Limitations of quantitative gene regulation models: a case study.

Understanding the relationship between network structure and behavior is fundamental to the field of computational and systems biology. A particularly important distinction is the extent to which qualitative aspects of network performance are encoded in network topology as opposed to being determined through quantitative details, such as those of kinetics. Here, we develop a general and rigorous mathematical framework for the analysis of genetic networks and apply it to a family of synthetic gene networks. A key feature of our methodology involves determining network behavior that is insensitive to kinetic parameters such as rate constants and nonlinear functional dependencies of rates on molecular concentrations. Results indicate that behavior observed in some networks cannot be reconciled with standard gene expression and regulation models. We explore relaxing model assumptions to explain the observed behavior, allowing for both dynamic and stochastic phenomena, and propose an alternative model. Our alternative model includes the suggestion of a new mechanism by which the counterintuitive behavior could be achieved; central to the model is the assumption that the Clp protein degradation system, which is responsible for the regulatory proteins used in this study, becomes saturated.

[1]  H. Bujard,et al.  Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. , 1997, Nucleic acids research.

[2]  M. Elowitz,et al.  Combinatorial Synthesis of Genetic Networks , 2002, Science.

[3]  L. Serrano,et al.  Engineering stability in gene networks by autoregulation , 2000, Nature.

[4]  R. Sauer,et al.  Role of a Peptide Tagging System in Degradation of Proteins Synthesized from Damaged Messenger RNA , 1996, Science.

[5]  G. Odell,et al.  The segment polarity network is a robust developmental module , 2000, Nature.

[6]  J. Collins,et al.  Construction of a genetic toggle switch in Escherichia coli , 2000, Nature.

[7]  M. Elowitz,et al.  A synthetic oscillatory network of transcriptional regulators , 2000, Nature.

[8]  P. Swain,et al.  Stochastic Gene Expression in a Single Cell , 2002, Science.

[9]  Steven H. Strogatz,et al.  Nonlinear Dynamics and Chaos , 2024 .

[10]  Douglas A Lauffenburger,et al.  Modeling and computational analysis of EGF receptor-mediated cell communication in Drosophila oogenesis. , 2002, Development.

[11]  D. Endy,et al.  Computation, prediction, and experimental tests of fitness for bacteriophage T7 mutants with permuted genomes. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[12]  E. Gilles,et al.  Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors , 2002, Nature Biotechnology.

[13]  U. Alon,et al.  Negative autoregulation speeds the response times of transcription networks. , 2002, Journal of molecular biology.

[14]  A. Arkin,et al.  Stochastic mechanisms in gene expression. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[15]  A. Arkin,et al.  Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. , 1998, Genetics.

[16]  C. Rao,et al.  Control motifs for intracellular regulatory networks. , 2001, Annual review of biomedical engineering.

[17]  D. Gillespie.  Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[18]  M. Thattai,et al.  Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.