterogeneous systems can be described as multiple phases: liquid, solid or semisolid, and gaseous phases (e.g. solid-state fermentation). Within this classification, parameters in a model can be classified as distributed and nondistributed (lumped). Distributed parameter models assume that operation parameters vary as a function of space. One, two, or three dimensions are considered in the description of key variables as a function of parameter distribution. As a result, the system is described by a set of partial differential equations (PDEs). On the other hand, a lumped model is necessary, and the system can be described by a set of ordinary differential equations (ODEs), since these parameters do not vary as a function of space.
\nIn this chapter, we provide an overview of mechanistic and empirical models for cell population in fermentation processes.
\nUnstructured kinetic models (UKMs) represent, in a simple global point of view, the metabolic behavior of the biomass cell production. Mainly, mathematical descriptions for microbial growth kinetics in fermentation processes are based on semiempirical observations. From simple experimental data, we can obtain information to represent cellular growth with unstructured kinetic models.
\nTo get the most efficient description of a kinetic model, it is essential to be clear about the application purpose. The application determines the complexity level and structure of the model. The correlation among cell growth, substrate consumption and inhibition [7], or description of the substrate profiles within the reactor during expression of extracellular proteins is the central goal of the model process [8]. The description of key variables is the contribution of the model [9]. These representations are expressed as equations in a simple mathematical model. The UKMs, which are unstructured, unsegregated, are based on the monitoring of cell and nutrient concentration and describe the fermentation process as an average of the species under ideal conditions. Also, it describes the cell and its components as a single species in solution. UKMs consider the apparent rate obtained by metabolic processes, which are carried out by microorganisms. These models are based on conservation equations for cell mass, nutrients, metabolites, and species generation/consumption rates. Most of the UKMs can be divided into three terms: rate expressions for cell growth, rate expressions for nutrient uptake, and rate expressions for metabolite production.
\nIn the case of exponential growth phase, which is the simplest representation of microbial growth, nutrient concentration profiles and decrease rate in several cases are not almost considered.
\nwhere r is the reaction rate, X represents biomass, μ is specific growth rate, kD is the death rate, α is the stoichiometric factor, and Yi is the yield.
\nThe simplest example of multiple reaction models includes substrate consumption for cell maintenance and true yield coefficients (g DCW/g DW) [5]. One of the most used UKMs is Monod’s model [10]. This is one of the simplest models to deal with microbial growth, physiology, and biochemistry. The Monod equation describes the proportional relationship between the specific growth rate and low substrate concentrations (Eq. (3)).
\nwhere μMAX is the maximum specific growth rate, [S] is the substrate concentration, and KS is the saturation constant.
\nThe disadvantage of the model is that the individual entity, regulatory complex, adaptive response to environmental changes, and capacity of cell organelles to generate various products in inherent metabolism cannot be considered. The simplest mathematical models used to estimate microbial growth and substrate consumption are still used for monoclonal antibody production by Chinese hamster ovary (CHO) cells [11, 12]. UKMs can predict specific growth rate in simple systems by calculation of mass balances with independent variables.
\nThe Monod equation is not able to predict the substrate inhibition effect. Thus, several models including such effects have been developed. For example, Andrew’s kinetic equation includes an inhibition function to relate substrate concentration and specific growth rate [13].
\nwhere \n
Under the assumption of steady state in continuous operation, substrate concentration is low, and the term \n
where [P] is the product concentration.
\nUnder the assumption of low product concentration, the term \n
The Monod model assumes that the fermentation culture media has only one limiting substrate. More than one limiting substrate is present and impacts specific growth rate. Thus, the following model considering multiple substrates is proposed [14].
\nwhere subscript i is the number of each substrate species and e represents the essential substrate.
\nThe limited but accurate information provided by UKMs may help to represent global reactions effectively. In addition to substrate consumption and microbial growth, fermentations present catabolic inhibition. Therefore, several research groups propose complete UKMs, which include the empirical observation such as variables regarding cells, substrates, and products. Hans and Levenspiel [15] proposed a kinetic model that assumes the existence of inhibitor critical concentrations.
\nwhere [I] is the inhibitor species concentration.
