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",isbn:"978-1-83968-918-5",printIsbn:"978-1-83968-917-8",pdfIsbn:"978-1-83968-919-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"1942bec30d40572f519327ca7a6d7aae",bookSignature:"Prof. Palanikumar Kayaroganam",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10198.jpg",keywords:"Response Surface, Modeling, Second Order, Curve Fitting, Optimization, Multiple Performance Optimization, Experimentation, Industry Needs, Engineering Applications, Response Surface Modeling, Service Sector, Residual Analysis",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 25th 2020",dateEndSecondStepPublish:"October 23rd 2020",dateEndThirdStepPublish:"December 22nd 2020",dateEndFourthStepPublish:"March 12th 2021",dateEndFifthStepPublish:"May 11th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A pioneering researcher in statistical modeling, simulation and optimization, head of the Sri Sairam Institute of Technology, receiver of the best researcher award from ISTE, world top 1% reviewer by Publon, holder of 15 published patents with Google h-index of 45.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"321730",title:"Prof.",name:"Palanikumar",middleName:null,surname:"Kayaroganam",slug:"palanikumar-kayaroganam",fullName:"Palanikumar Kayaroganam",profilePictureURL:"https://mts.intechopen.com/storage/users/321730/images/system/321730.jpg",biography:'Dr. K. Palanikumar is presently Professor and Principal, Sri Sairam Institute of Technology, Chennai, India. He completed his Masters Degree in Production Engineering with first rank from Annamalai University and obtained Ph.D Degree in Mechanical Engineering from Anna University, Chennai, Tamilnadu, India. He has more than 25 years of experience in teaching and research. He has advised 15 Ph.D scholars as a supervisor and has received “National Best Researcher Award" from ISTE. He has published more than 110 papers in SCI Journals having impact factor and 225 papers in Scopus Indexed Journals. He has also published/presented more than 300 papers in international and national journals and conferences. His Google Scholar h-index is 45. He is the life member of Indian Tribology Society of India, Indian Society for Non- Destructive testing (ISNT), Indian Welding Society, Indian Society for Technical Education and Fellow of Institution of Engineers (India), and Indian Institution of Production Engineers. He is the Associate Editor of “Journal of Modern Manufacturing Technology” and “International Journal of Materials Forming and Machining Processes (IJMFMP)”. His current area of research includes composite materials, statistical analysis, machining of composite materials, modern manufacturing, optimization, simulation and modeling.',institutionString:"Sri Sairam Institute of Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"184402",firstName:"Romina",lastName:"Rovan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/184402/images/4747_n.jpg",email:"romina.r@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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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"}},{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:"65117",title:"Atypical Protein Kinase Cs in Melanoma Progression",doi:"10.5772/intechopen.83410",slug:"atypical-protein-kinase-cs-in-melanoma-progression",body:'\nThe protein kinase C (PKC) is a family of Ser/Thr kinases which are involved in transmembrane signal transduction pathways triggered by various extra and intracellular stimuli [1]. Over time, more information has become available since the 1st discovery of PKCs in 1970s. Activation of PKCs may depend on calcium ions and cofactors like the lipid metabolite diacylglycerol (DAG) and phosphatidylserine (PS) [2, 3]. The PKC family consists of fifteen isozymes which are grouped into three on the basis of their co-factor requirements [4, 5]. First group is the conventional PKCs (cPKC) which includes the isoforms alpha (α), beta I (βI), beta II (βII) and gamma (γ) and they require calcium ions, DAG and phospholipids for the activation. Second group is the novel PKCs (nPKC) and it includes delta (δ), epsilon (ε), eta (η) and theta (θ). These are calcium ion independent but dependent on DAG and phospholipids. The aPKC isozymes are the third group, which are independent of Calcium and DAG for their activation. PKC-ζ and PKC-ι in humans (lambda (λ) is the mouse homologs of iota) are the three aPKCs. Protein kinase D, mu (μ) and some PKC-related kinases (PRK1, PRK2 and PRK3), known as PKN are also considered as PKCs [6].
\nPKCs have extremely conserved carboxyl-terminal catalytic domain (kinase domain) and PKC isozymes differ from each other on the basis of their amino-terminal (N-terminal) regulatory domain. The N-terminal domain is very important for secondary messenger binding, recruiting the enzyme to the membrane and protein-protein interactions [2]. The pseudosubstrate (PS) domain is located at the N-terminal. PS has a peptide-sequence similar to that of a substrate but lacks alanine in the phosphoacceptor position. In the inactive form of PKCs, the PS prevents complete activation of PKC by blocking the substrate binding pocket [7]. The PS is released upon activation [8, 9]. The activation of PKCs typically involves a cascade of three coordinated phosphorylation events [10, 11]. First, phosphorylation takes place at the activation loop triggered by phosphoinositide-dependent kinase-1 (PDK-1) [12, 13, 14]. This initiates a chain reaction that involves autophosphorylation at the turn motif that further stimulates the autophosphorylation at hydrophobic motif of N-terminal [13]. The autophosphorylation at hydrophobic motif is the third and concluding step of the activation.
\nAtypical PKCs contains two structurally and functionally distinct isozymes in human, PKC-ι and PKC-ζ. The amino acid sequences in both PKC-ι and PKC-ζ are very similar to each other [15, 16]. PKC-ι is encoded by the PRKCI gene and PKC-ζ is encoded by the PRKCZ gene. They are believed to be involved in cell cycle progression, tumorigenesis, cell survival and cell migration of carcinoma cells. Additionally, aPKCs play important roles in insulin-stimulated glucose transport [16, 17]. PKC-ι specifically has a strong influence on cell cycle progression. It is also involved in changing cell polarity during cell division [17]. Lung cancer cell proliferation is highly dependent on the PKC-ι level since it increases tumor cell proliferation by activating the ERK1 pathway [15]. PKC-ι and PKC-ζ are expressed in both transformed and malignant melanoma [18]. Overexpression of PKC-ι plays an important role in the chemoresistance of leukemia [19]. PKC-ι is involved in glioma cell proliferation by regulating by phosphorylation of cyclin-dependent kinase activating kinase/cyclin-dependent kinase 7 pathway [20, 21]. A very important study by Selzer et al., investigated the presence of 11 PKC isoforms in 8 different melanoma metastases, 3 normal melanocyte cell lines and 3 spontaneously transformed melanocytes along with several melanoma tumor samples. PKC-ζ and PKC-ι were found in all transformed melanocytes and melanoma metastases samples in very high levels. PKC-ζ was also found in normal melanocytes in low levels. Figure 1 demonstrates a comparison of the aPKC expression in two normal melanocyte cell lines (PCS-200-013 and MEL-F-NEO) against two melanoma cell lines (SK-MEL-2 and MeWo) which were used for our studies in Acevedo-Duncan’s laboratory at the University of South Florida. As demonstrated in Figure 1, normal melanocytes did not show detectable levels of PKC-ι compared to the larger expression observed in SK-MEL-2 and MeWo cell lines. Moreover, PKC-ζ expression was very low in both normal melanocyte cell lines compared to heightened expression in melanoma cells. These results supported the expression patterns demonstrated by patient samples as described in Selzer et al. [18]. All these results indicate a strong relationship between aPKCs and melanoma progression. Here, we discuss our key findings of our recent research on melanoma owing to its relationship with aPKCs in a detailed manner.
\naPKC expression comparison of normal melanocytes and melanoma cell lines. The expression of PKC-ι and PKC-ζ was reported at approximately 50 and 100% confluency for PCS-200-013 and MEL-F-NEO normal melanocytes against SK-MEL-2 and MeWo metastatic melanoma cells. Western blots were conducted with 50 μg of total proteins loaded in each lane and the complete procedure was adapted from Ratnayake et al. [67].
Nuclear factor kappa-light-chain-enhancer of activated B (NF-κB) and phosphatidylinositol 3-kinase and protein kinase B (PI3K/AKT) pathways are often hyper-activated in many different cancers in order to promote cellular differentiation, growth and survival. Overexpression of aPKCs is often associated with anti-apoptotic effects in many cancers. We have published outcomes of in-vitro treatments of aPKC specific inhibitors in which, treatments decreased melanoma cell population markedly compared to normal melanocytes [22, 23, 24, 25]. These results confirm that melanoma cellular functions are highly dependent on aPKCs, but normal melanocytes do not depend on aPKCs.
\nOur recent publications describe the in-vitro effects of five aPKC inhibitors on melanoma cell lines compared to normal melanocytes [22, 23]. 2-Acetyl-1,3-cyclopentanedione (ACPD) and 3,4-diaminonaphthalene-2,7-disulfonic acid (DNDA) are specific to both PKC-ι and PKC-ζ while [4-(5-amino-4-carbamoylimidazol-1-yl)-2,3-dihydroxycyclopentyl] methyl dihydrogen phosphate (ICA-1T) along with its nucleoside analog 5-amino-1-((1R,2S,3S,4R)-2,3-dihydroxy-4-methylcyclopentyl)-1H-imidazole-4-carboxamide (ICA-1S) which are specific to PKC-ι and 8-hydroxy-1,3,6-naphthalenetrisulfonic acid (ζ-Stat) is specific to PKC-ζ. These compounds were identified from the National Cancer Institute/Developmental Therapeutics Program (NCI/DTP) database using molecular docking simulations. “AutoDockTools” and “AutoDock Vina” programs were used for the docking simulation by selecting structural pockets in PKC-ι and PKC-ζ which were compatible with small drug like molecules. Sixteen different pockets were identified on PKC-ι and PKC-ζ structures using “fpocket,” a very fast open source protein pocket (cavity) detection system based on Voronoi Tessellation. We confirmed the presence of a potentially druggable allosteric site in the structure of PKC-ι using solved crystal structure of PKC-ι. The pocket located in C-lobe of the kinase domain, is framed by solvent exposed residues of helices ⍺F-⍺I and the activation segment. PKC-ι inhibitors were predicted to interact with this site with moderate affinity based on molecular docking. Combinations of drugs targeting the ATP binding site and allosteric sites would be expected to more effectively inhibit cancer cell growth [23]. More details about other aPKC inhibitors form different research groups can be found in the latter portion of the chapter.
