Bayesian Network Modeling and Inference in Plant Gene Networks And Analysis of Sequencing and Imaging Data

Scientific and technological advancements over the years have made curing, preventing or managing all diseases, a goal that seems to be within reach. The approach to manipulating biological systems is multifaceted. This dissertation focuses on two problems that pose fundamental challenges in developing methods to control biological systems: the first is to model complex interactions in biological systems; the second is faithful representation and analysis of biological data obtained from scientific equipments. The first part of this dissertation is a discussion on modeling and inference in gene networks, and Bayesian inference. Then we describe the application of Bayesian network modeling to represent interactions among genes, and integrating gene expression data in order to identify potential points of intervention in the gene network. We conclude with a summary of evolving directions for modeling gene interactions. The second topic this dissertation focuses on is taming biological data to obtain actionable insights. We introduce the challenges in representation and analysis of high throughput sequencing data and proceeds to describe the analysis of imaging data in the dynamic environment of cancer cells. Then we discuss tackling the problem of analyzing high throughput RNA sequencing data in order to pinpoint genes that exhibit different behaviors under monitored experimental conditions. Then we address the interesting problem of deciphering and quantifying gene-level activity from epifluorescent imaging data

Applications of Probabilistic Graphical Models in Genomic Networks for Agriculture

Agricultural productivity is severely limited by environmental stresses that affect plants. Environmental stresses can be classified as abiotic or biotic. This study focuses on drought and saline stress, the two significant abiotic stresses causing crop loss worldwide. Crop loss due to drought and saline stress are major factors that threaten global food security. This problem is exacerbated by the growing world population, which is expected to rise by 2 billion in the next thirty years. Fortunately, plants have internal mechanisms to defend against environmental stresses. These mechanisms are deployed through complex networks of molecules known as signaling pathways. Environmental stress stimuli can trigger signaling pathways that activate or inhibit downstream genes to implement defensive measures and restore homeostasis. Signaling pathways are not only limited in their capability to defend against stresses but are also responsible for mediating other activities, including protein synthesis, cell death, and differentiation. Thus understanding the signaling pathways in plants is key to developing plants that can defend against environmental stresses and are nutritionally valuable. We studied the drought signaling pathways in Arabidopsis to identify the genetic regulators of drought-responsive genes. Additionally, we examined the lysine biosynthesis pathway in rice under normal and saline stress conditions. Lysine is an essential amino acid present in the lowest quantity compared to all the other amino acids in rice. Amino acids are the building block of proteins and play a crucial role in maintaining the human body’s healthy functioning. Thus, increasing the lysine content in rice will help improve global health. We modeled both the drought signaling and lysine synthesis pathways using Bayesian networks. We chose Bayesian networks as they allow us to integrate pathway information from literature with experimental data. Using Bayesian networks, we identified that ATAF1 is a negative regulator of drought and DAPF is the most potent regulator of lysine. These regulators can be targeted using genetic intervention methods such as CRISPR-CAS9 to make plants robust against drought and increase lysine content in rice. Our work with drought signaling pathways was validated through wet-lab experiments

MATHEMATICAL MODELING AND INFERENCE OF CANCER NETWORKS

Cancer is a group of diseases characterized by abnormal cell growth. Old cells do not die and grow uncontrollably, forming a mass of tissue, called a tumor. In order to understand this abnormal cell growth, there have been various efforts to model the interactions between different molecules and pathways that initiate and drive cell proliferation. In this work, we analyze Bayesian and Boolean techniques that can aid in modeling different cancer networks and infer the drug combinations that can effectively kill tumor cells. Signaling pathways supervise cellular processes such as growth, differentiation, and death. In healthy cells, these processes are tightly regulated, however, in cancerous cells, mutations in crucial genes often lead to irregularities in these processes and eventually cancer. In this work, we study pathways and genes characterizing Breast cancer, Pancreatic cancer, and Lung cancer. We make use of biological literature to construct the pathways and then use mathematical modeling techniques to analyze and rank different therapeutic interventions. We first develop a Bayesian network of Breast cancer and using a messaging passing algorithm, we infer the network and rank drugs according to their ability to induce apoptosis. We then model the signaling network and mutations of Pancreatic cancer using a multi-fault Boolean framework and simulate the network to theoretically assess the efficacy of drug combinations. Finally, we use a modified Boolean approach to mathematically model feedback loops in Lung cancer and determine the drug combinations that produce cell death for the majority of mutations. Our theoretical analyses point out that drug combinations containing Cryptotanshinone, a compound found in traditional Chinese herbs, result in significantly increased cell death in each of Breast, Pancreatic, and Lung cancer pathways. We corroborated our theoretical results with experiments on MCF-7 breast cancer cell lines, Human Pancreatic Cancer (HPAC) cell lines, H2073 and SW900 lung cancer cell lines

Advancing multiple model-based control of complex biological systems: Applications in T cell biology

