2014/08/31 by Jorge G. T. Zañudo, Réka Albert · 1 citation
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #q-bio.MN #cond-mat.dis-nn #physics.bio-ph
paper · pdf · doi:10.1371/journal.pcbi.1004193
61 pages (main text, 15 pages; supporting information, 46 pages) and 12 figures (main text, 6 figures; supporting information, 6 figures). In review
arxiv created 2014/12/15 · arxiv updated 2015/08/19
Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.