\nAll five inhibitors were cytostatic to malignant cells rather than cytotoxic. Cells underwent growth arrest before apoptotic stimulation. Regardless, ICA-1S and ICA-1T showed a minor toxicity towards malignant melanoma cells, suggesting that all inhibitors were effective against malignant cells without harming normal cells. This is an indication that melanoma cells heavily rely on aPKCs to remain viable which was observed in some other cancers [19, 20, 26, 27]. These previous reports show that overexpression of aPKCs have an anti-apoptotic effect [15, 19, 20, 21, 28, 29]. Our two previous publications on the applications of aPKC specific inhibitors report apoptosis analysis on MeWo and SK-MEL-2 cells. The data confirmed that inhibition of aPKCs lead to induce apoptosis [22, 23]. Increase in Caspase-3, increase in poly ADP ribose polymerase (PARP) cleavage, and a decrease in B-cell lymphoma-2 (Bcl-2) all indicate apoptosis stimulation [30, 31, 32, 33]. All five inhibitors have demonstrated similar pattern on these markers. But, increase in Caspase-3 levels is not always a direct indication of inducing the apoptosis due to the tight binding of cleaved Caspase-3 with X-linked inhibitor of apoptosis protein (XIAP). XIAP undergoes auto-ubiquitylation, but this process delays apoptosis until all XIAP is removed [34]. On the other hand, PARP is a known downstream target for Caspase-3, therefore we have also tested direct PARP and cleaved PARP levels upon inhibitor treatments. PARP cleavage increases upon inducing the apoptosis [35]. Bcl-2 inhibits Caspase activity by preventing Cytochrome c release from the mitochondria and/or by binding to the apoptosis-activating factor (APAF-1). In our studies, PKC-ι and PKC-ζ inhibition decreased Bcl-2 levels which depicted an increase in apoptotic activity in both SK-MEL-2 and MeWo cell lines. These data confirms that aPKCs have an anti-apoptotic effect in the tested melanoma cells.
\nPI3K/AKT mediated NF-κB activation is a major anti-apoptotic pathway, wherein aPKCs play a role in releasing NF-κB complex to translocate to the nucleus and promote cell survival. Win et al. reported that PKC-ζ actively upregulates the activation of NF-κB nuclei translocation thereby inducing cancer cell survival in prostate cancer cells [36, 37]. In addition, PI3K stimulates IκB kinase (IKKα/β) through activation of AKT by phosphorylation at S473 or S463, which ultimately stimulates translocation of NF-κB complex into the nucleus, heightening cell survival [38]. Phosphatase and tensin homolog (PTEN) regulates the levels of PI3K. Phosphorylation at S380 leads to the inactivation of PTEN, thereby increasing the levels of PI3K followed by enhancement in phosphorylated AKT (S473/S463). Our data indicates that inhibition of PKC-ι and PKC-ζ expressively decreased the levels of phosphorylated PTEN and phosphorylated AKT [23]. This specifies that PKC-ζ and PKC-ι may upregulate the PI3K/AKT pathway to induce cellular survival of melanoma cells. Additionally, we tested the levels of NF-κB translocation by separating the nuclear extracts from the cell lysates and found that NF-κB levels in the nuclei decreased upon aPKC inhibition. This suggested that translocation of activated NF-κB into nuclei was blocked as a result of inhibition of aPKCs. Furthermore, we also found that aPKC inhibition increased the levels of inhibitor of kappa B (IκB) while decreasing the levels of phosphorylated IκB (S32) and phosphorylated IKKα/β (S176/180), confirming that both PKC-ι and PKC-ζ play a role in phosphorylation of IKKα/β and IκB: increased levels of IκB therefore remain bound to NF-κB complex and prevent the translocation to the nucleus to promote cell survival (Figure 2). As summarized in Figure 2, our data also demonstrate the effects of TNF-α stimulation on the expression of aPKCs [23]. TNF-α is a cytokine, involved in the early phase of acute inflammation by activating NF-κB. TNF-α stimulation significantly increased NF-κB levels in both cytosol and nuclei. Increased NF-κB production promotes increases in total and phosphorylated aPKCs and increased the levels of Bcl-2, which enhanced melanoma cell survival. We observed amplified levels of IκB and NF-κB, which together enhanced the phosphorylation of IκB due to the augmented levels of aPKCs [23]. On the other hand, PI3K/AKT signaling can be diminished by inhibiting aPKCs via downregulation of NF-κB. These results confirm that both PKC-ζ and PKC-ι are rooted in cellular survival via NF-κB and PI3K/AKT pathways.
\nA schematic summary of the involvement of PKC-ι and PKC-ζ in melanoma progression via NF-κB and PI3K/AKT pathways. Upon extracellular stimulation with TNF-α, activation of AKT through PIP3 takes place as a result of inactivation of PTEN. Activated AKT pathway can lead to cell survival, rapid proliferation and differentiation which are critical parts of melanoma progression. AKT could indirectly stimulate NF-κB pathway along with PKC-ι and PKC-ζ in which they play a stimulatory role on IKK-α/β in order to promote the releasing the NF-κB complex from IκB to translocate into nucleus.
Throughout EMT, epithelial cells lose apical-basal polarity, remodel the extra cellular matrix (ECM), rearrange the cytoskeleton, drive changes in signaling programs that control the cell shape maintenance and adapt gene expression to obtain mesenchymal phenotype, which is invasive and increases individual cell motility [39]. EMT’s key features comprise downregulation of E-cadherin to destabilize tight junctions between cells and upregulation of genes such as Vimentin that may assist mesenchymal phenotype.
\nVimentin is a very important structural protein which belongs to the family of type III intermediate filament proteins. Intermediate filaments (IFs) make up a vast network of interconnecting proteins between the plasma membrane and the nuclear envelope and convey molecular and mechanical information between the cell surface and the nucleus. IF protein expression is cell type and tissue specific. Mesenchymal cells, fibroblasts, lymphocytes and most types of tumor cells express Vimentin [40, 41]. Vimentin is essential for organizing microfilament systems, changing cell polarity, and thereby changing cellular motility. Moreover, increased Vimentin expression during EMT is a hallmark of metastasis which plays a very important role in gaining rear-to-front polarity for transforming epithelial cells. In addition to EMT, Vimentin expression is observed in cell mechanisms involved in cellular development, immune response and wound healing [22, 23, 42].
\nVimentin is activated via phosphorylation. Various kinases such as; RhoA kinase, protein kinase A, PKC, Ca2+/calmodulin-dependent protein kinase II (CaM kinase II), cyclin-dependent kinase 1 (CDK1), RAC-alpha serine/threonine-protein kinase (AKT1) and RAF proto-oncogene serine/threonine-protein kinases (Raf-1-associated kinases) have been shown to play a role in regulation of Vimentin via phosphorylation. Studies show that amino acid sites S6, S7, S8, S33, S38 (same as S39 since some literature use M as the starting amino acid of Vimentin), S55 (or S56), S71, S72, and S82 (S83) amongst others, serve as specific phosphorylation sites on the head region of Vimentin [41, 43, 44, 45, 46, 47, 48, 49, 50].
\nOur previous reports demonstrated the effects of aPKC inhibition on melanoma cell migration and invasion [22, 23]. Migration and invasion studies in cancer research are very important because the main cause of death in cancer patients is related to metastatic progression. For cancer cells to spread and distribute throughout the body, they must migrate and invade through ECM, undergo intravasation into blood stream and extravasation to form distant tumors [51]. ACPD and DNDA treated samples demonstrated a reduction of melanoma motility but it was not conclusive which aPKC is responsible for upregulating metastasis, since both ACPD and DNDA inhibit PKC-ι and PKC-ζ [22]. This was solved using specific PKC-ι inhibitors (ICA-1S and ICA-1T) and a PKC-ζ specific inhibitor ζ-Stat. Migration and invasion were markedly reduced for samples treated with ICA-1T and ICA-1S compared to ζ-Stat treated samples, suggesting that PKC-ι inhibition significantly diminishes melanoma cell migration and invasion suggesting that only PKC-ι is involved in EMT in melanoma [23]. aPKC/Par6 signaling is known to stimulate EMT upon activation of TGF-β receptors in lung cancer cells. TGF-β activated aPKC/Par6 stimulates degradation of RhoA which leads to the depolymerization of filamentous actin (F-actin) and loss of epithelial structural integrity resulting a reduction in cell-cell adhesion [52]. RhoA is a GTPase, which promotes actin stress fiber formation thereby maintains cell integrity. Furthermore, TGFβ upregulates Zinc finger protein SNAI1 (SNAIL1) and Paired related homeobox-1 (PRRX1) transcription factors that drive genetic reprogramming to facilitate EMT [53]. Cells lose E-cadherin while gaining Vimentin during this process. We have recently reported that inhibition of PKC-ι using ICA-1T and ICA-1S significantly increased the levels of E-cadherin and RhoA while decreasing total and phosphorylated Vimentin (S39) and Par6. None of these protein levels were significantly changed as a result of PKC-ζ inhibition. We also reported that TGFβ treatments increased the expression of PKC-ι, Vimentin, phosphorylated Vimentin and Par6 while decreasing E-cadherin and RhoA [23]. These results confirmed the involvement of PKC-ι in EMT stimulation. Immunoprecipitation of PKC-ι confirmed a strong association with Par6 in both melanoma cells which was confirmed with reverse-immunoprecipitation of Par6. Previously published reports state that both aPKCs associate with Par6 and phosphorylate at S345 [54]. Interestingly, only PKC-ι showed an association with Par6, which confirmed that PKC-ι is a major activator of EMT in melanoma. In addition, immunoprecipitation of PKC-ι and Vimentin strongly confirmed an association between PKC-ι and Vimentin [22]. siRNA knockdown of PKC-ι and PKC-ζ, immunofluorescent staining and real time quantitative polymerase chain reaction (RT-qPCR) techniques were also used to study the association of Vimentin with PKC-ι. Our immunofluorescence staining revealed that the shape of melanoma cells significantly changed upon inhibition of PKC-ι. Both Vimentin and PKC-ι levels were relatively low in ICA-1T treated cells in comparison to their respective controls. In addition, invasive characteristics such as formation of lamellipodia, filopodia and invadopodia were distinctively visible in both controls, though they were not apparent in PKC-ι inhibited cells. Reduction of nuclei volume and cell size, also confirmed the growth retardation we observed in melanoma cells upon aPKC inhibitor treatments that had resulted in lesser growth in treated cells. As observed in qPCR experiments, treatments with PKC-ι specific inhibitors ICA-1T and ICA-1S, depicted a corresponding downregulation of PKC-ι suggested that PKC-ι plays a role in its own regulation [23]. This is further discussed in the next topic in Part 4.
