title: Adaptive models for gene networks.
abstract: Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems.
authors.affiliation: Electrical and Computer Engineering, Cornell University, Ithaca, New York, United States of America.
authors.forename: Yong-Jun
authors.initials: YJ
authors.lastname: Shin
authors.forename: Ali H
authors.initials: AH
authors.lastname: Sayed
authors.forename: Xiling
authors.initials: X
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D051736:Proto-Oncogene Proteins c-mdm2 / Q000235:genetics; D049490:Systems Biology; D013997:Time Factors; D016159:Tumor Suppressor Protein p53 / Q000235:genetics
chemical_list: D016159:Tumor Suppressor Protein p53; C497206:MDM2 protein, human; D051736:Proto-Oncogene Proteins c-mdm2