title: Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis.
abstract: Gene regulatory networks (GRNs) coherently coordinate the expressions of genes and control the behaviors of cellular systems. The complexity in modeling a quantitative GRN usually results from inaccurate parameter estimation, which is mostly due to small sample sizes. For better modeling of GRNs, we have designed a small-sample iterative optimization algorithm (SSIO) to quantitatively model GRNs with nonlinear regulatory relationships. The algorithm utilizes gene expression data as the primary input and it can be applied in case of small-sized samples. Using SSIO, we have quantitatively constructed the dynamic models for the GRNs controlling human and mouse adipogenesis. Compared with two other commonly-used methods, SSIO shows better performance with relatively lower residual errors, and it generates rational predictions on the adipocyte responses to external signals and steady-states. Sensitivity analysis further indicates the validity of our method. Several differences are observed between the GRNs of human and mouse adipocyte differentiations, suggesting the differences in regulatory efficiencies of the transcription factors between the two species. In addition, we use SSIO to quantitatively determine the strengths of the regulatory interactions as well as to optimize regulatory models. The results indicate that SSIO facilitates better investigation and understanding of gene regulatory processes.
authors.affiliation: College of Life Science and Biotechnology, Shanghai Jiaotong University, Shanghai, China.
authors.forename: Yin
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
authors.forename: Rudong
authors.initials: R
authors.lastname: Li
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Chunguang
authors.initials: C
authors.lastname: Ji
authors.affiliation: School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Shuliang
authors.initials: S
authors.lastname: Shi
authors.affiliation: Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
authors.forename: Yufan
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Shanghai Center for Bioinformation Technology, Shanghai, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Sun
authors.affiliation: College of Life Science and Biotechnology, Shanghai Jiaotong University, Shanghai, China; Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Shanghai Center for Bioinformation Technology, Shanghai, China.
authors.forename: Yixue
authors.initials: Y
authors.lastname: Li
mesh_terms: D017667:Adipocytes / Q000166:cytology / Q000378:metabolism; D050156:Adipogenesis; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D002478:Cells, Cultured; D053263:Gene Regulatory Networks; D006801:Humans; D008390:Markov Chains; D051379:Mice; D008957:Models, Genetic; D009010:Monte Carlo Method