\nPhosphorylation of Vimentin at S39 is required for its activation and inhibition of PKC-ι diminishes this activation process. The reduced levels of total Vimentin observed in Western blots for ICA-1T and ICA-1S treated cells indicate that without PKC-ι, unphosphorylated Vimentin undergoes rapid degradation. In addition to activating Vimentin, PKC-ι appears to play a role in regulating Vimentin expression in some carcinoma cells [55].
\nAs summarized in Figure 3, based on our published reports, we believe that TGFβ stimulated PKC-ι/Par6/RhoA and Smad2/3 pathways to induce EMT in melanoma through transcriptional activities of SNAIL1 and PRRX1. Vimentin and PKC-ι activation are upregulated simultaneously to facilitate EMT in melanoma. PKC-ι activated Vimentin thereby regulates the dynamic changes in melanoma metastasis. Our results further confirms that PKC-ι inhibition using specific inhibitors such as ICA-1T and ICA-1S, not only reduce melanoma cell survival but also negatively affects the melanoma metastatic progression by downregulating EMT. Taken together, this novel concept can be used to develop more specific effective therapeutics for melanoma patients based on PKC-ι. PKC-ι can be used as a novel biomarker to mitigate melanoma metastasis using specific inhibitors.
\nA schematic summary of the involvement of PKC-ι in melanoma progression via activation of EMT and Vimentin signaling. Upon extracellular stimulation with TGFβ, PKC-ι associates and activates Par6, which stimulates the degradation of RhoA thereby upregulates EMT. SNAIL1 and PRRX1 are two very important transcription factors and they drive EMT process by upregulating Vimentin while downregulating E-cadherin. PKC-ι activates Vimentin by phosphorylation and this initiates disassembly of VIF and facilitates cellular motility. During this process, cadherin junctions are disrupted as a result of loss of E-cadherin and β-catenin is translocated to nucleus to upregulate the production of facilitating proteins such as CD44 which further stimulate migration and EMT. Activated Vimentin changes cell polarity to maintain the mesenchymal phenotype of melanoma cells in-vitro.
In our previous study, we identified PKC-ι as a major component responsible for inducing cell growth, differentiation, survival and EMT promotion in melanoma, as a result of PKC-ι specific inhibitor applications [22, 23]. In addition to these findings, we noted that the inhibition of PKC-ι leads to a decrease in its own expression of PRKCI gene. This indicates that PKC-ι plays a role in its expression in melanoma. The PRKCI gene is located on chromosome 3 (3q26.2), a region identified as an amplicon [56]. Our latest published results describe the transcriptional regulation of PRKCI with an insight view of cell signaling crosstalk in melanoma cells. FOXO1 and c-Jun were identified as possible transcription factors that can bind to the PRKCI promoter region through PROMO and Genomatix Matinspector. These two transcription factors (TFs) were systematically silenced to analyze the downstream effect on PKC-ι expression.
\nc-Jun is the first discovered oncogenic TF that is associated with metastatic breast cancer, non-small cell lung cancer and several other types of cancer [57, 58, 59]. We found a positive correlation between c-Jun with PKC-ι expression. Phosphorylation at S63 and S73 by JNKs (c-Jun N-terminal kinases) activates c-Jun, thereby increasing c-Jun targeted gene transcription. c-Jun stimulates the oncogenic transformation of ‘ras’ and ‘fos’ in several type of cancers [60]. FOXO1 is a well-known tumor suppressor and we found it suppresses the expression of oncogenic PKC-ι. FOXO1 also plays a key role in gluconeogenesis, insulin signaling and adipogenesis. AKT is known to deactivate FOXO1 by phosphorylating FOXO1 at T24, which drives FOXO1 nuclear exclusion, leading to ubiquitination [61, 62]. Therefore, the phosphorylation of FOXO1 is an indication of its downregulation. FOXO1 plays a crucial regulatory role in both the intrinsic and extrinsic pathways of apoptosis in many types of cancers, demonstrating an association between FOXO dysregulation and cancer progression [63, 64]. Additionally, upregulation of FOXO1 inhibits cancer cell proliferation, migration and tumorigenesis [65]. Notably, FOXO1 can also be downregulated by ERK1/2 and PKC-ι, in addition to AKT [66]. In our most recent study, we demonstrated that, due to PKC-ι inhibition, the availability of active phosphorylated PKC-ι decreases, making it ineffective at deactivating FOXO1 through phosphorylation at T24. Importantly, this is the first showing direct involvement of PKC-ι in its own expression regulation and PKC-ι inhibition that leads to continuous upregulation of FOXO1 [67].
\nAs we discussed earlier in Part 2, our previous data showed that PKC-ι inhibition significantly downregulated the PI3K/AKT1 pathway, thereby suppressing the activation of AKT [22, 23]. Downregulation of NF-κB due to PKC-ι inhibition, result in downregulation of AKT. Our latest data shows that it increases total FOXO1 level, while reducing its phosphorylated levels [67]. This confirms that NF-κB downregulation upregulates FOXO1 activity as a result of PKC-ι specific inhibition. Elevated FOXO1 negatively influenced PKC-ι expression and phosphorylation at T555. This further confirms our previous observations with PKC-ι inhibition with ICA-1T and ICA-1S, where total PKC-ι, phosphorylated PKC-ι, NF-κB activation and activated AKT (S473) were significantly reduced [23]. These results could be due to the tight regulation of PKC-ι expression by FOXO1, which retards PRKCI from transcription. Such results confirmed that FOXO1 is a major regulator which suppresses the expression of PRKCI. Interestingly, c-Jun and phosphorylated c-Jun (S63) levels were not significantly altered as a result of NF-κB siRNA knockdown. This suggests that NF-κB diminution does not affect PKC-ι expression over c-Jun. Instead, c-Jun is known to protect cancer cells from apoptosis by cooperating with NF-κB signaling to facilitate survival upon TNF-α stimulation [68]. These overall effects have been summarized in Figure 4. We have previously shown how TNF-α upregulates NF-κB and AKT pathways along with PKC-ι expression in these two melanoma cell lines [23]. However, the data from the current study suggest that the TNF-α downstream target is mainly FOXO1, where it ‘switches off’ through the phosphorylation of elevated AKT. The inhibition of PKC-ι diminishes this AKT activation, thereby upregulating FOXO1 activity [67].
\nA schematic summary of the regulation of the expression of PKC-ι in melanoma. This model depicts the interactions between NF-κB, PI3K/AKT/FOXO1, JNK/c-Jun and STAT3/5 signaling pathways during the PKC-ι regulation. PKC-ι plays an important role in the regulation of its own expression in an intricate signaling web through c-Jun and FOXO1. PKC-ι is overexpressed in melanoma cells due to elevated transcriptional activity of c-Jun with the aid of PI3K/AKT, NF-κB, STAT3/5 signaling. The specific inhibition of PKC-ι initiates a disruption to rapid PKC-ι expression cycle in melanoma where the reduced activity of PKC-ι downregulates the NF-κB pathway and its transcriptional activity, which in turn diminishes the expression of IL-6/8. As a result of this AKT activity reduction, FOXO1 gets upregulated. FOXO1 turns out to be the most important TF regulating PKC-ι expression after the disruption initiated as a result of PKC-ι inhibition. Dominant FOXO1 negatively regulates the expression of PKC-ι and also diminishes the JNK activity to retard its activation of c-Jun. we found c-Jun as the transcription component which upregulates PKC-ι expression. The downregulation of IL-6 and IL-8 expression leads to the lessened STAT3/5 signaling, which causes c-Jun transcriptional reduction. This whole process continues and leads to the further downregulation of NF-κB, AKT and JNK/c-Jun while upregulating FOXO1, which leads to the continuation of the attenuation of PKC-ι expression. As a result, the total PKC-ι level decreases in melanoma cells.