Activated CD4+ T cells are important regulators of the adaptive immune response against invading pathogens and cancerous host cells. The process of activation is mediated by the T cell receptor and a vast network of intracellular signal transduction pathways, which recognize and interpret antigenic signals to determine the cell\u27s response. The critical role of these early signaling events in normal cell function and the pathogenesis of disease ultimately make them attractive therapeutic targets for numerous autoimmune diseases and cancers. Scientists increasingly rely on predictive mathematical models and control-theoretic tools to design effective strategies to manipulate cellular processes for the advancement of knowledge or therapeutic gain. However, the application of modern control theory to intracellular signal transduction is complicated by a unique set of intrinsic properties and technical limitations. These include complexities in the signaling network such as crosstalk, feedback and nonlinearity, and a dearth of rapid quantitative measurement techniques and specific and orthogonal modulators, the major consequences of which are uncertainty in the model representation and the prevention of real-time measurement feedback. Integrating such uncertainties and limitations into a control-theoretic approach under practical constraints represents an open challenge in controller design. The work presented in this dissertation addresses these challenges through the development of a computational methodology to aid in the design of experimental strategies to predictably manipulate intracellular signaling during the process of CD4+ T cell activation. This work achieves two main objectives: (1) the development of a generalized control-theoretic tool to effectively control uncertain nonlinear systems in the absence of real-time measurement feedback, and (2) the development and calibration of a predictive mathematical model (or collection of models) of CD4+ T cell activation to help derive experimental inputs to robustly force the system dynamics along prescribed trajectories. The crux of this strategy is the use of multiple data-supported models to inform the controller design. These models may represent alternative hypotheses for signaling mechanisms and give rise to distinct network topologies or kinetic rate scenarios and yet remain consistent with available data. Here, a novel adaptive weighting algorithm predicts variations in the models\u27 predictive accuracy over the admissible input space to produce a more reliable compromise solution from multiple competing objectives, a result corroborated by several experimental studies. This dissertation provides a practical means to effectively utilize the collective predictive capacity of multiple prediction models to predictably and robustly direct CD4 + T cells to exhibit regulatory, helper and anergic T cell-like signaling profiles through pharmacological manipulations in the absence of measurement feedback. The framework and procedures developed herein are expected to widely applicable to a more general class of continuous dynamical systems for which real-time feedback is not readily available. Furthermore, the ability to predictably and precisely control biological systems could greatly advance how we study and interrogate such systems and aid in the development of novel therapeutic designs for the treatment of disease

An Investigation Of Gene Networks Influenced By Low Dose Ionizing Radiation Using Statistical And Graph Theoretical Algorithms

Increased application of radiation in health and security sectors has raised concerns about its deleterious effects. Ionizing radiation (IR) less than 10cGys is considered low dose ionizing radiation (LDIR) by the National Research Committee to assess health risks from exposure to low levels of IR. It is hard to extract the effects of mild stimulus such as LDIR on gene expression profiles using simple differential expression. We hypothesized that differential correlation instead would capture the effects of LDIR on mutual relationships between genes. We tested this hypothesis on expression profiles from five inbred strains of mice treated with LDIR. Whereas ANOVA detected little effect of LDIR on gene expression, a differential correlation graph generated by a two stage statistical filter revealed gene networks enriched with genes implicated in radiation response, DNA damage repair, apoptosis, cancer and immune system. To mimic the effects of radiation on human populations, we profiled baseline expression of recombinant inbred strains of BXD mice derived from a cross between C57BL/6J and DBA/2J standard inbred strains. To establish a threshold for extraction of gene networks from the baseline expression profiles, we compared gene enrichment in paracliques obtained at different absolute Pearson correlations (APC) using graph algorithms. Gene networks extracted at statistically significant APC (r≈0.41) exhibited even better enrichment of genes participating in common biological processes than networks extracted at higher APCs from 0.6 to 0.875. Since immune response is influenced by LDIR, we investigated the effects of genetic background on variability of immune system in a population of BXD mice. Considering immune response as a complex trait, we identified significant QTLs explaining the ratio of CD8+ and CD4+ T-cells. Multiple regression modeling of genes neighboring statistically significant QTLs identified three candidate genes (Ptprk,Acp1 and Lamb1-1) explaining 61% variance of ratio of CD4+ and CD8+ T cells. Expression profiling of parental strains of BXD mice also revealed effects of LDIR and LDIR*strain on expression of genes related to immune response. Thus using an integrated approach involving transcriptomic, SNP and immunological data, we have developed novel methods to pinpoint candidate gene networks putatively influenced by LDIR

Host-pathogen interactions between the human innate immune system and Candida albicans—understanding and modeling defense and evasion strategies

The diploid, polymorphic yeast Candida albicans is one of the most important humanpathogenic fungi. C. albicans can grow, proliferate and coexist as a commensal on or within thehuman host for a long time. Alterations in the host environment, however, can render C. albicansvirulent. In this review, we describe the immunological cross-talk between C. albicans and thehuman innate immune system. We give an overview in form of pairs of human defense strategiesincluding immunological mechanisms as well as general stressors such as nutrient limitation,pH, fever etc. and the corresponding fungal response and evasion mechanisms. FurthermoreComputational Systems Biology approaches to model and investigate these complex interactionare highlighted with a special focus on game-theoretical methods and agent-based models. Anoutlook on interesting questions to be tackled by Systems Biology regarding entangled defenseand evasion mechanisms is given

