|Ph.D Student||Judith Somekh|
|Subject||Managing Molecular Biology Knowledge: A Conceptual|
Model-Based Systems Biology Approach
|Department||Department of Industrial Engineering and Management||Supervisors||Full Professor Dori Dov|
|Full Professor Choder Mordechai|
|Full Thesis text|
Given the unmanageable amounts of biological data and the high costs needed for conducting a biological research, a formal yet highly expressive systems biology conceptual modeling framework is needed. This framework should serve not only for supporting researchers in integrating the myriad items of often unrelated pieces of information, but also for gradually fleshing out a unified perspective of the architecture of the cell as a living system. Such a framework can be valuable for supporting the biological research and may increase its efficiency while decreasing its costs. We propose a Conceptual Model-based Systems Biology framework for qualitative modeling of molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a graphical and textual executable modeling language. OPM enables concurrent representation of the system's structure?the objects that comprise the system, and its behavior?how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we modeled two case studies?the mRNA transcription cycle and the mRNA Decay, which together comprise part of the mRNA lifecycle. Starting with this high level cell process, the model includes increasingly detailed processes along with participating objects. The modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology (GO), all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting.
During modeling and executing the mRNA transcription and mRNA Decay models, we detected inconsistencies and discovered knowledge gaps, which we present and classify into various types. We show how model execution detects errors and enhances the construction of a mechanistically coherent model.
Additionally, we demonstrate the value of our modeling framework, on the mRNA decay model, to reproduce outcomes that match the experimental findings, predict new possible outcomes and evaluate biological conjectures before conducting wet-lab experiments.
The ability to identify and pinpoint knowledge gaps as well as evaluate conjectures is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified executable model. Therefore the method can be used to preserve and evolve a coherent biological model and predict the validity of conjectures in silico before testing them in vivo or in vitro.