On the other hand, siRNA treatments for of c-Jun and FOXO1 revealed that c-Jun also plays a role in PKC-ι expression, apart from FOXO1. Enzyme-linked immunosorbent assay (ELISA) experiments were conducted to investigate cell signaling crosstalks. These findings demonstrated links between PKC-ι expression with the cytokines, interleukin (IL)-6, IL-8, IL-17E and ICAM-1, along with some other key cellular signaling points. Phosphorylation at S536 on the NF-κB p65 transactivation domain is an indication of dimerization of NF-κB subunits. ELISA results revealed a more than two fold increase of NF-κB p65 (S536) in PKC-ι inhibited samples. According to Ratnayake et al., PKC-ι inhibition downregulates NF-κB translocation to the nucleus therefore phospho-NF-κB levels increase in order to diminish the effect of PKC-ι inhibition. However, elevated FOXO1 does not allow NF-κB to annex the control since it is missing the essential assistance needed from PKC-ι due to its inhibition from ICA-1T and ICA-1S inhibitors [67] (Figure 4). Abnormal STAT3/5 activity has been shown to be connected to multiple types of cancer [69, 70, 71, 72]. Cytokines such as IL-6 and IL-5, upregulate STAT signaling, thereby induces cell survival in many types of cancer [69, 70, 73]. Importantly, upregulated STAT3 increases the transcription of c-Jun [69, 74]. Our ELISA results indicated that STAT3 and STAT5 activities were retarded due to PKC-ι inhibition, signifying c-Jun diminution. Hornsveld et al., and few other reports have provided connections between the JNK pathway and FOXO1, explaining its tumor suppressing features by weakening JNK activity [75, 76]. However, JNK activates c-Jun. Our latest Western blot and real time qPCR analysis demonstrated that c-Jun depletion lessened PKC-ι expression, which suggested that c-Jun acts as an activator of PKC-ι expression. This confirms that both FOXO1 and c-Jun are involved in regulating PKC-ι expression. The results suggest that FOXO1 plays a major role over c-Jun only upon PKC-ι inhibition, possibly through multiple mechanisms, such as the reduction of JNK signaling, retarding PKC-ι expression and cell cycle arrest. FOXO1 induces cell cycle arrest by promoting the transcription of cell cycle kinase inhibitors or cyclin-dependent kinase inhibitor (CKI). p21 and p27 are two well-known downstream CKIs induced by FOXOs [66, 75]. Especially, FOXO1 is also believed to induce anoikis, which is apoptosis that occurs when cells detach from the extracellular matrix. Our ELISA results revealed significantly higher levels of p21 in PKC-ι inhibited cells, suggesting that the inhibition of PKC-ι induces cell cycle arrest through FOXO1 [67]. This also explains why apoptosis was stimulated in melanoma cells as a result of inhibition of PKC-ι in addition to downregulation of PI3K/AKT and NF-κB pathways. Overall, FOXO1 is very important in enhancing anti-tumor activities upon PKC-ι inhibition and it plays the central role of oncogenic PKC-ι depletion.
\nThe next three paragraphs focus on more details concerning cytokine expression changes observed as a result of PKC-ι inhibition [67]. IL-6, IL-8, IL-17E and ICAM-1 expression were significantly altered in melanoma cells upon PKC-ι knockdown [67]. As shown by the results of both Western blot and RT-qPCR analyses, the protein levels of IL-6 and IL-8 (as well as their mRNA levels) decreased, while the levels of IL-17E and ICAM-1 increased significantly upon PKC-ι knockdown by siRNA [67]. This suggests that c-Jun and FOXO1 driven PKC-ι expression is involved in autocrine signaling. The micro-environment of a tumor, and in particular melanoma, is regularly exposed to numerous inflammatory factors and immune cells. The effect of these factors function to either promote chronic inflammation or engage in antitumor activity [77]. Cytokines are examples of these inflammatory factors; they play an essential role in regulating the tumor microenvironments [78]. They are vital in order to promote or dysregulate tumor progression and metastasis. Chemokine C-X-C motif ligand-1 (CXCL)-1, CXCL-12, IL-18, CXCL-10, IL-6 and IL-8 are known to promote cancer metastasis. Interestingly, CXCL-1, CXCL-10, CXCL-12 and IL-18 levels were not significantly altered due to PKC-ι depletion in melanoma cells.
\nIL-6 contributes to the degradation of IκB-α, leading to the upregulation of NF-κB translocation. We have previously discussed that PKC-ι stimulates NF-κB translocation through IκB-α degradation [23]. The translocation of NF-κB to the nucleus induces cell survival through the transcription of various survival factors as well as other pro-survival cytokines [69, 73, 79]. IL-8 plays a role in regulating polymorphonuclear neutrophil mobilization. In melanoma, IL-8 has been attributed to extravasation, a key step in metastasis. Studies have shown that the expression of IL-8 in melanoma is regulated via NF-κB. When NF-κB is translocated to the nucleus, IL-8 expression increases, leading to the promotion of a more favorable microenvironment for metastasis [80, 81]. Our results indicated that both IL-6 and IL-8 expression levels decreased upon diminution of PKC-ι [67].
\nSome cytokines promote anti-tumor activity by exploiting an immune response. ICAM-1 plays a key role in the immune response, including antigen recognition and lymphocyte activation [82, 83]. ICAM-1 is known for the inhibition of tumor progression through the inhibition of the PI3K/AKT pathway. Tumor cells are exposed to cytotoxic T-lymphocytes as a result of ICAM-1 [83]. According to ovarian cancer clinical data, inhibition of ICAM-1 expression is associated with an increased risk of metastasis for the patients within the first 5 years from the point of diagnosis [82, 83]. IL-17E (IL-25) is another anti-tumor cytokine belongs to a family of cytokines known as IL-17. Treatment with recombinant active IL-17E has been shown to decrease tumor growth of melanoma and pancreatic cancer [84, 85]. The upregulation of IL-17E is linked to the increased expression of TH17 cells. T cells, such as TH17 have been implicated in the inhibition of tumor-infiltrating effector T cells. The exact mechanism of IL-17E function in the anti-tumor effect has not been studied well enough [86]. Particularly, our most recent results indicated that ICAM-1 and IL-17E protein levels and mRNA expression increased upon PKC-ι knockdown by siRNA [67]. This strongly supports that anti-tumor signaling is upregulated upon the knockdown or inhibition of oncogenic PKC-ι via an autocrine manner through IL-17E and ICAM-1. Moreover, the results suggest that IL-17E and ICAM-1 play an important down-regulatory role in the regulation of PKC-ι expression along with FOXO1, opposite to IL-6/8 assisted c-Jun [67].
\nIn conclusion, based on the published results from Acevedo-Duncan’s laboratory and other available information, it is suggested that PKC-ι itself plays an important role in its expression in a complex signaling web through the transcriptional activation/deactivation of c-Jun and FOXO1. The retarded activity of PKC-ι due to application of specific inhibitors such as ICA-1S and ICA-1T, causes a downregulation of the NF-κB pathway and its transcriptional activity, which reduces the expression/production of IL-6 and IL-8. In addition, as a result, the activity of AKT decreases, upregulation of FOXO1 activity takes place. FOXO1 is the most important TF regulating PKC-ι expression and IL-17E and ICAM-1 cytokines seem to play a stimulatory role for FOXO1 to attenuate PKC-ι. FOXO1 negatively regulates the expression of PKC-ι, diminishing JNK activity which leads to retard c-Jun activation. IL-6 and IL-8 expression are downregulated via PKC-ι-mediated NF-κB transcriptional activity reduction. IL-6/8 attenuation leads to STAT3/5 signaling downregulation, further reducing c-Jun expression. This whole process continues and leads to the further downregulation of NF-κB, c-Jun and upregulation of FOXO1, which leads to the continuation of the depletion of PKC-ι expression. As a result of this sequence of events, the total PKC-ι level decreases in melanoma cells, which initiated as a result of PKC-ι inhibition using specific inhibitors. These results indicate that PKC-ι is being regulated in a rather complex manner, which involves itself as a key component. PKC-ι specific inhibition using ICA-1S and ICA-1T leads to a decrease in its own production, and during this process, PKC-ι inhibition also triggers multiple anti-tumor/pro-apoptotic signaling. This makes PKC-ι one of the central key points of interest to specifically target and diminish as a means of treating melanoma. The results also strongly suggest that PKC-ι is a prime novel biomarker that can be targeted to design and develop personalized and targeted therapeutics for melanoma.
\nWe have discussed the effects of five aPKC specific inhibitors throughout this chapter. The structures of these compounds are shown in Figure 5.
\nStructures of the aPKC specific inhibitors (ACPD, DNDA, ζ-Stat, ICA-1S and ICA-1T). chemical structures of ACPD (a) and DNDA are specific to both PKC-ι and PKC-ζ, ζ-Stat (C) is specific to PKC-ζ while ICA-1S (D) and ICA-1T (E) are specific to PKC-ι. molecular weights (MW) of ACPD (140.14 g/mol), DNDA (318.32 g/mol), ζ-Stat (MW = 384.34 g/mol), ICA-1S (MW = 256.26 g/mol) and ICA-1T (MW = 336.24 g/mol), respectively.
Atypical PKCs were first considered as a novel therapeutic target by Stallings-Mann et al. in 2006. They screened aurothiomalate as a potent inhibitor of the interaction between PB1 domain of PKC-ι and Par6 [87]. Half maximal inhibitory concentration (IC50) of aurothiomalate ranged from 300 nM to 100 μM and indicated that some cell lines are insensitive (i.e. H460 and A549 lung cancer cells) to the inhibitor [87].
\nBlázquez et al. tested calphostin C and chelerythrine against West Nile virus (WNV) which significantly inhibit WNV multiplication in cell culture without affecting cell viability. They report that PKCs have also been implicated in different steps during viral replication. Calphostin C and chelerythrine two wide range PKC inhibitors that target all three PKC classes. Results indicate that atypical PKCs are involved in WNV multiplication process which can be effectively retard using said inhibitors [88].
\nKim et al. reported the application of Echinochrome A as an inducer of cardiomyocyte differentiation from mouse embryonic stem cells. Echinochrome A was extracted from sea urchins. They investigated the potential use of Echinochrome A as an aPKC specific inhibitor and found that IC50 for PKC-ι is 107 μM under in-vitro kinase assay conditions. Molecular docking simulation results suggested a direct binding of Echinochrome A with PKC-ι [89].
\nAn important study by Kwiatkowski et al. identified an azaindole-based scaffold for the development of more potent and specific PKC-ι inhibitors. They described fragmented based approach an introduced a new class of potential aPKC inhibitors based on azaindole [90].
\nThe authors acknowledge the generous financial contributions from the Frederick H. Leonhardt Foundation, David Tanner Foundation, Bradley Zankel Foundation, Inc., Kyrias Foundation, Brotman Foundation of California, Baker Hughes Foundation, Irving S. Cooper Family Foundation, and the Creag Foundation.
\nThe authors declare that they have no competing interests.
