Predictive Network Modeling And Experimentation In Complex Biological Systems

Released on by Steven Steinway
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  • Predictive Network Modeling And Experimentation In Complex Biological Systems Book Detail

  • Author : Steven Steinway
  • Release Date : 2015
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  • File Size : 98,98 MB
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Predictive Network Modeling And Experimentation In Complex Biological Systems by Steven Steinway PDF Summary

Book Description: Biology is incredibly complex -- at the molecular, cellular, tissue, and population level, there exists a tremendous number of discrete interacting components tightly regulating the processes that sustain life. Biological systems have traditionally been viewed in a reductionist manner often literally (and metaphorically) through a magnifying glass, leading to insight into how the individual parts work. Network theory, on the other hand, can be used to put the pieces together, to understand how complex and emergent behaviors arise from the totality of interactions in complex systems, such as those seen in biology. Network theory is the study of systems of discrete interacting components and provides a framework for understanding complex systems. A network-focused investigation of a complex biological system allows for the understanding of the system's emergent properties, for example its function and dynamics. Network dynamics are of particular interest biologically because biological systems are not static but are constantly changing in response to perturbations and environmental stimuli in space and time. Systems level biological analysis has been aided by the recent explosion of high throughput data. This has led to an abundance of quantitative and qualitative information related to the activation of biological systems, but frequently there is still a paucity of kinetic and temporal information. Discrete dynamic modeling provides a means to create predictive models of biological systems by integrating fragmentary and qualitative interaction information. Using discrete dynamic modeling, a structural (static) network of biological regulatory relationships can be translated into a mathematical model without the use of kinetic parameters. This model can describe the dynamics of a biological system (i.e. how it changes over time), both in normal and in perturbation (e.g. disease) scenarios. In this dissertation we present the application of network theory and discrete dynamic modeling integrated with experimental laboratory analysis to understand biological diseases in three contexts. The first is the construction of a network model of epidermal derived growth factor receptor (EGFR) signaling in cancer. We translate this model into two types of discrete models: a Boolean model and a three-state model. We show how the effects of an EGFR inhibitor (such as the drug gefitinib) can suppress tumor growth, and we model how genomic variants can augment the effect of EGFR inhibition in tumor growth. Importantly, we compare discrete modeling outcomes to an alternative modeling framework, which relies on detailed kinetic information, called ordinary differential equation (ODE) modeling and show that both models achieve similar findings. Our results demonstrate that discrete dynamic model can accurately model biomedical systems and make important predictions about the effect a drug will have on a disease (e.g. tumor growth) in the context of various perturbations. Importantly, discrete dynamic models can be employed in the absence of kinetic parameters, making this modeling approach suitable for the many biological systems in which detailed kinetic information is not available. Second, we construct a network model of epithelial-to-mesenchymal transition (EMT), a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant metastases. We demonstrate that the EMT network model recapitulates known dysregulations during the induction of EMT and predicts the activation of the Wnt and Sonic hedgehog (SHH) signaling pathways during this process. We confirm the cross-talk between TGF[beta], Wnt and SHH signaling in vitro in multiple human liver cancer cell lines and tumor samples. Next, we use the EMT network model to systematically explore perturbations that suppress EMT, with the ultimate goal of identifying therapeutic interventions that suppress tumor invasion. We computationally explore close to half a million individual and combination perturbations to the EMT network and identify that only a dozen suppress EMT. We test these interventions experimentally and our findings suggest that many predicted interventions suppress the EMT process. Lastly, we construct a model of the enormous ecological community of bacteria that live in our intestines, collectively called the gut microbiome. This model is used to understand the effect of antibiotic treatment and opportunistic C. difficile infection (a devastating and highly prevalent disease entity) on the native microbiome and predict therapeutic probiotic interventions to suppress C. difficile infection. We integrate this modeling with another type of modeling, genome scale metabolic network reconstructions, to understand metabolic differences between community members and to identify the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of my computational model, that Barnesiella intestinihominis can in fact suppress C. difficile growth. This novel result suggests that Barnesiella could potentially be used as a probiotic to suppress C. difficile growth.Taken together, the studies presented in this thesis demonstrate the tremendous capacity of network modeling to elucidate biomedical systems. We build networks, construct mathematical models, study network dynamics, and use network-directed insight to guide experiments in critical biomedical areas. The ultimate goal of this work has been to translate network-directed insight into actionable biomedical findings that lead to improved understanding of human disease, enhanced patient care, and a betterment of the human condition.

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