Network control has been originally developed as part of systems and control theory. While the methods developed in this area have been applied successfully to many engineered and natural systems, several factors have limited its application to large complex biological systems such as cellular signaling networks. These include the limited scalability of the methods for large networks with thousands of components, incomplete information to build dynamical models with hundreds of variables, and the linearity assumption for the underlying dynamics by which bi-stability, that characterizes signaling networks, might be affected. Our two major aims in this project are: (1) Design highly scalable algorithms to identify combinations of intervention nodes to achieve targeted control of large signaling networks considering different graph structures and (2) Develop efficient algorithms for the prioritization of combinations of intervention nodes given the structure of the network and quantitative data.
Structure-Based Control of Dynamical Systems
Luis Sordo Vieira, Paola Vera-Licona †. Computing Signal Transduction in signaling networks modeled as Boolean Networks, Petri Nets and hypergraphs. bioRxiv 272344 https://doi.org/10.1101/272344, 2018.
Andrew Gainer-Dewar, Paola Vera-Licona †. The Minimal Hitting Set Generation Problem: Algorithms and Computation. SIAM Journal on Discrete Mathematics. 2017; 31(1):63–100, 2017.
Paola Vera-Licona †, Eric Bonnet, Emmanuel Barillot, Andrei Zinovyev. OCSANA: optimal combinations of interventions from network analysis. Bioinformatics, Volume 29, Issue 12, 15 June 2013, Pages 1571–1573, 2013.