With team research becoming more prevalent across disciplines [1], there remain some unresolved issues. One unresolved issue is in conceptualizing or theorizing teams as complex adaptive systems (CAS; [1]). Even though this advancement in conceptualizing teams as CAS has started to gain momentum, empirical research has yet to catch up [1]. One advantage of viewing teams as CAS is that it better positions research and theory building efforts in a team’s natural setting, occurring closer to the phenomena [1]. Complex adaptive systems adapt and change due to environmental conditions thus making them dynamic and challenging to understand [2]. Due to this self-organizing adaptation, models of CAS are lacking and are “hard to formulate” [2]. Complex adaptive systems are also hard to comprehend in that these systems are not just the aggregate of the actions of the individual parts; it is the composite of the interactions of the parts [2]. deMattos et al. [3] expressed this by highlighting complexity as the result of “the inter-relationship, inter-action, and inter-connectivity of elements within a system”. To better understand CAS we must be able to understand how behaviors emerge from these interactions [2].
The composite of these interactions within CAS is often a function of leadership. Leadership in CAS is based on driving and facilitating these interactions. Leadership, in this perspective, “is about inter-action, influencing others, and encompasses a relationality that is dyadic and networked” [4]. Within CAS, leadership often takes the form of shared leadership in which individuals, or team members, share power and roles with other members based on task and situational demands [5]. Leadership for CAS has also been found in boundary spanning in which the leader establishes required interactions with team members and external agents when needed [5]. For each type of leadership style, leading in CAS requires a change in focus, redirecting the flow of practice toward new interactions and in new directions [4].
The current theoretical article provides a model for CAS by utilizing an existing team model, the Team Emergence Leadership Development and Evaluation (TELDE) model [6]. Identifying teams as CAS, the TELDE model helps to conceptualize the behaviors and interactions that take place in a team setting to understand, drive, and predict these emergent transformations. As emergent transformations are a response to environmental forces, teams are better able to adapt and share resources to achieve a new entity to better manage these new external changes, thus requiring leadership to also share roles and resources. The theoretical model presented in the current article utilizes naturally occurring team functions as the structure (TELDE) for CAS. Collectively, these CAS that utilize the TELDE model as its structure has the potential of scaling to the broader organizational, industrial, or community levels. The theoretical model presented here is titled Complex Adaptive Team Systems (CATS). The CATS model utilizes natural occurring team functions to drive more substantial organizational activities, such as the implementing knowledge management functions [7].
The style of theorizing for the current article is the narrative style of theorizing [8, 9]. The narrative style to theorizing is in response to recent calls from researchers to add more diversity to theory development styles that are currently published [8]. Also, the narrative style to theorizing is advantageous when the goal is to show patterns and to make broad connections, providing the ability to “see the big picture” [9].
The following sections provide a review of complexity theory and some of its key components. Next, a coverage of CAS is provided with a look at utilizing interactions as the level of analysis when viewing CAS. Finally, a review of the TELDE model is presented along with a model of CAS that utilizes the TELDE model and natural occurring team processes. This model, the Complex Adaptive Team System (CATS) model, provides the structure that organizations can implement when addressing today’s complexity.
Borzillo and Kaminska-Labbe [10] highlighted enabling leadership for communities of practice, indicating the role of leadership is to create situations that increase the social interactions of individuals. Here complexity theory addresses knowledge creation by facilitating the number of interactions or connections among agents. Within organizational settings and from a learning organization perspective, Borzillo and Kaminska-Labbe [10] identified the leaders’ role as being one that increases connectivity for the enhancement of cooperation and learning. Complexity theory has seen growth within the leadership literature: strategic leadership [11]; managerial leadership [12]; organizational leadership [13]; in viewing leadership as an emergent, interactive dynamic [14]; and for viewing the dynamic and distributed nature of leadership [15].
Anderson et al. [16] identified complexity in the interactions between individual parts of an open system and to the unpredictable patterns that emerged from these interactions. Antonacopoulou and Chiva [17] identified both interaction and inter-dependence processes across different levels (i.e., individual, team, department, organization) as being critical to emergence in complex systems. These functions highlight individual agents and their social structures as being synonymous with fractals, they have the potential to operate both as a part of the system and as a whole at the same time [17]. Understanding complexity and the systems that make up social complex systems is essential in making sense of the dynamics leading to the interactions, resulting in interdependence between agents or systems [17]. Complexity theory, or complexity science, is viewed as being one method to investigate the properties and behaviors of these non-linear dynamic systems [3].
A distinction between complex and chaos systems is in order. Complex systems are non-deterministic whereas chaos systems are deterministic [13], however both are non-linear systems. Non-deterministic systems provide no means of predicting future states while deterministic systems allow for prediction of future states. Complexity theory is:
A study of changing patterns of order, self-organization or constrained diversity. Complexity arises from chaos theory [18] which first identified how order can be found in disorder (chaos). Chaos theory, in this sense, describes a mathematical concept that delineates how within different systems, patterns appear but in a random fashion. [16]
Complexity theory is also useful for understanding nonlinear systems [13, 19, 20]. Similarly, complex systems have been described as exhibiting butterfly effects in which a small change in the system could potentially lead to a large change overall [12, 13, 19]. Likewise, Crawford and Kreiser [21] identified the power law effect in which changes at one level can result in extreme changes at other levels within the system and Hammer et al. [22] described complexity as being a “perturbation, or disturbance, to a system”. Burgelman and Grove [23] identified nonlinearity as being a property in which the magnitude of the output is not linearly related to the input.
Complexity theory provides a framework for understanding complex systems by identifying and recognizing the behaviors of interdependent, heterogeneous, and autonomous agents or systems. Here it is the patterns of the interactions from autonomous agents acting interdependently within a network or system that are under investigation [10]. Hunt et al. [12] identified emergence resulting from the interdependence of agents and their components. Hanseth and Lyytinen [24] placed complexity in the field of information technology (IT) as being related to increasing heterogeneous components and their relationships, dynamics, and interactions. Likewise, Uhl-Bien et al. [14] highlighted heterogeneous agents interacting in networks that produce patterns of behavior.
Bode and Wagner [25] defined complex systems as those with a variety of parts that interact in unpredictable ways. Expanding upon this definition, Bode and Wagner [25] separated complexity into two conceptualizations, structural and behavioral. Structural complexity related to the number and variety of the elements while dynamic complexity (behavioral) related to the interactions between the systems’ parts or elements [25]. Similarly, Mowles [26] identified three kinds of social problems when viewed from the lens of complexity theory: simple, complicated, and complex. Simple problems relate to those that can be solved using known recipes, complicated problems consist of many sub-problems that can be resolved collectively to solve a bigger problem, and complex problems have no recipe or formula with changing variables [26] and often require dynamic solutions as the problem’s variables change.
Anderson et al. [16] acknowledged that complex systems present a lack of predictability due to the interactions that take place among the components of a system. These interactions produce unexpected change compared to complicated systems that do not involve multiple and multi-level interactions within and among the system components, thus reducing the potential for systemic change from occurring. Complicated systems are predictable, and their components are either managed or designed to perform specific functions [16]. Bode and Wagner [25] identified that the more complex a system, the number of elements increase along with a rise in the number of potential interactions between the elements, resulting in a variety of different states that the system could exhibit at any one time.
Complex systems also produce higher levels of uncertainty or ambiguity. Mowles [26] identified four levels of uncertainty as being: a clear enough future, alternative future, a range of future, and true ambiguity. The latter levels of having either a range of futures or true ambiguity relate to the dynamics of complexity [26]. As the level of uncertainty and ambiguity increase, strategy requires processes to become intertwined [26] due to the interconnections and dynamism within the sub-systems [16]. As the level of complexity increases, it becomes more essential for leadership to be more distributed among agents of the CAS as no one individual can be expected to be an expert on all tasks and activities required of the CAS [27].
Different domains can emerge within the same system, organization, or institution given the right circumstances. Richardson [28] described a domain as an autonomous structure that is different from the whole, also identified as noise. Domains emerge under differing circumstances and environmental factors, each with their life cycle [28]. These domains appear to be spontaneous with no structure or organizing features. However, through closer investigation these emerging entities, or domains, have structure, are self-organizing, develop distinct patterns [28], and persist until its usefulness abstains or a new emergent entity replaces it. A domain’s structure moves from equilibrium to a state of disequilibrium, changing its original adaptive functions, during this emergence [29]. The literature also described this phenomenon as adaptive tension that differentiates energy between the system and its environment as factors that drive self-organizing and emergent functions [10, 15]. Being able to arrange one’s components autonomously in response to external disturbances, using self–organizing functions as opposed to top–down directives, describes the phenomenon of emergence best [29]. Here, Beck and Plowman [29] connected self-organizing systems with emergence, meaning that a system must be self-organizing (open) before it can experience emergence. Uhl-Bien et al. [14] described two characteristics of emergence, the reformulation of existing structures into new elements and its ability to be self-organizing [12].
While CAS (e.g., teams, organizations) operate between the state of equilibrium and disequilibrium, they may appear to be operating randomly. However, this false perception of random behavior is the emergence that “guides agent-based systems to potential new levels of collective behavior” [11]. Complex adaptive systems have been viewed as a complexity region operating between the “edge of order” and the “edge of chaos” [10]. These CAS transcend or evolve when on the edge of chaos, also identified as limited instability [30], and as the paradox of control [15], up to the point where some form of equilibrium sustains between stability and chaos. This emergence cannot be programmed or managed into the elements of a system; rather emergence is a product of the interaction between the elements [19].