\nThe inhibition function proposed by Levenspiel [16] takes into account the inhibition of ethanol production of alcoholic fermentation modeling, where subscript \n
where [P] is the product species concentration and n is the index of cooperativity between inhibitors.
\nThese models can also explain multiple reactions and include biochemical information of metabolites in the global net effect, making them experimentally accurate. This characteristic is useful for structured and segregated modeling [17]. These models can also describe mixed metabolism [18] and hetero-fermentations [19]. The duality of Saccharomyces cerevisiae metabolism, aerobic and anaerobic metabolism, is the best example of multiple reactions. The aerobic growth of the yeast yields biomass by favoring metabolic pathways designed for anabolism and cell division. This metabolism is oxidative in amphibolic reactions. However, at low oxygen concentrations, the yeast metabolism changes from being purely respiratory to partially fermentative. The fermentative pathway mainly leads to ethanol production as a final electron acceptor. Thus, there is a limited growth with high ethanol yields in fermentation culture media. Both metabolisms can occur during the growth of S. cerevisiae in a wide range of simple carbohydrate fermentations. At high substrate concentrations, there are limitations in respiratory pathways, which lead to an overflow to ethanol production with enhanced fermentative pathways. The simple WBM with overall reactions could not explain in detail the dualism of both fermentative and respiratory metabolisms. Thus, there are two stoichiometric reactions proposed to explain oxidative and fermentative metabolisms [18].
\nwhere α, β, and γ are stoichiometric coefficients.
\nThis system considers nearly ideal Monod kinetics, no by-product formation, linear specific oxygen consumption rate, and correlation with substrate uptake. If the primary carbon source is glucose (instead of ethanol), glucose can be used aerobically and anaerobically. Ethanol can be used as a carbon source only aerobically. Then, different sets of linear algebraic equations can be derived concerning carbon, oxygen, and hydrogen balance.
\nThe respiratory quotient (RQ ) is often used as an indicator of fermentative processes. When RQ is close to one, there is no fermentative metabolism, whereas if RQ is above one, the fermentative metabolism occurs.
\nThe mechanistic characteristics of an unstructured, unsegregated kinetic model contribute to the knowledge of the complex metabolism of S. cerevisiae. Despite giving relevant information of simple metabolic processes with multiple reactions, UKMs cannot give information about complete intracellular oxidative metabolism. An example of the application of these models is explained in subsequent sections.
\nThere are several classifications of mechanistic and statistical models of cell population for bioprocess applications. Two terms are essential for mathematical description of cell populations: segregated and structured models. A structured model is related to cell material description using multiple chemical components. A segregate model is related to the description of individual cells in a heterogeneous population. Additionally, it is possible to combine a structured approach with a segregated approach. Structured kinetic models are introduced in this section.
\nStructured kinetic models (SKMs) describe changes in cell population. The liquid phase (abiotic phase) usually contains nutrients for cell growth and some extracellular metabolites. The microorganisms suspended in the liquid phase behave as multicomponent systems. SKMs consider the internal structure of cells (e.g. mitochondria), and the description of cell growth and its metabolism is used to assume a more accurate growth rate. The information used is a starting point to generate schemes that represent more accurately the growth of microorganisms and their cellular components. The complexity of the information variables and parameters increases in SKMs with the mathematical representation of cellular growth.
\nSKMs are generally classified into morphologically structured models, chemically structured models, genetically structured models, and metabolically structured models [20].
\nMorphologically structured models consider the kinetics of nutrient consumption and product formation. These models consider different cell types as living species in terms of the role that they play in the overall reaction. Chemically structured models consider the effects of chemical species in fermentation kinetics; all viable cells are functionally similar, and all the fermentation rates and transport phenomena parameters are accounted for. Genetically structured models assume molecular mechanism knowledge. The model includes the rate of expression of an operator-regulated gene and kinetic equations for the transcription, translation, and folding processes. Metabolically structured models provide a better understanding of process regulation mechanisms such as feedback regulation. This model is based on the main metabolic pathways and in most cases is included in MFA. In the presence of metabolite concentration changes, the network structure represents the reaction and metabolite concentration as a matrix array. Then, SKMs can be classified as dynamic and structural [21]. Dynamic models are described as a set of ordinary differential equations (ODEs). Structural models, which are simplified from ODEs, are represented by a set of algebraic equations through two main approaches: MFA and elementary mode analysis (EMA).
