%0 Book Section %1 statphys23_0337 %A Biham, O. %A Lipshtat, A. %A Loinger, A. %A Balaban, N.Q. %B Abstract Book of the XXIII IUPAP International Conference on Statistical Physics %C Genova, Italy %D 2007 %E Pietronero, Luciano %E Loreto, Vittorio %E Zapperi, Stefano %K genetic motifs network networks simulations statphys23 stochastic switch systems topic-10 %T Stochastic simulations of genetic regulatory networks %U http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=337 %X Regulation processes in cells are performed by networks of interacting genes, which regulate each other's expression. Recent studies have shown that these networks are typically sparse and include recurring modules or motifs. To analyze the function of these networks, one needs to simulate their dynamics. Since the networks often exhibit strong fluctuations, stochastic methods, based on the master equation, are required. In this talk I will consider a class of genetic switch systems, in which bistability is induced by the fluctuations 1,2. I will show that in general network modules that include feedback, fluctuations give rise to crucial quantitative and qualitative effects. The stochastic simulation of separate modules is only the first step in the analysis of genetic networks. More complete understanding of network function will require to simulate large complex networks, which consist of many interacting modules. While direct integration of the master equation is suitable for the analysis of small network modules, it becomes infeasible in the case of complex networks, since the number of equations proliferates exponentially with the number of genes in the network. To address this problem, I will present the multi-plane method (originally introduced in the context of stochastic chemical netoworks), which provides a dramatic reduction in the number of equations 3. The reduction is obtained by breaking the network into fully connected sub-networks (cliques), with suitable couplings between them. This method enables to perform stochastic analysis of genetic networks of any size and complexity and to analyze their function. 1) A. Lipshtat, A. Loinger, N.Q. Balaban and O. Biham, Genetic toggle switch without cooperative binding, Phys. Rev. Lett. 96, 188101 (2006).\\ 2) A. Loinger, A. Lipshtat, N.Q. Balaban and O. Biham, Stochastic simulations of genetic switch systems, Phys. Rev. E 75, 021904 (2007).\\ 3) A. Lipshtat, O. Biham, Efficient simulations of gas-grain chemistry in interstellar clouds, Phys. Rev. Lett. 93, 170601 (2004).

@incollection{statphys23_0337, abstract = {Regulation processes in cells are performed by networks of interacting genes, which regulate each other's expression. Recent studies have shown that these networks are typically sparse and include recurring modules or motifs. To analyze the function of these networks, one needs to simulate their dynamics. Since the networks often exhibit strong fluctuations, stochastic methods, based on the master equation, are required. In this talk I will consider a class of genetic switch systems, in which bistability is induced by the fluctuations [1,2]. I will show that in general network modules that include feedback, fluctuations give rise to crucial quantitative and qualitative effects. The stochastic simulation of separate modules is only the first step in the analysis of genetic networks. More complete understanding of network function will require to simulate large complex networks, which consist of many interacting modules. While direct integration of the master equation is suitable for the analysis of small network modules, it becomes infeasible in the case of complex networks, since the number of equations proliferates exponentially with the number of genes in the network. To address this problem, I will present the multi-plane method (originally introduced in the context of stochastic chemical netoworks), which provides a dramatic reduction in the number of equations [3]. The reduction is obtained by breaking the network into fully connected sub-networks (cliques), with suitable couplings between them. This method enables to perform stochastic analysis of genetic networks of any size and complexity and to analyze their function. 1) A. Lipshtat, A. Loinger, N.Q. Balaban and O. Biham, Genetic toggle switch without cooperative binding, Phys. Rev. Lett. 96, 188101 (2006).\\ 2) A. Loinger, A. Lipshtat, N.Q. Balaban and O. Biham, Stochastic simulations of genetic switch systems, Phys. Rev. E 75, 021904 (2007).\\ 3) A. Lipshtat, O. Biham, Efficient simulations of gas-grain chemistry in interstellar clouds, Phys. Rev. Lett. 93, 170601 (2004).}, added-at = {2007-06-20T10:16:09.000+0200}, address = {Genova, Italy}, author = {Biham, O. and Lipshtat, A. and Loinger, A. and Balaban, N.Q.}, biburl = {https://www.bibsonomy.org/bibtex/2c1d093872033646c7fe0ffd1f187f9c4/statphys23}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Pietronero, Luciano and Loreto, Vittorio and Zapperi, Stefano}, interhash = {72dde01cde5503c80f3d30040e22b09e}, intrahash = {c1d093872033646c7fe0ffd1f187f9c4}, keywords = {genetic motifs network networks simulations statphys23 stochastic switch systems topic-10}, month = {9-13 July}, timestamp = {2007-06-20T10:16:17.000+0200}, title = {Stochastic simulations of genetic regulatory networks}, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=337}, year = 2007 }