title: Computational study of p53 regulation via the chemical master equation.
abstract: A stochastic model of cellular p53 regulation was established in Leenders, and Tuszynski (2013 Front. Oncol. 3 1-16) to study the interactions of p53 with MDM2 proteins, where the stochastic analysis was done using a Monte Carlo approach. We revisit that model here using an alternative scheme, which is to directly solve the chemical master equation (CME) by an adaptive Krylov-based finite state projection method that combines the stochastic simulation algorithm with other computational strategies, namely Krylov approximation techniques to the matrix exponential, divide and conquer, and aggregation. We report numerical results that demonstrate the extend of tackling the CME with this combination of tools.
authors.affiliation: Department of Mathematics University of Alabama Tuscaloosa, AL 35487, USA.
authors.forename: Huy D
authors.initials: HD
authors.lastname: Vo
authors.forename: Roger B
authors.initials: RB
authors.lastname: Sidje
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008956:Models, Chemical; D051736:Proto-Oncogene Proteins c-mdm2 / Q000737:chemistry / Q000378:metabolism; D013269:Stochastic Processes; D016159:Tumor Suppressor Protein p53 / Q000737:chemistry* / Q000378:metabolism
chemical_list: D016159:Tumor Suppressor Protein p53; D051736:Proto-Oncogene Proteins c-mdm2