\nThe structured and unstructured kinetic models in the previous sections describe, with a high degree of accuracy, the dynamic behavior of microbial growth in bioreactors. These models, associated with material and energy balances, also help to understand the phenomena associated with microbial metabolism, giving clues to the process design and control.
\nBlack box models (BBMs) usually fall into two main categories: statistical models (SMs) and artificial intelligence tools (AITs). SMs use experimental design, response surface analysis, and exploratory data analysis, whereas AITs consider tools such as data mining, artificial networks, and fuzzy logic [22]. Also, several methodologies to combine mechanistic approaches with nonmechanistic modeling strategies have been developed. The hybrid models, which are known as gray box models (GBMs), inherit the advantages of BBMs such as data analysis and can achieve semi-mechanistic description to each metabolic phenomenon. GBMs offer greater estimation accuracy, calibration ease, better extrapolation properties, and more detailed information on the phenomenology of the system [23]. The advantages of GBMs in the application of bioreactor modeling are direct control and optimization. In this section, we will describe some of these nonmechanistic modeling tools and some of their applications, such as the design of soft sensors.
\nArtificial neural networks (ANNs) are mathematical models that are devised from the need to characterize biological neural processes. As the system of ANNs imitates the way which is used to interact with each other in brain neuron, ANNs are simple and strong processes to interconnect the elements that transmit and process information through electrical impulses. In ANNs, these simple process elements are also known as neurons, and depending on the complexity of the connection schemes, they can develop the ability to describe the nonlinear behavior of many dynamic systems [24]. ANNs are computational models that aim to achieve mathematical formalizations of the brain structure and functions, which are constantly reformed by learning through experience and extracting knowledge from the same experience. In ANNs, the hierarchical structure similar to that in brain is established, where neurons connect with each other and transmit the response to other neurons. Once the ANN’s structure is defined, it is necessary to develop memory form experience (experimental data). In order to introduce this experience, the ANN training algorithm performs a weight (ω) fitting process associated with each neuron, such that the actions introduced (input signals) converge to the reactions produced (output signal) [24]. Although ANNs do not provide a physical interpretation of the phenomena that take place in the system, these models can approximate the dynamic behavior of the system, making them suitable universal approximators [22]. ANNs are defined based on three basic characteristics: their architecture, activation functions, and training algorithm. The architecture deals with the type of interconnections between their processing units or neurons, while the activation function corresponds to the dynamic characteristics of the neuron transfer functions. The training algorithm refers to the parameter fitting procedure, which provides the learning ability.
\nFeedforward networks (FNN) represent the simplest network configuration capable of describing the nonlinear behavior of bioreactors. In FFNs, neurons of each layer propagate their information to all neurons in subsequent layers. In each neuron, the input information corresponds to the weighted sum of all the outputs of the previous layer, and the weighting factors, weights and thresholds, are internally fitted for better system description [24].
\nAnother type of ANNs frequently used is recurrent neural networks (RNNs). The structure of RNNs differ from FFNs, in the sense that some of the last layer neuron output signals are fed back as inputs to any previous layer. RNNs could converge to stable system solutions and include the effects of response delays. These characteristics make these models especially useful in the modeling of continuous bioreactors [25].
\nBBMs are based on the analysis of data generated to detect correlations, and basic functionalities between the variables and the WBMs are constructed from first principles. A hybrid model category between WBMs and BBMs is GBMs; these models implement a set of tools that combine some of the characteristics of both. Some of these characteristics include properties of process and control design, without losing the ability to explain the phenomena present in the system. Defining a parallel or serial data flow structure allows the integration of both, mechanistic and nonmechanistic information (e.g., Figure 2).
\nClassification of models as (A) parallel flow nonmechanistic model and (B) serial flow nonmechanistic model.
