Deriving executable models of biochemical network dynamics from qualitative data

Progress in advancing our understanding of biological systems is limited by their sheer complexity, the cost of laboratory materials and equipment, and limitations of current laboratory technology. Computational and mathematical modeling provide ways to address these obstacles through hypothesis generation and testing without experimentation---allowing researchers to analyze system structure and dynamics in silico and, then, design lab experiments that yield desired information about phenomena of interest. These models, however, are only as accurate and complete as the data used to build them. Currently, most models are constructed from quantitative experimental data. However, since accurate quantitative measurements are hard to obtain and difficult to adapt from literature and online databases, new sources of data for building models need to be explored. In my work, I have designed methods for building and executing computational models of cellular network dynamics based on qualitative experimental data, which are more abundant, easier to obtain, and reliably reproducible. Such executable models allow for in silico perturbation, simulation, and exploration of biological systems. In this thesis, I present two general strategies for building and executing tokenized models of biochemical networks using only qualitative data. Both methods have been successfully used to model and predict the dynamics of signaling networks in normal and cancer cell lines, rivaling the accuracy of existing methods trained on quantitative data. I have implemented these methods in the software tools PathwayOracle and Monarch, making the new techniques I present here accessible to experimental biologists and other domain experts in cellular biology

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods

Modeling and Analysis of Signal Transduction Networks

Biological pathways, such as signaling networks, are a key component of biological systems of each living cell. In fact, malfunctions of signaling pathways are linked to a number of diseases, and components of signaling pathways are used as potential drug targets. Elucidating the dynamic behavior of the components of pathways, and their interactions, is one of the key research areas of systems biology. Biological signaling networks are characterized by a large number of components and an even larger number of parameters describing the network. Furthermore, investigations of signaling networks are characterized by large uncertainties of the network as well as limited availability of data due to expensive and time-consuming experiments. As such, techniques derived from systems analysis, e.g., sensitivity analysis, experimental design, and parameter estimation, are important tools for elucidating the mechanisms involved in signaling networks. This Special Issue contains papers that investigate a variety of different signaling networks via established, as well as newly developed modeling and analysis techniques

Integrated analysis of breast cancer cell lines reveals unique signaling pathways

Mapping of sub-networks in the EGFR-MAPK pathway in different breast cancer cell lines reveals that PAK1 may be a marker for sensitivity to MEK inhibitors

Reconstruction And Analysis Of The Molecular Programs Involved In Deciding Mammalian Cell Fate

Cellular function hinges on the ability to process information from the outside environment into specific decisions. Ultimately these processes decide cell fate, whether it be to undergo proliferation, apoptosis, differentiation, migration and other cellular functions. These processes can be thought of as finely tuned programs evolved to maintain robust function in spite of environmental perturbations. Malfunctions in these programs can lead to improper cellular function and various disease states. To develop more effective, personalized and even preventative therapeutics we must attain a better, more detailed, understanding of the programs involved. To this end we have employed mechanistic mathematical modeling to a variety of complex cellular programs. In Chapter 1, we review a variety of computational methods have have been used successfully in different areas of biotechnology. In Chapter 2, we present the software platform UNIVERSAL, which was developed in our lab. UNIVERSAL is an extensible code generation framework for Mac OS X which produces editable, fully commented platform-independent physiochemical model code in several common programming languages from a variety of inputs. UNIVERSAL generates mass-action ODE models of intracellular signal transduction processes and model analysis code, such as adjoint sensitivity balances. We employed the mass-action ODE framework, as generated by UNIVERSAL, commonly throughout the studies presented here. In Chapter 3, we introduce a variety of modeling strategies in the context of EGF-induced Eukaryotic transcription. We demon- strated the ability to make meaningful and statistically consistent model predictions despite considerable parametric uncertainty. In Chapter 4, we constructed a mathematical model to study a mechanism for androgen independent proliferation in prostate cancer. Analysis of the model provided insight into the importance of network components as a function of androgen dependence. Translation became progressively more important in androgen independent cells. Moreover, the analysis suggested that direct targeting of the translational machinery, specifically eIF4E, could be efficacious in androgen independent prostate cancers. In Chapter 5, A mathematical model of RA-induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt) knock-out and knock-in HL-60 cell lines with and without RA. The ensemble of HL-60 models recapitulated the positive feedback between BLR1 and MAPK signaling. We investigated the robustness of the HL-60 network architecture to structural perturbations and generated experimentally testable hypotheses for future study. In Chapter 6, we carried out experimental studies to reduce the structural uncertainty of the HL60 model. Result from the HL-60 model cRaf as the most critical component of the MAPK cascade. To investigate the role of cRaf in RA-induced differentiation we observed the effect of cRaf kinase inhibition. Furthermore, we interrogated a panel of proteins to identify RA responsive cRaf binding partner. We found that cRaf kinase activity was necessary for functional ROS response, but not for RA-induced growth arrest. Based on our findings, we proposed a simplified ontrol architecture for sustained MAPK activation. Computational modeling identified a bistability suggesting that the MAPK activation was self-sustaining. This result was experimentally validated, and could explain previously observed cellular memory effects. Taken together, the results of these studies demonstrated that computational modeling can identify therapeutically relevant targets for human disease such as cancer. Furthermore, we demonstrated the ability of an iterative strategy between computational and experimental analysis to provide insight on key regulator circuits for complex programs involved in deciding cell fate

