This interdisciplinary research investigates creating dynamic models of biological systems with predictive power that is beyond the capabilities of current generation of descriptive, static models. A biological system such as a cell can be conceptualized as a complex integrated network of biochemical reactions. Metabolic reactions are an important class of reactions performing essential cellular functions such as energy generation, biosynthesis, and harmful waste and byproduct elimination. Predictive models of cellular metabolism offer broad benefits as tools for both basic and applied research. In the context of public health, metabolic models can be used to integrate new laboratory and clinical data on drug efficacy, to compare healthy and diseased tissues, and to predict potentially harmful side effects of new drugs under development. Metabolic models also play an essential role in biotechnology as they enable the design and optimization of genetically engineered microbial cells that produce industrially useful bulk and value-added chemicals (e.g. biofuels).
This research focuses on two innovative modeling techniques for metabolic networks. The driving principle for both techniques is integration of structural and functional analyses. The first technique is multi-resolution structural modeling, which combines top-down modularization and bottom-up functional abstraction of individual reactions. The second technique compensates for incomplete information during mathematical modeling. This work investigates a co-estimation of reaction rate law functions and relevant parameters for each dominant reaction set while using noisy data for model calibration. The two modeling techniques and their associated algorithms are tested on experimental data collected from cultures of liver cells (hepatocytes), a representative and well-studied model system with biochemical complexity and relevance to public health. The modeling techniques obtained through this study feature generic aspects, e.g. mathematical abstraction of directed and time-varying interactions, which apply not only to metabolic networks but also other types of important biochemical (e.g. signaling, gene regulatory, etc.) networks.