Parallel arrays are mainly used when there is a well-defined mechanistic model of the process and are suitable to improve its estimation performance. It is especially useful in cases where dynamic aspects of the system can be decoupled. Figure 2A shows a conceptual diagram of parallel interaction where the circle represents WBMs and the square BBMs, and the circle inside the square corresponds to a hybrid model. In the case of serial arrays, BBMs describe just specific terms of the WBMs, such as growth kinetics or transport parameters. Figure 2B represents the hybrid model where the BBMs (square) are substituted into the WBMs (circle). Stosch et al. presented a detailed panorama of this model [23].
\nIn the design of bioreactors and their associated controllers, one of the difficulties is the determination of the kinetic model that adequately describes the growth rate of the respective microorganisms. The selection of a kinetic model leads to restrictive models for fixed operating conditions with little extrapolation possibility. GBMs take advantage of the well-known mechanistic information through mass and energy balances to describe specific dynamics of the system and expand its parametric scenario of applicability.
\nIn the determination of bacterial growth kinetic models, GBMs have some advantages compared to BMMs; FFNs are able to describe the system state values, but have important deviations in their estimates, due to their high sensitivity to noise. GBMs combine WBMs with neural network structures, either by feeding the outputs of the ANNs to the state space model, or by backpropagating the estimation error from output layers. Therefore, GBMs offer better forecasting properties and strengthen their performance in the presence of noise [26].
\nMonitoring culture media requires the measurement of variables such as biomass concentration, substrates, dissolved oxygen, carbon dioxide, ammonia, and temperature, among others. These values are used for growth kinetics determination and bioreactor design. However, on multiple occasions, implementation of specific sensors is complicated, and there may be limitations in sensing frequency for variables such as biomass concentration. The implementation of indirect measurement methodologies, such as signal filtering, observer design, and ANNs, allows the estimation of some of these variables, and even the estimation of complex variables such as overall microbial growth rate and the heat flux produced by the system.
\nIn control system theory, a major implementation of state observers is a common complementary strategy. These observers estimate some state variables that cannot be easily measured, either by the absence of suitable sensors, or because of low sampling frequency and high delay times. The main types of observers used for these purposes are those based on the Luenberger scheme, finite-dimensional observers, Bayesian estimators such as Kalman filters, interval observers, observers for fault detection, and even models of artificial intelligence such as ANNs and hybrid models [27].
\nState observer design requires that the estimated variables are detectable and observable. These states are observable if for a set of specific initial conditions, the internal states of the system are inferred from the knowledge of their outputs. Once its observability is determined, the observer can be designed; the desired type of observer is selected from categories mentioned above. Afterwards, tests of the estimator are carried out by comparing the real values against observer estimates, and in the case of important discrepancies between these values, the observer is adjusted or a different one is selected.
\nIn the case of stirred tank reactors (batch, continuous, or semi-continuous), we may often assume that homogeneous conditions are available, so the system models obtained consist of aggregate parameter systems (ODEs). However, frequently in the case of tubular reactors or solid substrate fermentation systems, homogeneity assumptions are not adequate, so it is necessary to construct using distributed parameter models (PDEs). In the latter case, the design and performance of the main observers, such as Luenberger or Bayesian type, are usually limited, and therefore, it is usual to resort to another type of observer. In such a case, the observers based on a discretized system are substituted for traditional observers [28].
\nSoft sensors or virtual sensors are used as state observers in specific application. These soft sensors combine several physical measurements with dynamic characteristics to calculate other variables that are not measured.
\nSoft sensors can not only provide variable information to characterize a system but also facilitate the design of the control schemes.
\nIn bioreactor design, soft sensors can be used to estimate unavailable variables such as biomass. Traditionally, biomass has been traditionally determined by use of a variety of methodologies such as optical density, dry weight, and microbial counts, among others. These techniques present several problems, the most important being the lack of continuous online measurements. To overcome this problem, various strategies have been applied, such as the implementation of low-cost sensors combined with signal processing strategies. For instance, the RGB sensor is used for biomass measurement in microalgae production reactors [29]. This type of sensors uses the intensity of the red, green, and blue (RGB) colors, which correlates with the biomass concentration using dry weight and/or colony formation unit (CFU) information, using the Beer-Lambert law principles. The correlation is described through linear fitting [30]. Additionally, it is possible to compensate background noise by use of ANNs even in the case of nonlinear correlation [31].
