Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Targeting the PI3K p110alpha isoform inhibits medulloblastoma proliferation, chemoresistance, and migration.

PURPOSE: The phosphoinositide 3-kinase (PI3K)/Akt pathway is frequently activated in human cancer and plays a crucial role in medulloblastoma biology. We were interested in gaining further insight into the potential of targeting PI3K/Akt signaling as a novel antiproliferative approach in medulloblastoma. EXPERIMENTAL DESIGN: The expression pattern and functions of class I(A) PI3K isoforms were investigated in medulloblastoma tumour samples and cell lines. Effects on cell survival and downstream signaling were analyzed following down-regulation of p110alpha, p110beta, or p110delta by means of RNA interference or inhibition with isoform-specific PI3K inhibitors. RESULTS: Overexpression of the catalytic p110alpha isoform was detected in a panel of primary medulloblastoma samples and cell lines compared with normal brain tissue. Down-regulation of p110alpha expression by RNA interference impaired the growth of medulloblastoma cells, induced apoptosis, and led to decreased migratory capacity of the cells. This effect was selective, because RNA interference targeting of p110beta or p110delta did not result in a comparable impairment of DAOY cell survival. Isoform-specific p110alpha inhibitors also impaired medulloblastoma cell proliferation and sensitized the cells to chemotherapy. Medulloblastoma cells treated with p110alpha inhibitors further displayed reduced activation of Akt and the ribosomal protein S6 kinase in response to stimulation with hepatocyte growth factor and insulin-like growth factor-I. CONCLUSIONS: Together, our data reveal a novel function of p110alpha in medulloblastoma growth and survival.

Targeting PI3KC2β Impairs Proliferation and Survival in Acute Leukemia, Brain Tumours and Neuroendocrine Tumours.

BACKGROUND: Eight human catalytic phosphoinositide 3-kinase (PI3K) isoforms exist which are subdivided into three classes. While class I isoforms have been well-studied in cancer, little is known about the functions of class II PI3Ks. MATERIALS AND METHODS: The expression pattern and functions of the class II PI3KC2β isoform were investigated in a panel of tumour samples and cell lines. RESULTS: Overexpression of PI3KC2β was found in subsets of tumours and cell lines from acute myeloid leukemia (AML), glioblastoma multiforme (GBM), medulloblastoma (MB), neuroblastoma (NB), and small cell lung cancer (SCLC). Specific pharmacological inhibitors of PI3KC2β or RNA interference impaired proliferation of a panel of human cancer cell lines and primary cultures. Inhibition of PI3KC2β also induced apoptosis and sensitised the cancer cells to chemotherapeutic agents. CONCLUSION: Together, these data show that PI3KC2β contributes to proliferation and survival in AML, brain tumours and neuroendocrine tumours, and may represent a novel target in these malignancies.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.

Revisiting the specificity of the MHC class II transactivator CIITA in vivo.

CIITA is a master regulatory factor for the expression of MHC class II (MHC-II) and accessory genes involved in Ag presentation. It has recently been suggested that CIITA also regulates numerous other genes having diverse functions within and outside the immune system. To determine whether these genes are indeed relevant targets of CIITA in vivo, we studied their expression in CIITA-transgenic and CIITA-deficient mice. In contrast to the decisive control of MHC-II and related genes by CIITA, nine putative non-MHC target genes (Eif3s2, Kpna6, Tap1, Yars, Col1a2, Ctse, Ptprr, Tnfsf6 and Plxna1) were found to be CIITA independent in all cell types examined. Two other target genes, encoding IL-4 and IFN-gamma, were indeed found to be up- and down-regulated, respectively, in CIITA-transgenic CD4(+) T cells. However, there was no correlation between MHC-II expression and this Th2 bias at the level of individual transgenic T cells, indicating an indirect control by CIITA. These results show that MHC-II-restricted Ag presentation, and its indirect influences on T cells, remains the only pathway under direct control by CIITA in vivo. They also imply that precisely regulated MHC-II expression is essential for maintaining a proper Th1-Th2 balance.

Education of Murine NK Cells Requires Both cis and trans Recognition of MHC Class I Molecules.

Although NK cells use invariant receptors to identify diseased cells, they nevertheless adapt to their environment, including the presence of certain MHC class I (MHC-I) molecules. This NK cell education, which is mediated by inhibitory receptors specific for MHC-I molecules, changes the responsiveness of activating NK cell receptors (licensing) and modifies the repertoire of MHC-I receptors used by NK cells. The fact that certain MHC-I receptors have the unusual capacity to recognize MHC-I molecules expressed by other cells (trans) and by the NK cell itself (cis) has raised the question regarding possible contributions of the two types of interactions to NK cell education. Although the analysis of an MHC-I receptor variant suggested a role for cis interaction for NK cell licensing, adoptive NK cell transfer experiments supported a key role for trans recognition. To reconcile some of these findings, we have analyzed the impact of cell type-specific deletion of an MHC-I molecule and of a novel MHC-I receptor variant on the education of murine NK cells when these mature under steady-state conditions in vivo. We find that MHC-I expression by NK cells (cis) and by T cells (trans), and MHC-I recognition in cis and in trans, are both needed for NK cell licensing. Unexpectedly, modifications of the MHC-I receptor repertoire are chiefly dependent on cis binding, which provides additional support for an essential role for this unconventional type of interaction for NK cell education. These data suggest that two separate functions of MHC-I receptors are needed to adapt NK cells to self-MHC-I.