Applications of Boolean modelling to study and stratify dynamics of a complex disease

Interpretation of omics data is needed to form meaningful hypotheses about disease mechanisms. Pathway databases give an overview of disease-related processes, while mathematical models give qualitative and quantitative insights into their complexity. Similarly to pathway databases, mathematical models are stored and shared on dedicated platforms. Moreover, community-driven initiatives such as disease maps encode disease-specific mechanisms in both computable and diagrammatic form using dedicated tools for diagram biocuration and visualisation. To investigate the dynamic properties of complex disease mechanisms, computationally readable content can be used as a scaffold for building dynamic models in an automated fashion. The dynamic properties of a disease are extremely complex. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. In this study, Parkinson’s disease (PD) is analyzed as an example of a complex disorder. PD is associated with complex genetic, environmental causes and comorbidities that need to be analysed in a systematic way to better understand the progression of different disease subtypes. Studying PD as a multifactorial disease requires deconvoluting the multiple and overlapping changes to identify the driving neurodegenerative mechanisms. Integrated systems analysis and modelling can enable us to study different aspects of a disease such as progression, diagnosis, and response to therapeutics. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. Modelling such complex processes depends on the scope and it may vary depending on the nature of the process (e.g. signalling vs metabolic). Experimental design and the resulting data also influence model structure and analysis. Boolean modelling is proposed to analyse the complexity of PD mechanisms. Boolean models (BMs) are qualitative rather than quantitative and do not require detailed kinetic information such as Petri nets or Ordinary Differential equations (ODEs). Boolean modelling represents a logical formalism where available variables have binary values of one (ON) or zero (OFF), making it a plausible approach in cases where quantitative details and kinetic parameters 9 are not available. Boolean modelling is well validated in clinical and translational medicine research. In this project, the PD map was translated into BMs in an automated fashion using different methods. Therefore, the complexity of disease pathways can be analysed by simulating the effect of genomic burden on omics data. In order to make sure that BMs accurately represent the biological system, validation was performed by simulating models at different scales of complexity. The behaviour of the models was compared with expected behavior based on validated biological knowledge. The TCA cycle was used as an example of a well-studied simple network. Different scales of complex signalling networks were used including the Wnt-PI3k/AKT pathway, and T-cell differentiation models. As a result, matched and mismatched behaviours were identified, allowing the models to be modified to better represent disease mechanisms. The BMs were stratified by integrating omics data from multiple disease cohorts. The miRNA datasets from the Parkinson’s Progression Markers Initiative study (PPMI) were analysed. PPMI provides an important resource for the investigation of potential biomarkers and therapeutic targets for PD. Such stratification allowed studying disease heterogeneity and specific responses to molecular perturbations. The results can support research hypotheses, diagnose a condition, and maximize the benefit of a treatment. Furthermore, the challenges and limitations associated with Boolean modelling in general were discussed, as well as those specific to the current study. Based on the results, there are different ways to improve Boolean modelling applications. Modellers can perform exploratory investigations, gathering the associated information about the model from literature and data resources. The missing details can be inferred by integrating omics data, which identifies missing components and optimises model accuracy. Accurate and computable models improve the efficiency of simulations and the resulting analysis of their controllability. In parallel, the maintenance of model repositories and the sharing of models in easily interoperable formats are also important

Resolution of inflammation: Mechanisms and opportunity for drug development

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Unravelling the insulin signalling pathway using mechanistic modelling