This concept of emergent domains can be both positive as well as negative. For example, teams are utilized in the workplace to perform functions that individuals are unable to do on their own. The resources, collective experience and knowledge, afforded by functional teams, aid in the team’s overall outcome. In some cases, this outcome is not only better than expected, but it can often be unexpected as well, making the team process one of emergence. Likewise, emergence can also be detrimental, making things more complicated. Take for example the concept of wicked problems. Aagaard [31] identified wicked problems developing through turbulent environments with continually changing expectations and solutions. These constant fluctuations present problems with ever–changing variables being derived based on the current environmental conditions, as environmental conditions change so to do the variables. These cyclical dynamics could be viewed as a form of emergence that reformulates a problem organically as environmental conditions fluctuate. Addressing wicked problems requires organizations and institutions to become more adaptive in their problem-solving methodologies [31]. Wicked problems have been viewed as being influenced by CAS where institutions, “such as nations, oil companies, and utilities are important actors” [32]. Traditional problem defining practices are not practical when addressing wicked problems. Here, one addresses wicked problems with an understanding of adaptability, emergence, and interconnectivity.
The function of self-organizing is a process [33], one in which the components of the system communicate with each other and cooperate in their coordinated efforts. This self-organizing process is critical to the emergence outcome, a co-creator of emergence [33]. Adaptability is vital in that self-organizing processes allow for systems to become adaptable and react to both external (environmental changes) and internal (organizational policies and processes) forces, leading closer to emergence. Also, this adaptability leads to a system being open and non-linear as compared to a closed and linear system. Chiva et al. [30] described adaptability as a “system’s capacity to adjust to changes in the environment without endangering its essential organizational features”. Adaptability is what differentiates closed systems from open systems; closed systems maintain the status quo while open systems adapt to external forces [34]. This distinction, between closed and open systems, is an important one. Closed systems can be self-organizing; however only open systems can be adaptive through self-organization without any external (managerial) intervention. However, there are times when an organization’s features do change after adaptability forces react, this is where emergence comes into play. Emergence changes the system’s structure to a new transformed structure allowing it to better adapt to external forces.
Organizations are complex systems made up of interdependent agents [11, 29] with overlapping functions. These complex systems can often be identified as networks, projects, hierarchies [31], teams, task forces, and departments to name only a few. Anderson et al. [16] identified entrepreneurship as being a complex system in which individual entrepreneurship efforts aggregate into the macroeconomy, with each micro process being unique, self-organizing, and different from the next. Alternatively, Aritua et al. [35] called on the profession of project management to develop new techniques and methodologies by viewing multi-projects as CAS. They identified one problem was with the field treating multi-projects as the aggregate of single project techniques and methodologies, which has not been very successful.
Complex adaptive systems are composed of individual actors acting interdependently, and autonomously [19], toward common goals. These CAS also learn through interactions and adapt behaviors based on this new knowledge [29], with the ability to evolve and self-organize [24]. Complex adaptive systems are the building blocks for higher level agents or systems (e.g., organizations, economies) while continuously adapting to environmental changes, called phase transitions [33]. They are dynamic and direct energy to sustain the system’s activities and structures [19]. Simon defined a complex system as “one made up of a large number of parts that interact in a non-simple way” (as cited in [36]) while utilizing heterogeneous interactions among one another and external elements [30]. The following definitions of complex adaptive systems from the literature follow.
Complex adaptive systems are responsive systems consisting of multiple agents that “cannot be created, designed or controlled by individual actors. But the system can be influenced, nurtured and exploited by a group of actors” [31]. This responsive aspect refers to the ability of the agents to act freely [16], interdependently [31], to learn and adapt [3, 29], and are linked dynamically [14]. Agents within CAS interact in response to internal and external threats, producing both complex and adaptive behavior patterns [11]. The interactivity among independent agents makes complex systems difficult to predict [36]. Patterns, or outcomes, are unpredictable and nonlinear [21] due to the nature of complexity involved and the interconnectivity across the sub-systems. Emergence results from these interactions in which a new system evolves from constant revising and rearranging the system components [33], providing the system with new capabilities of addressing internal and external threats.
The following definition for CAS will guide the theoretical model presented in the current research:
Neural-like networks of interacting, interdependent agents who are bonded in a cooperative dynamic by common goal, outlook, need, etc. They are changeable structures with multiple, overlapping hierarchies, and like the individuals that comprise them, CAS are linked with one another in a dynamic, interactive network [14].
Complex adaptive systems, in its purest form, have been characterized as systems exhibiting characteristics of complexity theory [35]. For example, within the strategic management literature researchers identified the concept of strategic renewal as: “The incremental process through which an organization continuously adapts to the environment and explores opportunities to invoke change in its activity choices and outputs” [36]. As an evolutionary process, renewal occurs from relational exchanges (interactions) that provide organizations with systematic methods of addressing environmental change [37]. Strategic renewal views activity systems (e.g., CAS) from either an inertial view or from an adaptive view [36, 37]. The inertial view concentrates on the distribution of interdependencies (pattern) while the adaptive view focuses on information and resource flows (rules):
the interdependency pattern, the relative distribution of interdependencies among a focal firm’s activities, and
the interdependency rules, the prescriptive guidance of resource and information flows among interdependent activities [36].
Within the strategic management literature, it is the interdependency that enables strategic renewal in organizations [36]. Activity systems are complicated, in part due to their degree of modularity, concentration, and openness [36]. Modularity involves the number of subsystems within each system, concentration involves central control in one subsystem affecting other peripheral systems, and openness relates to one system’s dependency in making its own decisions, procedures, and policies separate from the other systems [36]. The more interdependent and interactive these components become, the more complex the system.
In the organizational learning literature, Antonacopoulou and Chiva [17] highlighted something similar to strategic renewal. They described the process of interdependence as a balancing act in which agents co-exist and co-evolve simultaneously. Interdependence allows processes to navigate between stability and change as part of an adaptive process. Complex adaptive systems have been identified as having the following essential components: “diversity and individuality of components, localized interactions among those components, and an autonomous process that uses the outcomes of those interactions to select a subset of those components for replication or enhancement” [3]. Similarly, Gregory et al. [38] and Hammer et al. [22] identified a total of 16 characteristics of CAS categorized into four facets: continuous varying interactions (CVI), patterns development (PD), people factors (PF), and self-organization (SO). The CVI facet involves types of interactions, the PD represents patterns that emerge from these interactions, the PF represents humans as social systems and, SO is constantly present in the background of the CAS [22].
Inter-relationships are common in social systems when taking a systems theory point of view. However, while systems theory mainly addresses closed and simple systems, complexity theory addresses complexity in open systems via CAS. In contrast to closed systems that do not interact with their external environment, open systems do. The more open a system becomes, the more it is affected by changes in its external environment. Just as individuals act in similar ways to those in proximity, the same could be said about other systems. Groups act similarly to other groups in proximity (i.e., organizational departments, executive boards), organizations act similarly to other organizations in proximity (i.e., industry, sector), communities act similarly to other communities in proximity (i.e., sister cities, smart cities), and so on. Emergence occurs when a set of individuals, as in a team setting, combine efforts to develop something positive, innovative, and unexpected. The same is true when multiple groups get together, when organizations get together, when governments get together, and so on. This perspective, that emergence can yield from interactions among collectives, has been highlighted in the literature: “Complex adaptive systems show that surprising and innovative behaviors can emerge from the interaction of groups of agents, seemingly without the necessity of centralized control” [11]. Although having a positive result is desired, negative results could also occur (i.e., riots, war), but the focus for the current article is on positive emergence. Feedback is a key component to any system, open or closed, in that it supports learning within the system and aids in identifying new properties when emergence occurs. Having the ability to adapt and learn is one primary characteristic of a CAS [3].
Open systems operating in complexity are, by definition, non-linear. Changes within and external of the system affect all other parts of the system in unpredictable (non-linear) ways. These non-linear states of dis-equilibrium do not behave randomly either; they operate on the edge of chaos [11]. With too much order the system tends to revert toward the original state of equilibrium, while too little order causes the system to potentially reach its undesirable state of chaos [39]. Given the right amount of complexity, systems can self-organize [39] and find their optimal balance.
Waldrop provided seven conditions that must be present for CAS:
A network of many agents acting in parallel.
Control is highly dispersed.
Coherent behavior in the system arising from competition and co-operation among the agents themselves.
Many levels of organization, with agents at one level serving as the building blocks for agents as a higher level.
Constant revising and rearranging of their building blocks as they gain experience.
Constant testing of its implicit or explicit assumptions about the way things are out there.
Exploitation of the many niches in the system by agents adapted to fill those niches [33].
These seven conditions [33] expand upon Holland’s [2] original conception of the essential components of CAS: parallelism, competition, and recombination. Other literature identified the following four critical characteristics of CAS as being; nonlinearity, order emerges from interactions, irreversibility, and unpredictable outcomes [24]. These four characteristics are described below:
nonlinearity, that is small changes in the input or the initial state can lead to order of magnitude differences in the output or the final state
order emerges from complex interactions
irreversibility of system states, that is, that change is path dependent; and
unpredictability of system outcomes. [24]
A leader facilitating the interactions that take place within and among CAS needs to begin at the individual level and work their way up to the organizational, industry, or network level, depending on the goal of the interaction or change initiative. This bottom-up approach is the desired approach when leading CAS. The following mechanisms are ways in which leadership can alter and support CAS. Leadership has the ability to alter:
the size of the system and the number of sub-units within it (N),
the interdependence among component units (K),
the collective schema of members (P), and
the interdependence of the system on others (C) [12].
Here, fostering and leading CAS is a function of the structure of the system, its interdependence, its collective cognitive structure, and its interdependence (f [N + K + P + C]). To facilitate the structure of a CAS the current research utilized Turner and Baker’s [6] TELDE model. Within the TELDE model the systems components act interdependently (K), team members develop collective cognitive structures (P), while operating interdependently (C) to obtain the team’s task. The size of the system and its sub-units (N), as identified by Hunt et al. [12], is a function of the number of TELDE models operating in succession. Collectively, the TELDE model along with the facilitating functions of [K + P + C] is presented in the following theoretical model as the Complex Adaptive Team System (CATS). The CATS model can be structured as a multi-team model or as a larger networking model, depending on the structure and the number of sub-units (TELDE models) in the CATS model. The CATS model provides a tool for organizations to recombine organizational resources, or to re-architect their business unit portfolios (40), when adapting to changing markets.