IL-6 activated integrated BATF/IRF4 functions in lymphocytes are T-bet-independent and reversed by subcutaneous immunotherapy.

IL-6 plays a central role in supporting pathological TH2 and TH17 cell development and inhibiting the protective T regulatory cells in allergic asthma. TH17 cells have been demonstrated to regulate allergic asthma in general and T-bet-deficiency-induced asthma in particular. Here we found an inverse correlation between T-bet and Il-6 mRNA expression in asthmatic children. Moreover, experimental subcutaneous immunotherapy (SIT) in T-bet((-/-)) mice inhibited IL-6, IL-21R and lung TH17 cells in a setting of asthma. Finally, local delivery of an anti-IL-6R antibody in T-bet((-/-)) mice resulted in the resolution of this allergic trait. Noteworthy, BATF, crucial for the immunoglobulin-class-switch and TH2,TH17 development, was found down-regulated in the lungs of T-bet((-/-)) mice after SIT and after treatment with anti-IL-6R antibody, indicating a critical role of IL-6 in controlling BATF/IRF4 integrated functions in TH2, TH17 cells and B cells also in a T-bet independent fashion in allergic asthma.

Adaptive divergence of ancient gene duplicates in the avian MHC class II beta.

Gene duplication and neofunctionalization are known to be important processes in the evolution of phenotypic complexity. They account for important evolutionary novelties that confer ecological adaptation, such as the major histocompatibility complex (MHC), a multigene family crucial to the vertebrate immune system. In birds, two MHC class II β (MHCIIβ) exon 3 lineages have been recently characterized, and two hypotheses for the evolutionary history of MHCIIβ lineages were proposed. These lineages could have arisen either by 1) an ancient duplication and subsequent divergence of one paralog or by 2) recent parallel duplications followed by functional convergence. Here, we compiled a data set consisting of 63 MHCIIβ exon 3 sequences from six avian orders to distinguish between these hypotheses and to understand the role of selection in the divergent evolution of the two avian MHCIIβ lineages. Based on phylogenetic reconstructions and simulations, we show that a unique duplication event preceding the major avian radiations gave rise to two ancestral MHCIIβ lineages that were each likely lost once later during avian evolution. Maximum likelihood estimation shows that following the ancestral duplication, positive selection drove a radical shift from basic to acidic amino acid composition of a protein domain facing the α-chain in the MHCII α β-heterodimer. Structural analyses of the MHCII α β-heterodimer highlight that three of these residues are potentially involved in direct interactions with the α-chain, suggesting that the shift following duplication may have been accompanied by coevolution of the interacting α- and β-chains. These results provide new insights into the long-term evolutionary relationships among avian MHC genes and open interesting perspectives for comparative and population genomic studies of avian MHC evolution.

Decreased binding of peptides-MHC class I (pMHC) multimeric complexes to CD8 affects their binding avidity for the TCR but does not significantly impact on pMHC/TCR dissociation rate.

The CD8 coreceptor plays a crucial role in both T cell development in the thymus and in the activation of mature T cells in response to Ag-specific stimulation. In this study we used soluble peptides-MHC class I (pMHC) multimeric complexes bearing mutations in the CD8 binding site that impair their binding to the MHC, together with altered peptide ligands, to assess the impact of CD8 on pMHC binding to the TCR. Our data support a model in which CD8 promotes the binding of TCR to pMHC. However, once the pMHC/TCR complex is formed, the TCR dominates the pMHC/TCR dissociation rates. As a consequence of these molecular interactions, under physiologic conditions CD8 plays a key role in complex formation, resulting in the enhancement of CD8 T cell functions whose specificity, however, is determined by the TCR.

A Role for cis Interaction between the Inhibitory Ly49A receptor and MHC class I for natural killer cell education.

Natural killer (NK) cells show enhanced functional competence when they express inhibitory receptors specific for inherited major histocompatibility complex class I (MHC-I) molecules. Current models imply that NK cell education requires an interaction of inhibitory receptors with MHC-I expressed on other cells. However, the inhibitory Ly49A receptor can also bind MHC-I ligand on the NK cell itself (in cis). Here we describe a Ly49A variant, which can engage MHC-I expressed on other cells but not in cis. Even though this variant inhibited NK cell effector function, it failed to educate NK cells. The association with MHC-I in cis sequestered wild-type Ly49A, and this was found to relieve NK cells from a suppressive effect of unengaged Ly49A. These data explain how inhibitory MHC-I receptors can facilitate NK cell activation. They dissociate classical inhibitory from educating functions of Ly49A and suggest that cis interaction of Ly49A is necessary for NK cell education.

