Boolean Networks as Predictive Models of Emergent Biological Behaviors

Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions (from molecules in gene regulatory networks to species in ecological networks) and the often-incomplete state of system knowledge (e.g., the unknown values of kinetic parameters for biochemical reactions). Boolean networks have emerged as a powerful tool for modeling these systems. We provide a methodological overview of Boolean network models of biological systems. After a brief introduction, we describe the process of building, analyzing, and validating a Boolean model. We then present the use of the model to make predictions about the system's response to perturbations and about how to control (or at least influence) its behavior. We emphasize the interplay between structural and dynamical properties of Boolean networks and illustrate them in three case studies from disparate levels of biological organization.Comment: Review, to appear in the Cambridge Elements serie

Computational analysis on the effects of variations in T and B cells. Primary immunodeficiencies and cancer neoepitopes

Computational approaches are essential to study the effects of inborn and somatic variations. Results from such studies contribute to better diagnosis and therapies. Primary immunodeficiencies (PIDs) are rare inborn defects of key immune response genes. Somatic variations are main drivers of most cancers. Large and diverse data on PID genes and proteins can enable systems biology studies on their dynamic effects on T and B cells. Amino acid substitutions (AASs) are somatic variations that drive cancers. However, AASs also cause cancer-associated antigens that are recognized by lymphocytes as non-self, and are called neoantigens. Detail analysis these neoantigens can be performed due to the availability of cancer data from many consortia.The purpose of this thesis was to investigate the effects of PIDs on T and B cells and to explore features of neoepitopes in cancers. The object of the first study was to detect the central T cell-specific protein network. The purpose of the second and third studies were to reconstruct the T and B cell network model and simulate the dynamic effects of PID perturbations. The aim of the fourth study was to characterize neoepitopes from pan-cancer datasets.The immunome interactome was reconstructed, and the links weighed with gene expression correlation of integrated, time series data (Paper I). The significance of the weighted links were computed with the Global Statistical Significance (GloSS) method, and the weighted interactome network was filtered to obtain the central T cell network. Next, the T cell network model was reconstructed from literature mining and the core T cell protein interaction network (Paper II). The B cell network model was reconstructed by mining the literature for central B cell interactions (Paper III). The normalized HillCube software was used to study the dynamic effects of PID perturbations in T and B cells. Proteome-wide amino AASs on putatively derived 8-, 9-, 10-, and 11-mer neoepitopes in 30 cancer types were analyzed with the NetMHC 4.0 software (Paper IV).The interconnectedness of the major T cell pathways are maintained in the central T cell PPI network. Empirical evidence from Gene Ontology term and essential genes enrichment analyses were in support for the central T cell network. In the T and B cell simulations for several knockout PIDs correspond to previous results. In the T cell model, simulations for TCR, PTPRC, LCK, ZAP70 and ITK indicated profound disruption in network dynamics. BCL10, CARD11, MALT1, NEMO and MAP3K14 simulations showed significant effects. In B cell, the simulations for LYN, BTK, STIM1, ORAI1, CD19, CD21 and CD81 indicated profound changes to many proteins in the network. Severe effects were observed in the BCL10, IKKB, knockout CARD11, MALT1, NEMO, IKKB and WIPF1 simulations. No major effects were observed for constitutively active PID proteins. The most likely epitopes are those which are detected by several macromolecular histocompartibility complexes (MHCs) and of several peptide lengths. 0.17% of all variants yield more than 100 neoepitopes. Amino acid distributions indicate that variants at all positions in neoepitopes of any length are, on average, more hydrophobic compared to the wild-type.The core T cell network approach is general and applicable to any system with adequate data. The T and B cell models enable the understanding of the dynamic effects of PID disease processes and reveals several novel proteins that may be of interest when diagnosing and treating immunological defects. The neoepitope characteristics can be employed for targeted cancer vaccine applications in personalized therapies