The following sections provide a review of Turner and Baker’s [6] TELDE model and its components, presents interactions as a new level of analysis for the CATS model, identifies the different CATS levels, discusses the role that leadership plays in implementing CATS, and places the CATS model in context (OL/LO, Organizational Change, Collective Cognitive Structures, Innovation, Cross-Business-Unit Collaborations).
The Team Emergence Leadership Development and Evaluation Model (TELDE) provides a visual representation of leadership development that derives natural, organic, leadership growth and team learning [6]. Typically, teams are not structured in a way that allow each group member to share in the team’s leadership role, provide feedback to other members during their leadership role, and reflect on their personal performance during their tenure as the team leader. While it is typical for team members to learn from other team members during teamwork (e.g., achieving the team’s tasks), it is rare for team members to learn both individually and collectively during these teamwork episodes (e.g., transition from one task to the next). Teams have historically consisted of a single leader with members relying on the leader for direction and guidance, this traditional model is still widely used today [11]. The TELDE model presents an approach in which each team member, regardless of rank within the organization, acts as the team leader for one of the team’s task-episodes (sub-task), ultimately resulting in all team members taking a leadership position while observing and providing feedback to other team members during their leadership tenure. This model provides the characteristic of “leadership development, team development, shared leadership, coaching, self-organizing and practice” [6], the characteristics of leadership development is also achieved by each team member by them taking a leadership role during one of the team’s task-episodes.
The model, as shown in Figure 1, illustrates a four-member team performing a project with four task-episodes (one task, four subtasks). The tasks are shown on the X-axis with the team members on the y-axis. As team member one takes ownership of his/her task and begins to drive it to completion, they are building their own leadership skills, as well as displaying leadership traits and characteristics for the rest of the team. As the first task concludes and team member two is taking over for the next task, a phenomenon known as transference occurs, where team member two is applying and growing in their own leadership capabilities by applying what they learned from team member one, further adapting to their own task situation. This same pattern continues for team members three and four, each member learning from the previous leader’s task achievement and eventually bringing the project to completion. At the height of each team member’s development there is a point known as leadership emergence [6]. This is the peak of adaptation to the leadership role and the high point of application of their new skills that the team members experience in their time as task leader.
The Team Emergence Leadership Development & Evaluation (TELDE) Model. Note: From Turner and Baker [63].
The TELDE model focused on leadership development at the individual level (team members) while addressing leadership as a group construct [6]. The TELDE model was presented as a model for organizational leadership development and leadership; however, this model has far-reaching potential in obtaining other organizational developmental objectives. This model could be implemented to achieve organizational change initiatives, to implement organizational culture interventions, as a means of adopting a new organizational policy, to training or onboarding new employees. The TELDE model’s utility is further expanded upon in the current research by incorporating it as the fundamental structure for the CATS model due to it being a CAS with individual agents working independently and interdependently toward a common goal while adapting and operating in a self-organizing manner toward emergence.
Complex adaptive systems are non-linear by definition, with an unlimited number of ways to abstract its processes. Richardson [28] highlighted this point by stating:
Because of the nature of nonlinearity there is a huge number of ways to abstract a (nonlinear) problem in such a way that will easily be confirmed by our limited empirical evidence, i.e., there is one way to ‘curve-fit’ a linear problem (assuming a fixed number of dimensions) but there is an infinite number of ways to ‘curve-fit’ a non-linear problem.
Richardson [28] continued by indicating that there is no one right abstraction or model when addressing non-linear models. Here, the only way to accurately model non-linear models, as in CAS, is to construct the CAS from the bottom up [28]. Rather than working backwards from some desired state of which we have limited knowledge [28], CAS should be addressed from what is known, the interactions that lead to complex patterns and emergence [12]. The function of leadership when operating in CAS is to foster and direct these interactions, leadership is inter-actional [4] through shared roles and responsibilities among the agents, resulting in a bottom-up process.
The CATS model takes the connectionist perspective for viewing, understanding, and predicting CAS. The level of analysis does not take place at either the macro level (i.e., team, department, organization) or the micro level (individual). The level of analysis identified here is new; it views the interactions between two independent agents within a system as a level of analysis worth considering. This dyadic event becomes the beginning of the overall process that leads toward emergence; thus, it should be considered as a means of better identifying and representing this process. The current article defines interactions as “the network of linkages across which information flows and connects” [14]. While the rules of engagement among individual agents in a system are critical factors of emergence in that individuals act to form these interactions, the individual and the interaction are considered two separate levels of analysis. The CATS model presents a theoretical model that provides an approach to understanding and guiding CAS. This theoretical model concentrates on the outcome that results from these interactions moving toward emergence rather than on the rules-based approach trying to understand the rules of engagement that led to these interactions.
This interaction level is believed to be the driving factor that fosters emergence that takes place in, and spans across, all levels, rather than the levels driving interaction. This interaction level is where leadership should focus a large portion of their efforts toward when operating in CAS. Concerning CAS, and more importantly to the CATS model, interactions that begin at the individual level within the TELDE model hold the potential to emerge into larger organizational, and even global, patterns.
As identified, it is the interaction between the agents of CATS that result in individual learning, the formation of new cognitive structures that contribute to emergent properties. Also, interactions among CATS produce much needed emergent properties; organizational learning and learning organization properties that allow organizations to better address wicked problems and to operate in today’s complex globalized environment. In viewing the level of analysis as the interaction, we identified four different CATS interaction levels: one to one/dyad, dyad to many/team, team to team/organization, and organization to network/industry. Figure 2 identifies each of these interaction levels.
Reciprocal Interactions at Various Organizational Levels.
Each of these four interaction levels consist of variations on micro- and macro-level perspectives. Micro-level represents the lower or smaller entity when compared to a higher or larger, macro entity. These micro- and macro-levels are utilized when representing multilevel models or theories. When a higher level affects a lower level, for example when new governmental regulations affect organizational policies, this process is identified as being top-down. Likewise, when a lower level affects a higher level as in poor employee engagement affecting organizational performance, this is identified as being a bottom-up process. Kozlowski and Klein [41] identified top-down processes as macro-levels exerting influence over micro-levels. Alternatively, bottom-up processes were defined as higher emergent properties that originated at lower levels [41]. In sum, top-down processes provide influence (e.g., mission statement, vision) while bottom-up processes have the potential of producing emergent properties. Because emergent properties come from bottom-up processes, and these processes are driven by the interactions among the agents involved, the focal point when addressing interactions as the level of analysis should be at bottom-up processes. However, even though the interactions and emergence come from bottom-up processes, the influence from the macro-level onto the micro-level (top-down) should not be disregarded. Both the bottom-up and the top-down processes should be considered in totality. This is depicted in Figure 2 by the arrows, an arrow from a micro-level to a macro-level represent emergent, bottom-up, processes. Likewise, an arrow from a macro-level to a micro-level represents influential, top-down, processes.
These levels of interaction are similar to the enabling functions identified by Uhl-Bien and Marion [15]. Their enabling functions began at the micro level (individual level) and aggregate into macro levels which, in turn, also affect the messo level. Figure 3 provides a representation of how these different interactions would take place within a single organization, inter-organizationally. In Figure 3, interactions take place at the individual level within each TELDE model, intra-team. Also, with multiple TELDE models operating sequentially (the CATS model) interactions take place across each team, inter-team to represent the macro-level.
CATS Model: Multiple TELDE Models Acting Inter-Organizationally.
In the inter-organizational model (Figure 3), each system (TELDE model) has peripheral influence over other, adjacent, systems. Here modularity is present as identified by [36].
The aggregate of the micro- and macro-level interactions, along with replication of the CATS model in additional organizations or entities, represents the messo-level interactions. These messo-level interactions are best represented in Figure 4 in which the CATS model is replicated, resulting in interactions inter-organizationally or across different networks (messo-level interactions). He et al. [42] provided one example of this when the researchers looked at how industrial clusters (CAS) formed, they formed through the interactions of the micro-organizations: “clusters form from micro-interactions and spontaneously evolve over time without any intervention”. These micro-interactions emerged across the micro-organizations and eventually influenced the formation of industrial clusters. In essence it is the initial dynamics that evolve into an organization’s adaptability [15], this set of interactive dynamics should be facilitated, not managed, by utilizing the CATS model.
CATS Model Intra-Organizationally or Across Networks (Globally).
The best way to manage CATS is to promote and flourish the interactions that take place within and between TELDE systems. Luoma [43] stressed that managers must be capable of leading in times of rapid change. Managements’ role within complex systems expands beyond traditional human relation functions to one that manages systems and networks [44]. Facilitating these interactions and acting as a change agent within such systems is another function of management. However, this function can only guide emergent processes and cannot control it due to its non-linear and non-predictable nature. A successful agent succeeds by triggering change to meet its own systemic needs [33]. Bovaird [33] identified this process as a self-reinforcing spiral, operating similar to how the knowledge management literature [45, 46, 47] described knowledge creating spirals and how He et al. [42] described knowledge spillover. Even if attempts to manage the emergence is taken, causal mechanisms remain unknown due to the complexity and number of interactions that cannot be accounted for. This is similar to punctuated equilibrium in which some causes are weakly associated to certain effects, but not all effects have associated or knowable causes [33].
Organizations and systems are unable to remain in a state of equilibrium, successful organizations and systems avoid equilibrium [33] in favor of operating on the edge of chaos. Organizations should, due to this run toward chaos, build structures and systems that expedite self-transformation and create conditions for change that lead to self-organizing systems [33]. Granted, however, even though it may seem that self-organizing systems come free to organizations in which they do not need to be managed, this thinking is counter-intuitive. Lindberg and Schneider [20] identified that order is not free when talking about self-organizing systems. Instead, leadership must be able to achieve the right balance between equilibrium and chaos without hampering the emergent processes that come from self-organizing systems [20]. Luoma [43] warned against efforts that try to eliminate this disorder, by eliminating this run toward chaos leaders can destroy the system’s self-organizing capabilities. Here leadership plays a critical role in the self-organizing function [20], facilitating interactions with the system [48].