From gene-expressions to pathways

Rapid advancements in experimental techniques have benefited molecular biology in many ways. The experiments once considered impossible due to the lack of resources can now be performed with relative ease in an acceptable time-span; monitoring simultaneous expressions of thousands of genes at a given time point is one of them. Microarray technology is the most popular method in biological sciences to observe the simultaneous expression levels of a large number of genes. The large amount of data produced by a microarray experiment requires considerable computational analysis before some biologically meaningful hypothesis can be drawn. In contrast to a single time-point microarray experiment, the temporal microarray experiments enable us to understand the dynamics of the underlying system. Such information, if properly utilized, can provide vital clues about the structure and functioning of the system under study. This dissertation introduces some new computational techniques to process temporal microarray data. We focus on three broad stages of microarray data analysis - normalization, clustering and inference of gene-regulatory networks. We explain our methods using various synthesized datasets and a real biological dataset, produced in-house, to monitor the leaf senescence process in Arabidopsis thaliana

Nutrigenomics in human peripheral blood mononuclear cells : the effects of fatty acids on gene expression profiles of human circulating cells as assessed in human intervention studies

Research on the effects of nutrition on the function and health of organs in the human body, such as liver and intestine, is difficult, because for this research organ tissue is needed. Since nutrition research is usually performed in healthy volunteers, this tissue is difficult to obtain. However, to find out what happens on cellular level we do need human cells. Because blood cells are transported through the entire body and are relatively easy to obtain, these cells are ideal to study the effect of nutrition on cellular level. For this research we used the latest molecular genomics techniques to study the activity (on/off switching, increase/decrease) of all our genes at once. We found that consumption of different types of fat, both directly after consumption and after continued intake, changed the activity of specific groups of genes in these cells. With this research we have shown that the subtle effects of nutrition can be studied using nutrigenomics techniques in humans by using blood cells

Optimization of logical networks for the modelling of cancer signalling pathways

Cancer is one of the main causes of death throughout the world. The survival of patients diagnosed with various cancer types remains low despite the numerous progresses of the last decades. Some of the reasons for this unmet clinical need are the high heterogeneity between patients, the differentiation of cancer cells within a single tumor, the persistence of cancer stem cells, and the high number of possible clinical phenotypes arising from the combination of the genetic and epigenetic insults that confer to cells the functional characteristics enabling them to proliferate, evade the immune system and programmed cell death, and give rise to neoplasms. To identify new therapeutic options, a better understanding of the mechanisms that generate and maintain these functional characteristics is needed. As many of the alterations that characterize cancerous lesions relate to the signaling pathways that ensure the adequacy of cellular behavior in a specific micro-environment and in response to molecular cues, it is likely that increased knowledge about these signaling pathways will result in the identification of new pharmacological targets towards which new drugs can be designed. As such, the modeling of the cellular regulatory networks can play a prominent role in this understanding, as computational modeling allows the integration of large quantities of data and the simulation of large systems. Logical modeling is well adapted to the large-scale modeling of regulatory networks. Different types of logical network modeling have been used successfully to study cancer signaling pathways and investigate specific hypotheses. In this work we propose a Dynamic Bayesian Network framework to contextualize network models of signaling pathways. We implemented FALCON, a Matlab toolbox to formulate the parametrization of a prior-knowledge interaction network given a set of biological measurements under different experimental conditions. The FALCON toolbox allows a systems-level analysis of the model with the aim of identifying the most sensitive nodes and interactions of the inferred regulatory network and point to possible ways to modify its functional properties. The resulting hypotheses can be tested in the form of virtual knock-out experiments. We also propose a series of regularization schemes, materializing biological assumptions, to incorporate relevant research questions in the optimization procedure. These questions include the detection of the active signaling pathways in a specific context, the identification of the most important differences within a group of cell lines, or the time-frame of network rewiring. We used the toolbox and its extensions on a series of toy models and biological examples. We showed that our pipeline is able to identify cell type-specific parameters that are predictive of drug sensitivity, using a regularization scheme based on local parameter densities in the parameter space. We applied FALCON to the analysis of the resistance mechanism in A375 melanoma cells adapted to low doses of a TNFR agonist, and we accurately predict the re-sensitization and successful induction of apoptosis in the adapted cells via the silencing of XIAP and the down-regulation of NFkB. We further point to specific drug combinations that could be applied in the clinics. Overall, we demonstrate that our approach is able to identify the most relevant changes between sensitive and resistant cancer clones

SYSTEM INVESTIGATION OF LY303511-INDUCED SENSITIZATION OF HELA CELLS TO TRAIL-INDUCED APOPTOSIS: ROLE OF HYDROGEN PEROXIDE, PEROXYNITRITE, NITRIC OXIDE AND CALCIUM

Ph.DDOCTOR OF PHILOSOPH