Mechanisms of cell diversification

Cell identity and function is determined by the intrinsic wiring of the gene regulatory network that endows progenitors with the competence to respond appropriately to extrinsic cues in a spatiotemporally-dependent manner. One such class of cues, morphogens, instruct cells in their identity by virtue of a concentration gradient, but how this is interpreted at gene regulatory levels to result in sharp and robust boundaries of gene expression is poorly understood. The patterning of the dorsoventral (DV) axis of the developing vertebrate nervous system by Sonic hedgehog (Shh) and its bifunctional transcriptional mediator, Gli, results in the specification of distinct neural subtypes and serves as a model of morphogen function. The identification and functional analysis is described of cis-regulatory modules (CRMs) required for the neural-specific interpretation of morphogen activity by genes that pattern the dorsoventral axis of the CNS and coordinately specify progenitor subtype identity. The results presented are consistent with a model in which morphogen exposure is interpreted via distinct transcriptional mechanisms by genes induced close to the morphogen source as compared to those induced at long-range. In particular, long-range genes directly interpret the Gli repressor (GliR) gradient, resulting in target gene derepression in response to Shh. As a result, expression of long-range targets is critically reliant on additional activators that act in synergy with Gli activators (GliA) as well as direct repressive input from other TFs that restrict expression to the ventral neural tube. By contrast, locally induced Shh target genes directly interpret the balance between GliA and GliR and require input by GliA for their expression. Although synergy with other activators is required for expression, locally induced genes appear to be largely insensitive to mutations of their Gli-binding sites. Evidence is provided that input from other morphogens that pattern the DV axis as well as from Hox proteins that regulate cell identity along the anteroposterior axis is directly integrated into the same set of Shh-regulated CRMs to modulate the relative sizes of progenitor domains along these axes. The high dependence of local targets on the balance of Gli isoforms to regulate their range of expression obviates the need for other direct repressive input, and, consistent with this, genetic and gain-of-function evidence is presented that Pax6 cell-autonomously suppresses expression of local responses by upregulating Gli3 and, hence, GliR. Conversely, the locally induced Shh target, Nkx2.2, is shown to cell-autonomously amplify the Shh response by downregulating Gli3. Extracanonical feedback modulation by Shh-regulated genes offers a mechanism for the phenomenon of cellular memory that is essential to produce qualitative responses to quantitative input, including previous observations that the highest Shh responses are not immediately accessible, but rather depend on ongoing morphogen exposure. Accordingly, whereas Pax6 suppresses floor plate (FP) differentiation, ectopic expression of Nkx2 proteins at early stages promotes FP differentiation in a Shh-dependent manner, whereas misexpression at later stages specifies p3 identity, and it is suggested that the loss of this ability reflects a temporal switch of progenitor competence. Shh signaling is transduced through the primary cilium, which is absolutely required for stabilization of GliA and facilitates GliR formation. The differential sensitivity of local and long-range target genes to perturbed Shh signaling is consistent with the phenotypes of mutants that impact cilia morphology but do not prevent ciliogenesis. Mutants of Rfx4, which regulates ciliogenesis, display a selective reduction of the size of locally regulated domains. Surprisingly, this is due not to a delayed induction of local target genes, but rather to a failure to maintain them as Shh signaling declines. This period is characterized by reactivation and extended co-expression of Olig2 and Pax6 in Nkx2.2-expressing progenitors that do not commit to FP fate. It is suggested that this mixed identity corresponds to a metastable cell state that is acutely sensitive to ongoing fluctuations in morphogen exposure and required to generate sharp domain boundaries. Consistent with impaired Shh signaling, Rfx4 mutants fail to extinguish Gli1 expression at the ventral midline, which is correlated with an extension to the ventral midline of the zone of Olig2/Pax6 reactivation and delayed FP commitment. Evidence is presented that the neural-specific response of morphogen target genes is regulated by Soxb1 proteins, which are sufficient to induce these genes in the developing limb in response to Shh, retinoid, or Bmp morphogen exposure. Moreover, the collocation of Soxb1- and Gli-binding sites constitutes a genomic signature that reliably predicts the neural-specific expression of nearby genes

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research

Reduction of dynamical biochemical reaction networks in computational biology

Biochemical networks are used in computational biology, to model the static and dynamical details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multi-scaleness is another property of these networks, that can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler networks, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state and quasi-equilibrium approximations, and provide practical recipes for model reduction of linear and nonlinear networks. We also discuss the application of model reduction to backward pruning machine learning techniques

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u