Organizations need to view leadership as an emerging construct that facilitates self-organizing behavior to achieve desirable outcomes [20]. Leadership also needs to take a more integrated approach that accounts for both inter-system (e.g., inter-team, inter-organization, inter-network) and intra-system (e.g., intra-team, intra-organization, intra-network) dynamics [49]. Tong and Arvey [44] identified three managerial behaviors for leading in complexity; enabling, sensmaking, and facilitating shared leadership. Enabling behaviors allow leaders to enable adaptive outcomes rather than control them, sensmaking relates to a leader’s ability to identify what information is important and where a team’s attention needs to be focused, and facilitating shared leadership involves collective leadership compared to one overarching leader [44]. The CATS model enables members to adapt as needed, allowing the system to emerge in response to internal and external environmental forces. Sensemaking results in the collection of individual accounts [44], which is inherent in the CATS model. The CATS model provides the collective leadership needed in that it views the team as the leader in the TELDE model [6].
This transformation to a new leadership is in alignment with the Law of Requisite Complexity [14, 50]: “It takes complexity to defeat complexity—a system must possess complexity equal to that of its environment in order to function effectively” [14]. He et al. [42] explained that complexity requires a great deal of abstraction to predict general patterns of change. The CATS model aids this new leadership in facilitating and managing this complexity.
Antonacopoulou and Chiva [17] identified social complexity to highlight the need for learning in OL processes. They highlighted learning as being central to complexity because learning highlights the conditions of, and the outcomes from, interactions that fosters self-organizing activities which lead to emergence. Mowles [26] introduced this process as learning through complexity. Boal and Schultz [11] described learning in complex systems as being related to information flow within these social systems, driven by the interaction patterns of the agents within the system and the interaction patterns between systems. Learning is a process that cannot be controlled when identified as a dynamic and complex process [17]. Learning in complex systems is a product of the connections and interactions of the individual agents that result, or contribute to, emergence. At the more macro level, learning is a product of the connections through interactions across systems, such is the case in OL practices when learning occurs in cross-functional groups. This learning through systemic interactions is an area that needs to be further developed and researched through different organizational settings to determine if fostering interactions at various levels result in emergence, thus mediating OL/LO or organizational performance.
Organizational change is often delayed due to four primary processes: structural, institutional, political, and learning processes [51]. The CATS model provides a model that potentially addresses all four delayed processes. The CATS model provides a structural model (structure) for driving organizational change as a bottom-up, self-organizing, process (learning) while achieving organizational and stakeholder objectives (institutional) in response to any environmental forces from the community, the government, or due to globalization (political).
Organizational change has been identified as occurring in cascades where change leads to additional change which, in turn, leads to even more change [51]. Here cascades occur within each TELDE model, ultimately resulting in organizational change through the aggregate from the CATS model.
At the network level, as shown in Figure 4, community ecology looks at the interdependence among differing organizations in which an organization’s legitimacy is related to its similarity and proximity to already legitimate organizations [52]. This network model, supported by the CATS model, provides a platform for collectives to structure similar cognitive spaces. This network model is representative of organizational interdependence models in that it provides interdependence between systems while also providing a proximate association. Organizational interdependence occurs at all levels of analysis; networks, populations, communities, global [52]. In addition to providing a model for organizational interdependence, identifying the interaction as the level of analysis for these network systems aid organizations, communities, and governments with a new architecture to facilitate global change. Further research is needed to test the CATS model to determine its impact on organizational change and organizational interdependence.
Learning within dynamic and complex systems, agents have the capability of being emergent and transformative [53], similar to the concept of transference in Turner and Baker’s [6] TELDE model. Through observation, practice, feedback, and reflection individual agents learn individually as well as collectively. This process was described by Boal and Schultz [11] as shared schemas where interactions lead to the development of similar cognitive structures or schemas. Likewise, Borzillo and Kaminska-Labbe [10] contrasted individual intelligence (interconnected neurons) with collective corporate intelligence (interconnectedness among agents). Turner et al. [54] identified similar team or group cognition models that explain how information is structured and processed collectively: shared mental models, SMM [55]; team mental models, TMM [56, 57]; information sharing, IS [58] transitive memory systems, TMS [59]; cognitive consensus, [60]; and group learning, [61, 62]. Using complexity science to understand corporate entrepreneurship strategy, Crawford and Kreiser [21] identified two organizational antecedents: “the cognitions of the individuals within the focal firm and the firm’s external environmental conditions” (emphasis in original). The shared cognition among the agents within the CATS aids in their capability of becoming more adaptable. At this point, learning becomes collective and emergence begins to develop. Further research is needed to determine the impact that the CATS model might have on shared cognition in teams and small groups, as well as assessing its influence on small networks.
Chiva et al. [30] associated OL with competitive issues such as innovation. They identified innovation as involving new organizational processes along with more traditional concepts: new products, new services, and new knowledge [30]. Innovation is presented as being a collective construct, requiring the organization to learn and to develop new knowledge for the innovative product or process while, at the same time, learn from the innovative processes through feedback channels [30]. This reciprocal process includes both bottom-up and top-down processes at the same time. The CATS model needs to be tested to determine its impact on organizational innovation. The CATS model is one tool that could be utilized to manage innovative processes within organizations, providing self-organizing systems to be innovative (bottom-up) while addressing organizational problems (top-down).
Martin and Eisenhardt [40] introduced restructuring as one method for organizations to address changes in the market. One such effort is in cross-business-unit collaborations. Unfortunately, there is a lack of theoretical models and research addressing “how executives create high-performing cross-BU collaborations” [40]. Their research showed that executive decision-making was effective in multi-business settings when executives were part of a multibusiness team, acting collectively while consensually agreeing to decisions. These multibusiness teams act in a manner that is consistent with the TELDE model which could foster future research efforts. When these multibusiness teams operate across different businesses or industries, they act similarly to the CATS model. The CATS theory adds to the multibusiness organization literature by including a model that incorporates complexity theory and complex adaptive systems. Thus, making a fundamental contribution and meeting the requirements of a new theory [40].
The Columbia response effort began as “idiosyncratic local organizing actions” [29] among the participating agencies (i.e., NASA, FEMA, DOD, EPA). In order to respond quickly and to organize efforts between the multiple agencies that became involved, Beck and Plowman [29] identified four main categories that led to the successful collaborative efforts that came from the initial chaos:
Initial contextual conditions precipitated the collaborative effort.
The organizing actions taken by independent agencies.
The development of trust.
The development of a collective identity.
Success from the Columbia response effort resulted, not from any one agency being in charge, but from the in-charge agency (FEMA) acting as an enabler for the other agencies [29]. Their case study exemplified the support function from the host organization as a means for self-organization to take place. This support function included providing guidance, resources, and tools to the team/group as needed so they could complete their tasks. Also, the interactions that took place within the CAS were facilitated by the host organization. By providing the right direction and resources, the team/group could focus more attention on self-organizing activities aimed toward goal attainment, and in some cases, emergence. The agents involved in the Columbia response effort practiced an aggregated form of the CATS model in which agents acted interdependently toward one common goal that was facilitated by FEMA.
Complexity theory takes a different perspective when viewing systems. Rather than examining systems using reductionistic methods, complexity takes a connectionist perspective in understanding that emerging properties arise from the interactions among and between the system’s elements. As systems evolve from being complicated to being complex, typically by increases in the number of components and interactions within a system, CAS are formed requiring leadership to be distributed. Linearity is often associated with models that provide predictability and causal relationships [43] while CAS are associated with non-linearity, open systems, and non-predictablility. One method of facilitating and managing CAS is through the implementation of Turner and Baker’s [6] TELDE model. A CAS that utilizes the TELDE model as a means of driving change is known as the CATS model.
Micro-level activity and interactions aggregate, and eventually reflect higher-level activities [21]. This results in organizational outcomes being the result of micro-level adaptive and emergent forces through CAS and CATS. Crawford and Kreiser [21] explained: “Unless a new activity pattern emerges or is imposed by top-down tensions, the higher level aggregate activity will exactly [emphasis added] reflect and resemble the scaling pattern of the micro-level pattern”. When viewing CAS, Boal and Schultz [11] stressed that leaders must create the structures and interactions that occur in CAS, allowing them to self-organize and emerge, as in the CATS model presented in this chapter. It was also stressed that strategic leaders should be the catalyst for adaptive systems [11]. Uhl-Bien et al. [14] presented the concept of enabling leadership for their complex leadership theory in which leadership should concentrate their efforts to foster interactions and interdependency while injecting adaptive tension. Campbell-Hunt [19] acknowledged that leaders should be a participant in the flow of events that take place in CAS/CATS as opposed to just trying to control the flow of events. Leaders should make available organizational resources while releasing control of the CAS/CATS in order to allow the system to self-organize and emerge into a new order [19]. This active participation on the part of leadership was identified by Campbell-Hunt [19] as being “an epistemology of engagement with the challenge of an unknowable emergent order” and is presented here in the CATS model.
Chiva et al. [30] presented innovation as introducing either new products, processes, markets, or organizational innovations. Organizational innovation involves incorporating new organizational methods, such as in implementing the TELDE model as the foundation of building CATS to drive organizational initiatives such as leadership development, new employee orientation, change initiatives, diversity training, organizational culture exercises, and new technology orientation, to only name a few. Today’s new leadership is best identified as being capable of influencing systems [12]. This influence comes, in part, through leaders’ managing the interactions between teams and agents as depicted in the CATS model. Leaders’ focusing on these interactions result in building connections and connecting agents, providing a new direction for leaders in today’s complexity: “What might get lost in leadership in the flow of practice is the basic connection (relationships) between the organizational agents” [4]. Utilizing and implementing CATS as standard practice to drive knowledge creation and innovation, and in making new connections within organizations, is one tool that is available for today’s leaders to operate in today’s complex and challenging environment.
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