Ly49D engagement on T lymphocytes induces TCR-independent activation and CD8 effector functions that control tumor growth.

Recent data showing expression of activating NK receptors (NKR) by conventional T lymphocytes raise the question of their role in the triggering of TCR-independent responses that could be damaging for the host. Transgenic mice expressing the activating receptor Ly49D/DAP12 offer the opportunity to better understand the relevance of ITAM signaling in the biology of T cells. In vitro experiments showed that Ly49D engagement on T lymphocytes by a cognate MHC class I ligand expressed by Chinese hamster ovary (CHO) cells or by specific Ab triggered cellular activation of both CD4 and CD8 populations with modulation of activation markers and cytokine production. The forced expression of the ITAM signaling chain DAP12 is mandatory for Ly49D-transgenic T cell activation. In addition, Ly49D stimulation induced T lymphocyte proliferation, which was much stronger for CD8 T cells. Phenotypic analysis of anti-Ly49D-stimulated CD8 T cells and their ability to produce high levels of IFN-gamma and to kill target cells indicate that Ly49D ligation generates effector cytotoxic CD8 T cells. Ly49D engagement by itself also triggered cytotoxic activity of activated CD8 T cells. Adoptive transfer experiments confirmed that Ly49D-transgenic CD8 T cells are able to control growth of CHO tumor cells or RMA cells transfected with Hm1-C4, the Ly49D ligand normally expressed by CHO. In conclusion, Ly49D engagement on T cells leads to T cell activation and to a full range of TCR-independent effector functions of CD8 T cells.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

Functional analysis of monocyte MHC class II compartments.

Circulating monocytes, as dendritic cell and macrophage precursors, exhibit several functions usually associated with antigen-presenting cells, such as phagocytosis and presence of endosomal/lysosomal degradative compartments particularly enriched in Lamp-1, MHC class II molecules, and other proteins related to antigen processing and MHC class II loading [MHC class II compartments (MIICs)]. Ultrastructural analysis of these organelles indicates that, differently from the multivesicular bodies present in dendritic cells, in monocytes the MIICs are characterized by a single perimetral membrane surrounding an electron-dense core. Analysis of their content reveals enrichment in myeloperoxidase, an enzyme classically associated with azurophilic granules in granulocytes and mast cell secretory lysosomes. Elevation in intracellular free calcium levels in monocytes induced secretion of beta-hexosaminidase, cathepsins, and myeloperoxidase in the extracellular milieu; surface up-regulation of MHC class II molecules; and appearance of lysosomal resident proteins. The Ca(2+)-regulated surface transport mechanism of MHC class II molecules observed in monocytes is different from the tubulovesicular organization of the multivesicular bodies previously reported in dendritic cells and macrophages. Hence, in monocytes, MHC class II-enriched organelles combine degradative functions typical of lysosomes and regulated secretion typical of secretory lysosomes. More important, Ca(2+)-mediated up-regulation of surface MHC class II molecules is accompanied by extracellular release of lysosomal resident enzymes.

Role of major histocompatibility complex class II expression by non-hematopoietic cells in autoimmune and inflammatory disorders: facts and fiction.

It is well established that interactions between CD4(+) T cells and major histocompatibility complex class II (MHCII) positive antigen-presenting cells (APCs) of hematopoietic origin play key roles in both the maintenance of tolerance and the initiation and development of autoimmune and inflammatory disorders. In sharp contrast, despite nearly three decades of intensive research, the functional relevance of MHCII expression by non-hematopoietic tissue-resident cells has remained obscure. The widespread assumption that MHCII expression by non-hematopoietic APCs has an impact on autoimmune and inflammatory diseases has in most instances neither been confirmed nor excluded by indisputable in vivo data. Here we review and put into perspective conflicting in vitro and in vivo results on the putative impact of MHCII expression by non-hematopoietic APCs-in both target organs and secondary lymphoid tissues-on the initiation and development of representative autoimmune and inflammatory disorders. Emphasis will be placed on the lacunar status of our knowledge in this field. We also discuss new mouse models-developed on the basis of our understanding of the molecular mechanisms that regulate MHCII expression-that constitute valuable tools for filling the severe gaps in our knowledge on the functions of non-hematopoietic APCs in inflammatory conditions.

bHLH class transcription factors take centre stage in phytochrome signalling.

The phytochrome family of photoreceptors (there are five phytochromes in Arabidopsis, named phyA to phyE) maximally absorbs red and far-red light and plays important functions throughout the life cycle of plants. Several recent studies have shown that multiple related bHLH (basic helix-loop-helix) class transcription factors play key roles in phytochrome signal transduction. Somewhat surprisingly these transcription factors primarily act as negative regulators of phytochrome signalling. Moreover, in some cases, the phytochromes inhibit those negative regulators.