adaptive models for gene networks为基因网络自适应模型.pdfVIP

Adaptive Models for Gene Networks 1 2 1 Yong-Jun Shin , Ali H. Sayed , Xiling Shen * 1 Electrical and Computer Engineering, Cornell University, Ithaca, New York, United States of America, 2 Electrical Engineering, University of California Los Angeles, Los Angeles, California, United States of America 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. Citation: Shin Y-J, Sayed AH, Shen X (2012) Adaptive Models for Gene Networks. PLoS ONE 7(2): e31657. doi:10.1371/journal.pone.0031657 Editor: Mark Isalan, Center for Genomic Regulation, Spain Received November 3, 2011; Accepted January 17, 2012; Published February 16, 2012 Copyright: 2012 Shin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by Cornell Stem Cell Program, National Institutes of Health grant R01GM95990 and National Science Foundation grants CCF- 1011918 and CCF-0942936. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: xs66@ Introduction