Stochastic inference and control of complex biological networks

University Of Delaware, Newark DE

Investigators

Abstract

Living cells encode complex and dynamic networks of interconnected biomolecular components that orchestrate diverse life processes. Unlike electrical circuits, these networks consist of biochemical species (genes, proteins, RNAs, etc.) that interact and regulate each other via chemical reactions. A holistic understanding of how biomolecular networks process information for cellular decision-making will have tremendous long-term impact on human health. For example, a systems-level deciphering of biomolecular networks will fundamentally transform our knowledge of aberrant regulation driving diseased states, and will lead to novel ways of finding biomarkers and drug targets that take the network dynamics into account. Moreover, designing and rewiring of networks using systems approaches will open doors for different applications, such as, production of biofuels and therapeutics. To facilitate these transformations, this project aims to build scalable mathematical tools for modeling, analysis, inference and control of biological networks. Measurements inside individual cells reveal biological networks with rich stochastic dynamics, owing to the inherent probabilistic nature of biochemical processes. The intellectual merit lies in modeling of biomolecular networks via a Stochastic Hybrid Systems (SHS) framework. Combining tools from control theory, dynamical systems and random processes, the project will develop computationally tractable methods for analyzing deterministic and stochastic dynamics of complex biomolecular systems. These methods will improve computational efficiency of predicting network dynamics by orders of magnitude as compared to traditionally used Monte Carlo simulation techniques. Analysis tools will be used to explore designs of feedback and feedforward loops in biomolecular systems that allow for systematic manipulation of network activity, for example, controlling fluctuations in the level of a specific protein embedded in a larger network. Accurate methods for predicting stochastic dynamics also motivate an intriguing inference problem of learning about the underlying network architecture from measured joint fluctuations in the network components. Advances in single-cell technologies enable precise quantitative measurements of protein copy numbers inside individual cells over time. Motivated by increasing availability of such measurements, the project will build inference methods for improved characterization and reverse engineering of network interactions from time-series measurements of protein levels. In terms of broader impact, the project tools will be applied to study diverse biological pathways in close collaborations with experimental researchers in the life sciences. These include regulatory networks underlying several medically important systems, such as, cell-fate regulation in the human immunodeficiency virus (HIV), and nongenetic drug resistance in cancer cells. Finally, courses applying systems and control concepts to biological networks will be developed for both students and professionals from local industry.