Bayesian Models for Gene Regulatory Networks Applied to Cancer Tissues

Cellular behavior is controlled through multivariate interactions between various biological molecules such as proteins and DNA. Various methods have previously been proposed to model such interactions. However many of these methods require large volumes of data to effectively estimate the associated unknown parameters. In this work we explore the use of Bayesian methods to exploit the prior knowledge about pathway information in combination with collected data in order to make accurate and useful inferences about tissue level behavior. These predictions would in turn help in the discovery of better therapeutic strategies such as the development of better combination therapies involving kinase inhibiting drugs. Various problems of modeling cancerous and healthy tissues from a Bayesian perspective have been addressed in this work. We give a short description of these problems here in this section. An important problem in the study of cancer is the understanding of the heterogeneous nature of the cell population. The clonal evolution of the tumor cells results in the tumors being composed of multiple sub-populations. Each sub-population reacts differently to any given therapy. This calls for the development of novel (regulatory network) models, which can accommodate heterogeneity in cancerous tissues. Here we present a new approach to model heterogeneity in cancer. We model heterogeneity as an ensemble of deterministic Boolean networks based on prior pathway knowledge. We develop the model considering the use of qPCR data. By observing gene expressions when the tissue is subjected to various stimuli, the compositional breakup of the tissue under study can be determined. We demonstrate the viability of this approach by using our model on synthetic data, and real world data collected from fibroblasts. Another problem which is addressed in this work is the determination of locations of dysregulations in a Boolean network used to model signal transduction networks. Knowledge about which proteins/genes are dysregulated in a regulatory network, such as in the Mitogen Activated Protein Kinase (MAPK) Network, can be used not only to decide upon which therapy to use for a particular case of cancer, but also help in discovering effective targets for new drugs. The posterior inference problem is solved using a version of the message passing algorithm. We have done simulation experiments on synthetic data to verify the efficacy of the algorithm as compared to the results from the much more computationally intensive Markov Chain Monte-Carlo methods. We also applied the model to analyze data collected from fibroblasts, thereby demonstrating how this model can be used on real world data. Another important issue in Bayesian computation is that the processing of the collected data must be done as efficiently as possible in terms of computational speed and memory requirements. The use of Markov Chain Monte Carlo methods is time consuming and hence other methods need to be used for the analysis. The use of conjugate exponential models is investigated in the modeling of the heterogeneity of cancerous tissues where variational methods could be used in a straightforward manner. Variational algorithms, which allow for the fast computations of posterior probability distributions of variables of interest, have been used in the inference of the compositional breakup of the heterogeneous tissue under study. The efficacy of these methods has been demonstrated by comparing them with other methods such as Markov chain Monte Carlo and Expectation maximization

Gene Regulatory Networks: Modeling, Intervention and Context

abstract: Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level.Dissertation/ThesisPh.D. Computer Science 201

Computational approaches to complex biological networks

The need of understanding and modeling the biological networks is one of the raisons d'\ueatre and of the driving forces behind the emergence of Systems Biology. Because of its holistic approach and because of the widely different level of complexity of the networks, different mathematical methods have been developed during the years. Some of these computational methods are used in this thesis in order to investigate various properties of different biological systems. The first part deals with the prediction of the perturbation of cellular metabolism induced by drugs. Using Flux Balance Analysis to describe the reconstructed genome-wide metabolic networks, we consider the problem of identifying the most selective drug synergisms for given therapeutic targets. The second part of this thesis considers gene regulatory and large social networks as signed graphs (activation/deactivation or friendship/hostility are rephrased as positive/negative coupling between spins). Using the analogy with an Ising spin glass an analysis of the energy landscape and of the content of \u201cdisorder\u201d 'is carried out. Finally, the last part concerns the study of the spatial heterogeneity of the signaling pathway of rod photoreceptors. The electrophysiological data produced by our collaborators in the Neurobiology laboratory have been analyzed with various dynamical systems giving an insight into the process of ageing of photoreceptors and into the role diffusion in the pathway

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas

Application of Logic Synthesis Toward the Inference and Control of Gene Regulatory Networks

