EAGER: Formal Analysis of Stochastic Models in Systems Biology Under Uncertainty

College Of Charleston, Charleston SC

Investigators

Abstract

Biological system models comprise of multiple biological processes that are stochastic and often, multiscale, processes executing at different time scales such as communication of molecular processes with cellular processes. Formal methods such as model checking have been used as a querying mechanism to understand the details of communication of biological processes. The imprecision and incompleteness of experimental data in system level modeling of biological processes necessitates construction of novel theoretical framework and algorithms for formal analysis. The project is timely and appropriate in addressing the challenges of the state explosion problem in model checking combined with construction of multiscale and stochastic models in biology under uncertainty. The construction of novel model reduction algorithms for model checking of multiscale and stochastic systems becomes essential for a rigorous evaluation of queries represented in temporal logic. The goal of this project is to create a solid theoretical framework for formal analysis of multiscale models of stochastic systems and develop innovative models for efficient querying mechanisms for biological processes under uncertainty. Tools developed in the project will be open-sourced. The interdisciplinary nature of the research will provide opportunities for undergraduates for cross-fertilization of ideas between computer science, mathematics, chemistry and biology. The impact of proposed work will push the limits in modeling a large system under uncertainty for rigorous formal analysis and modeling in systems biology. The goal of this project is pursued by the aims, i) Develop theoretical formalism representing multiscale processes in stochastic systems under uncertainty for formal analysis and ii) Create and evaluate efficient algorithms for querying on models of stochastic systems under uncertainty. Models of stochastic models under uncertainty will be created and evaluated. The novel framework will provide succinct and precise models of biological processes when combined with experimental data. The project will be conducted at a primarily undergraduate institution (PUI). Educational materials from the project will be used at different levels of undergraduate curriculum. The project will provide research experiences through focused mini-course projects in undergraduate classes with the goal of developing interdisciplinary researchers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.