\nSoft sensors can also be applied to nonexplicit system states. These observers can estimate lumped system variables, such as growth rate. As the simplest factor, temperature is commonly used, since it allows estimating system concentrations, due to intrinsic dependence between reaction rates and reaction enthalpy. The heat of reaction, either consumed or dissipated by the system, is one of the implicit system states used for reaction rate determination. The same strategy may also be used to determine microbial growth rates [32].
\nMicrobial growth rates are inherently variable due to their metabolic nature and operation conditions. For example, as fluctuation in substrate concentration occurs in fed-batch bioreactors, the condition of osmotic pressure within cells is modified through the plasma membrane, which may change cellular energetics and the viability of cell division. A suitable strategy for these cases is the design of a substrate consumption rate observer. This kind of observer helps to design a robust control strategy against important fluctuations in maintaining constant substrate concentrations.
\nThe use of observers or soft sensors is an interesting alternative to elucidate approximate values of system states, whether these are explicit or implicit, in cases where online continuous physical measurement is not available. These approximations can be used to design process control schemes that ensure proper functioning.
\nThere are many practical applications of structured or unstructured kinetic models. In the acetone-butanol-ethanol (ABE) production, the models for the bioprocess have evolved from simple stoichiometric equations to sophisticated and elaborate kinetic models based on metabolic pathways [33, 34], genome-scale metabolic flux modeling [35], system-level modeling [34], and metabolic network [21]. Gordeeva classified mathematical modeling of specific growth rate (dependent or independent on substrate concentration), specific rate of substrate consumption, and specific rate of product formation in batch fermentations [36]. In this study, the states in fermentation are described by a system of three ODEs [36]. Cui reported unstructured lactate formation by enzymatic hydrolysis of sugarcane bagasse, and the model is based on Logistic equations, Luedeking-Piret equations and Luedeking-Piret-like equations [37]. Similarly, Sharma reported an unstructured model to describe growth, substrate utilization, and lactate production by Lactobacillus plantarum [38]. On the other hand, the common mathematical descriptions of the fermentation process are based on UKMs. For example, the fermentation of sweet sorghum stalk juice by immobilized Saccharomyces cerevisiae is explained by the kinetic parameters of Hinshelwood’s model [39]. Another example using the UKM model is the basic logistic model incorporated with the Luedeking-Piret model (hybrid model) to describe the production of bioethanol from banana and pineapple wastes [40].
\nCephalosporium acremonium (ATCC 36225) is one example of the utilization of SKMs where morphological differentiation and catabolite repression are the main aspects of the model approach [41]. SKMs can also effectively represent diauxic growth as well as the monitoring of an intracellular reactant in acetic acid production by Bacillus licheniformis [42]. Sansonetti reported a biochemically structured model for ethanol production from ricotta cheese whey by Kluyveromyces marxianus [43]. Wang studied a segregated kinetic model in fed-batch culture to represent simultaneous saccharification and co-fermentation (SSCF) for bioethanol production from lignocellulosic raw materials at high substrate concentrations [44]. Another interesting process is the solid-state fermentation. In most proposed models, a set of PDEs is used to describe how intraparticles are diffused or how the growth can be affected by intraparticle diffusion of oxygen, enzymes, hydrolysis products, and other nutrients and the role in the fermentation of other phenomena such as particle shrinkage and spatial microbial biomass distribution [45]. Computational fluid dynamics (CFD) provides information concerning the mixing modeling and design of bioreactors [46]. Another example of CFD is cephalosporin production by Acremonium chrysogenum; it was found that the oxygen transfer rate (OTR) directly affects fermentation performance with different impeller combinations [47]. Applications of CFD to fermentation modeling include effects of stress on cell morphology and mass transfer from the bulk solution to the organisms [46]. Biochemical models should be coupled to the CFD models in order to give a closed link between biochemistry and fluid dynamics of the system [33]. Haringa assesses the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations toward rational scale-down and design optimization [48].