In the quest to understand cell behavior and cure genetic diseases such as cancer, the fundamental approach being taken is undergoing a gradual change. It is becoming more acceptable to view these diseases as an engineering problem, and systems engineering approaches are being deployed to tackle genetic diseases. In this light, we believe that logic synthesis techniques can play a very important role. Several techniques from the field of logic synthesis can be adapted to assist in the arguably huge effort of modeling cell behavior, inferring biological networks, and controlling genetic diseases. Genes interact with other genes in a Gene Regulatory Network (GRN) and can be modeled as a Boolean Network (BN) or equivalently as a Finite State Machine (FSM). As the expression of genes deter- mine cell behavior, important problems include (i) inferring the GRN from observed gene expression data from biological measurements, and (ii) using the inferred GRN to explain how genetic diseases occur and determine the ”best” therapy towards treatment of disease. We report results on the application of logic synthesis techniques that we have developed to address both these problems. In the first technique, we present Boolean Satisfiability (SAT) based approaches to infer the predictor (logical support) of each gene that regulates melanoma, using gene expression data from patients who are suffering from the disease. From the output of such a tool, biologists can construct targeted experiments to understand the logic functions that regulate a particular target gene. Our second technique builds upon the first, in which we use a logic synthesis technique; implemented using SAT, to determine gene regulating functions for predictors and gene expression data. This technique determines a BN (or family of BNs) to describe the GRN and is validated on a synthetic network and the p53 network. The first two techniques assume binary valued gene expression data. In the third technique, we utilize continuous (analog) expression data, and present an algorithm to infer and rank predictors using modified Zhegalkin polynomials. We demonstrate our method to rank predictors for genes in the mutated mammalian and melanoma networks. The final technique assumes that the GRN is known, and uses weighted partial Max-SAT (WPMS) towards cancer therapy. In this technique, the GRN is assumed to be known. Cancer is modeled using a stuck-at fault model, and ATPG techniques are used to characterize genes leading to cancer and select drugs to treat cancer. To steer the GRN state towards a desirable healthy state, the optimal selection of drugs is formulated using WPMS. Our techniques can be used to find a set of drugs with the least side-effects, and is demonstrated in the context of growth factor pathways for colon cancer

A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma.

BACKGROUND: Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. RESULTS: By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS: The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies

A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer

Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.Sung-Young Shin, Margaret M. Centenera, Joshua T. Hodgson, Elizabeth V. Nguyen, Lisa M. Butler, Roger J. Daly and Lan K. Nguye

Exploiting natural and induced genetic variation to study hematopoiesis

PUZZLING WITH DNA Blood cell formation can be studied by making use of natural genetic variation across mouse strains. There are, for example, two mouse strains that do not only differ in fur color, but also in average life span and more specifically in the number of blood-forming stem cells in their bone marrow. The cause of these differences can be found in the DNA of these mice. This DNA differs slightly between the two mouse strains, making some genes in one strain just a bit more or less active compared to those same genes in the other strain. The aim of part I of this thesis was to study the influence of genetic variation on gene expression and how this might explain the specific characteristics of the mouse strains. One of the findings in this study was that the influence of genetic variation on gene expression is strongly cell-type-dependent. Additionally, blood cell formation can be studied by introducing genetic variation into the system. In part II of this thesis genetic variation was introduced into mouse blood-forming stem cells by letting random DNA sequences or “barcodes” integrate into the DNA of these cells. Thereby, these cells were provided with a unique and identifiable label that was heritable from mother- to daughter cell. In this manner the fate of blood-forming stem cells and their progeny could be tracked following transplantation in mice. This technique is very promising for monitoring blood cell formation in future clinical gene therapy studies in humans. PUZZELEN MET DNA Bloedvorming kan bestudeerd worden door gebruik te maken van natuurlijke genetische variatie tussen muizenstammen. Zo bestaan er bijvoorbeeld twee muizenstammen die niet alleen verschillen in vachtkleur, maar ook in gemiddelde levensduur en meer specifiek in het aantal bloedvormende stamcellen dat zich in hun beenmerg bevindt. De oorzaak van deze verschillen kan gevonden worden in het DNA van deze muizen. Dat DNA verschilt net iets tussen de twee muizenstammen, waardoor sommige genen in de ene stam actiever of juist minder actief zijn dan diezelfde genen in de andere stam. In deel I van dit proefschrift is onderzocht hoe genetische variatie de expressie van genen beïnvloedt en hoe dit de specifieke eigenschappen van de muizenstammen zou kunnen verklaren. Er is onder andere gevonden dat de invloed van genetische variatie op de expressie van genen sterk celtype-afhankelijk is. Daarnaast kan bloedvorming bestudeerd worden door genetische variatie te introduceren in het systeem. In deel II van dit proefschrift is genetische variatie in bloedvormende stamcellen van muizen geïntroduceerd door random DNA volgordes of “barcodes” te laten integreren in het DNA van deze cellen. Dit resulteert erin dat elke cel voorzien wordt van een uniek label dat overgegeven wordt van moeder- op dochtercel. De DNA volgorde van het label kan gelezen worden met behulp van een zogenaamde sequencing techniek. Op deze manier kan het lot van bloedvormende stamcellen en hun nakomelingen gevolgd worden na transplantatie in muizen. Deze techniek is zeer veelbelovend voor het monitoren van bloedvorming in toekomstige klinische gentherapie studies in de mens.