Type two diabetes affects 5% of the world's population and is increasing in prevalence. A key precursor to this disease is insulin resistance, which is characterised by a loss of responsiveness to insulin in liver, muscle and adipose tissue. This thesis focuses on understanding insulin signalling using the 3T3-L1 adipocyte cell model. Computational modelling was used to generate quantitative predictions in the signalling pathways of the adipocyte, many of which are mediated by enzymatic reactions. This study began by comparing existing enzyme kinetic models and evaluating their applicability to insulin signalling in particular. From this understanding, we developed an improved enzyme kinetic model, the differential quasi-steady state model (dQSSA), that avoids the reactant stationary assumption used in the Michaelis Menten model. The dQSSA was found to more accurately model the behaviours of enzymes in large in silico systems, and in various coenzyme inhibited and non-inhibited reactions in vitro. To apply the dQSSA, the SigMat software package was developed in the MATLAB environment to construct mathematical models from qualitative descriptions of networks. After the robustness of the package was verified, it was used to construct a basic model of the insulin signalling pathway. This model was trained against experimental temporal data at 1 nM and 100 nM doses of insulin. It revealed that the simple description of Akt activation, which displays an overshoot behaviour, was insufficient to describe the kinetics of substrate phosphorylation, which does not display the overshoot behaviour. The model was expanded to include Akt translocation and the individual phosphorylation at the 308 and 473 residues. This model resolved the discrepancy and predicts that Akt substrates are only accessible to Akt localised in the cytosol and that PIP3 sequestration of cytosolic Akt acts as a negative feedback

An Extended Gene Protein/Products Boolean Network Model Including Post-Transcriptional Regulation

Background: Networks Biology allows the study of complex interactions between biological systems using formal, well structured, and computationally friendly models. Several different network models can be created, depending on the type of interactions that need to be investigated. Gene Regulatory Networks (GRN) are an effective model commonly used to study the complex regulatory mechanisms of a cell. Unfortunately, given their intrinsic complexity and non discrete nature, the computational study of realistic-sized complex GRNs requires some abstractions. Boolean Networks (BNs), for example, are a reliable model that can be used to represent networks where the possible state of a node is a boolean value (0 or 1). Despite this strong simplification, BNs have been used to study both structural and dynamic properties of real as well as randomly generated GRNs. Results: In this paper we show how it is possible to include the post-transcriptional regulation mechanism (a key process mediated by small non-coding RNA molecules like the miRNAs) into the BN model of a GRN. The enhanced BN model is implemented in a software toolkit (EBNT) that allows to analyze boolean GRNs from both a structural and a dynamic point of view. The open-source toolkit is compatible with available visualization tools like Cytoscape and allows to run detailed analysis of the network topology as well as of its attractors, trajectories, and state-space. In the paper, a small GRN built around the mTOR gene is used to demonstrate the main capabilities of the toolkit. Conclusions: The extended model proposed in this paper opens new opportunities in the study of gene regulation. Several of the successful researches done with the support of BN to understand high-level characteristics of regulatory networks, can now be improved to better understand the role of post-transcriptional regulation for example as a network-wide noise-reduction or stabilization mechanism

Modular languages for systems and synthetic biology

Systems biology is a rapidly growing field which seeks a refined quantitative understanding of organisms, particularly studying how molecular species such as metabolites, proteins and genes interact in cells to form the complex emerging behaviour exhibited by living systems. Synthetic biology is a related and emerging field which seeks to engineer new organisms for practical purposes. Both fields can benefit from formal languages for modelling, simulation and analysis. In systems biology there is however a trade-off in the landscape of existing formal languages: some are modular but may be difficult for some biologists to understand (e.g. process calculi) while others are more intuitive but monolithic (e.g. rule-based languages). The first major contribution of this thesis is to bridge this gap with a Language for Biochemical Systems (LBS). LBS is based on the modular Calculus of Biochemical Systems and adds e.g. parameterised modules with subtyping and a notion of nondeterminism for handling combinatorial explosion. LBS can also incorporate other rule-based languages such as Kappa, hence adding modularity to these. Modularity is important for a rational structuring of models but can also be exploited in analysis as is shown for the specific case of Petri net flows. On the synthetic biology side, none of the few existing dedicated languages allow for a high-level description of designs that can be automatically translated into DNA sequences for implementation in living cells. The second major contribution of this thesis is exactly such a language for Genetic Engineering of Cells (GEC). GEC exploits the recent advent of standard genetic parts (“biobricks”) and allows for the composition of such parts into genes in a modular and abstract manner using logical constraints. GEC programs can then be translated to DNA sequences using a constraint satisfaction engine based on a given database of genetic parts