Genomic Regulatory Networks, Reduction Mappings and Control

All high-level living organisms are made of small cell units, containing DNA, RNA, genes, proteins etc. Genes are important components of the cells and it is necessary to understand the inter-gene relations, in order to comprehend, predict and ultimately intervene in the cells’ dynamics. Genetic regulatory networks (GRN) represent the gene interactions that dictate the cell behavior. Translational genomics aims to mathematically model GRNs and one of the main goals is to alter the networks’ behavior away from undesirable phenotypes such as cancer. The mathematical framework that has been often used for modeling GRNs is the probabilistic Boolean network (PBN), which is a collection of constituent Boolean networks with perturbation, BNp. This dissertation uses BNps, to model gene regulatory networks with an intent of designing stationary control policies (CP) for the networks to shift their dynamics toward more desirable states. Markov Chains (MC) are used to represent the PBNs and stochastic control has been employed to find stationary control policies to affect steady-state distribution of the MC. However, as the number of genes increases, it becomes computationally burdensome, or even infeasible, to derive optimal or greedy intervention policies. This dissertation considers the problem of modeling and intervening in large GRNs. To overcome the computational challenges associated with large networks, two approaches are proposed: first, a reduction mapping that deletes genes from the network; and second, a greedy control policy that can be directly designed on large networks. Simulation results show that these methods achieve the goal of controlling large networks by shifting the steady-state distribution of the networks toward more desirable states. Furthermore, a new inference method is used to derive a large 17-gene Boolean network from microarray experiments on gastrointestinal cancer samples. The new algorithm has similarities to a previously developed well-known inference method, which uses seed genes to grow subnetworks, out of a large network; however, it has major differences with that algorithm. Most importantly, the objective of the new algorithm is to infer a network from a seed gene with an intention to derive the Gene Activity Profile toward more desirable phenotypes. The newly introduced reduction mappings approach is used to delete genes from the 17-gene GRN and when the network is small enough, an intervention policy is designed for the reduced network and induced back to the original network. In another experiment, the greedy control policy approach is used to directly design an intervention policy on the large 17-gene network to beneficially change the long-run behavior of the network. Finally, a novel algorithm is developed for selecting only non-isomorphic BNs, while generating synthetic networks, using a method that generates synthetic BNs, with a prescribed set of attractors. The goal of the new method described in this dissertation is to discard isomorphic networks

On the Interplay between the Evolvability and Network Robustness in an Evolutionary Biological Network: A Systems Biology Approach

In the evolutionary process, the random transmission and mutation of genes provide biological diversities for natural selection. In order to preserve functional phenotypes between generations, gene networks need to evolve robustly under the influence of random perturbations. Therefore, the robustness of the phenotype, in the evolutionary process, exerts a selection force on gene networks to keep network functions. However, gene networks need to adjust, by variations in genetic content, to generate phenotypes for new challenges in the network’s evolution, ie, the evolvability. Hence, there should be some interplay between the evolvability and network robustness in evolutionary gene networks. In this study, the interplay between the evolvability and network robustness of a gene network and a biochemical network is discussed from a nonlinear stochastic system point of view. It was found that if the genetic robustness plus environmental robustness is less than the network robustness, the phenotype of the biological network is robust in evolution. The tradeoff between the genetic robustness and environmental robustness in evolution is discussed from the stochastic stability robustness and sensitivity of the nonlinear stochastic biological network, which may be relevant to the statistical tradeoff between bias and variance, the so-called bias/variance dilemma. Further, the tradeoff could be considered as an antagonistic pleiotropic action of a gene network and discussed from the systems biology perspective

Reverse engineering of biological signaling networks via integration of data and knowledge using probabilistic graphical models

Motivation The postulate that biological molecules rather act together in intricate networks, pioneered systems biology and popularized the study on approaches to reconstruct and understand these networks. These networks give an insight of the underlying biological process and diseases involving aberration in these pathways like, cancer and neuro degenerative diseases. These networks can be reconstructed by two different approaches namely, data driven and knowledge driven methods. This leaves a critical question of relying on either of them. Relying completely on data driven approaches brings in the issue of overfitting, whereas, an entirely knowledge driven approach leaves us without acquisition of any new information/knowledge. This thesis presents hybrid approach in terms of integration of high throughput data and biological knowledge to reverse-engineer the structure of biological networks in a probabilistic way and showcases the improvement brought about as a result. Accomplishments The current work aims to learn networks from perturbation data. It extends the existing Nested Effects Model (NEMs) for pathway reconstruction in order to use the time course data, allowing the differentiation between direct and indirect effects and resolve feedback loops. The thesis also introduces an approach to learn the signaling network from phenotype data in form of images/movie, widening the scope of NEMs, which was so far limited to gene expression data. Furthermore, the thesis introduces methodologies to integrate knoowledge from different existing sources as probabilistic prior that improved the reconstruction accuracy of the network and could make it biologically more rational. These methods were finally integrated and for reverse engineering of more accurate and realistic networks. Conclusion The thesis added three dimensions to existing scope of network reverse engineering specially Nested Effects Models in terms of use of time course data, phenotype data and finally the incorporation of prior biological knowledge from multiple sources. The approaches developed demonstrate their application to understand signaling in stem cells and cell division and breast cancer. Furthermore the integrative approach shows the reconstruction of AMPK/EGFR pathway that is used to identify potential drug targets in lung cancer which were also validated experimentally, meeting one of the desired goals in systems biology