\nAnother way to construct mathematical models of microbial growth is the use of FFNs, which describe the behavior of different configurations of bioreactors. An example of this type of applications is the modeling of the production of bioethanol obtained from sugar beets [49]. Here, a three-layer FFN is used to describe the dynamic behavior of the reactor. The first neuron layer consists of system inputs, which correspond to substrate concentration, substrate type, and fermentation time. The second layer corresponds to hidden neurons that process the information through their activation function. Finally, the third layer matches the output of the system that corresponds to the viable cell count of yeasts and the concentration of ethanol produced. On the other hand, GBMs and their hybrid models are not only used to characterize fermentation kinetics but can also describe general behaviors of bioprocesses. For example, in fed batch cultures of Chlorella pyrenoidosa, a hybrid scheme of ANN with mass balance mechanistic models describes the general behavior of the states of the system, reducing considerably the variability of their predictions, and achieving versatility in application [50]. These types of GBMs are useful in cases of high complexity due to metabolic dynamics of microorganisms [51]. GBMs or hybrid models are not only combinations of first principles with ANNs, but there may also be hybrid models obtained through the combination of statistical models with ANNs. This type of models usually has special applicability in the optimization of operating conditions of bioreactors (e.g., fed batch fermentation of Ralstonia eutropha for poly-β-hydroxybutyrate production) [52].
\nSoft sensors are also useful in control design. For example, sliding mode observers can describe the behavior of sulfate reduction rate which results from Desulfovibrio alaskensis fermentation [53]. These observers use turbidimetric and colorimetric titration information, and formulate based on sliding modes and sigmoidal functions, but their performance depends strongly on the nature of the system and its monitoring schemes.
\nAbrupt leaps in substrate concentration can be detected and prevented by the strategy of adaptive or optimal control by coupling with an observation scheme such as ANNs. For example, in L-glutamate production with Corynebacterium glutamicum fermentation, physical sensor applications are limited because of high costs and system complexity. However, it is possible to use simpler measurements such as oxygen concentrations, temperature, pH, and carbon dioxide production to train models of ANNs that can approximate the dynamic behavior of glucose concentration [54].
\nIn bioprocesses, representation through simple or complex models, fermentation must consider process variables and analytes in mathematical models to achieve optimization, to develop simulations, and to calculate the output of critical variables in bioprocesses. Kinetic models allow predicting the behavior of biochemical reactions. This useful information is critical to techno-economic analysis. The incorporation of simple or complex models could represent phenomena more precisely and thus enhance our comprehension. In the design of bioreactors, a mathematical model is necessary to allow selecting the optimal operating conditions. There is a wide variety of types of models ranging from simple statistical descriptions to artificial intelligence tools. Appropriate model selection depends on the specific application: unstructured models can describe the global behavior, while unstructured models can describe specific phenomena such as metabolic pathways.
\nAnother alternative in the modeling of bioreactors is the black or gray box models, which can be used for bioreactor design, without describing in detail the phenomenology present in the system, which is mainly focused on the global behavior of the system. An important part of the modeling, design, and control of bioreactors is the selection of appropriate sensors. It is often difficult to find suitable sensors for the process, so soft sensors are an interesting alternative to solve this problem.
\nOnce a model describing the dynamical behavior of the bioreactor reaches the available condition, the control scheme can be designed. The goal may be different in each scenario: in the case of variables such as pH, this objective is usually regulation, but in variables such as concentrations and temperature, tracking is usually the goal. In any of these cases, slow and smooth dynamics inherent in these processes usually allow PID controllers to bring system states to the set point efficiently.
\nThe authors want to thank ITESO Fund for Research Support 2017-2018.
\nThe authors declare not to have a conflict of interest.
Edited by Jan Oxholm Gordeladze, ISBN 978-953-51-3020-8, Print ISBN 978-953-51-3019-2, 336 pages,
\nPublisher: IntechOpen
\nChapters published March 22, 2017 under CC BY 3.0 license
\nDOI: 10.5772/61430
\nEdited Volume
This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\\n\\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\\n\\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\\n\\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\\n\\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\\n\\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\\n\\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\\n\\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\\n\\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\\n\\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\\n\\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\\n\\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
\\n"}]'},components:[{type:"htmlEditorComponent",content:'This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\n\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\n\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\n\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\n\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\n\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\n\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\n\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\n\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\n\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\n\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\n\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
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