Modelling the role of polarity and geometry in cell-fate dynamics of mammary organoids

Mammary organoids are three-dimensional structures that are derived from mammary gland cells and can recapitulate the complex architecture and functionality of the mammary gland in vitro. Mammary organoids hold great promise for advancing our understanding of mammary gland biology, breast cancer, and precision medicine. However, phenotypic and genetic instabilities observed in long-term expansion limit their applications to prolonged experiments and large-scale production. A proposed factor driving this organoid-wise heterogeneity is plasticity within mammary epithelial cells, the phenotypic switching of cells. Therefore, we examine the dynamics of key intracellular pathways that govern cell-fate commitment in mammary organoids. Specifically, we explore the influence of local tissue geometry and polarity in cell-cell signalling in stabilising cell-fate determinants using a combination of analytic and numerical multiscale modelling approaches. We introduce interconnected dynamical systems, graph-coupled dynamical systems with input-output representations to describe intercellular signal flow between cells. Exploiting structural properties of the bilayer graphs describing mammary tissue architecture, we derive low-dimensional forms of these models enabling the analytic examination of the interplay of structure and polarity on cell-fate patterning, extending existing methods to include pathway crosstalk and providing rigorous links between low-dimensional and their associated large-scale representations. Supporting the analytic investigations of static spatial domains with cellbased modelling, we provide evidence that sufficiently strong cell-cell signal polarity has the capacity to generate and sustain bilayer laminar patterns of Notch1, a critical cell-fate determinant and inducer of plasticity in mammary epithelial cells. Furthermore, we demonstrate how local tissue curvature can relax the constraints of polarity supporting healthy tissue growth and supporting branching morphologies. Fundamentally, this study highlights the significance of cell signalling polarity as a control mechanism of cell-fate commitment. Thus, the establishment and maintenance of epithelial polarity should be considered in long-term mammary organoid expansion protocol development

Recent Developments in Cancer Systems Biology

This ebook includes original research articles and reviews to update readers on the state of the art systems approach to not only discover novel diagnostic and prognostic biomarkers for several cancer types, but also evaluate methodologies to map out important genomic signatures. In addition, therapeutic targets and drug repurposing have been emphasized for a variety of cancer types. In particular, new and established researchers who desire to learn about cancer systems biology and why it is possibly the leading front to a personalized medicine approach will enjoy reading this book

Dynamics of Macrosystems; Proceedings of a Workshop, September 3-7, 1984

There is an increasing awareness of the important and persuasive role that instability and random, chaotic motion play in the dynamics of macrosystems. Further research in the field should aim at providing useful tools, and therefore the motivation should come from important questions arising in specific macrosystems. Such systems include biochemical networks, genetic mechanisms, biological communities, neutral networks, cognitive processes and economic structures. This list may seem heterogeneous, but there are similarities between evolution in the different fields. It is not surprising that mathematical methods devised in one field can also be used to describe the dynamics of another. IIASA is attempting to make progress in this direction. With this aim in view this workshop was held at Laxenburg over the period 3-7 September 1984. These Proceedings cover a broad canvas, ranging from specific biological and economic problems to general aspects of dynamical systems and evolutionary theory

Complex event types for agent-based simulation

This thesis presents a novel formal modelling language, complex event types (CETs), to describe behaviours in agent-based simulations. CETs are able to describe behaviours at any computationally represented level of abstraction. Behaviours can be specified both in terms of the state transition rules of the agent-based model that generate them and in terms of the state transition structures themselves. Based on CETs, novel computational statistical methods are introduced which allow statistical dependencies between behaviours at different levels to be established. Different dependencies formalise different probabilistic causal relations and Complex Systems constructs such as ‘emergence’ and ‘autopoiesis’. Explicit links are also made between the different types of CET inter-dependency and the theoretical assumptions they represent. With the novel computational statistical methods, three categories of model can be validated and discovered: (i) inter-level models, which define probabilistic dependencies between behaviours at different levels; (ii) multi-level models, which define the set of simulations for which an inter-level model holds; (iii) inferred predictive models, which define latent relationships between behaviours at different levels. The CET modelling language and computational statistical methods are then applied to a novel agent-based model of Colonic Cancer to demonstrate their applicability to Complex Systems sciences such as Systems Biology. This proof of principle model provides a framework for further development of a detailed integrative model of the system, which can progressively incorporate biological data from different levels and scales as these become available