Evolving BlenX programs to simulate the evolution of biological networks

We present a formal approach to study the evolution of biological networks. We use the Beta Workbench and its BlenX language to model and simulate networks in connection with evolutionary algorithms. Mutations are done on the structure of BlenX programs and networks are selected at any generation by using a fitness function. The feasibility of the approach is illustrated with a simple example.

[1]  Corrado Priami,et al.  Modelling and simulation of biological processes in BlenX , 2008, PERV.

[2]  S. Teichmann,et al.  Evolutionary dynamics of prokaryotic transcriptional regulatory networks. , 2006, Journal of molecular biology.

[3]  Luca Cardelli,et al.  A Graphical Representation for Biological Processes in the Stochastic pi-Calculus , 2006, Trans. Comp. Sys. Biology.

[4]  Corrado Priami,et al.  The Beta Workbench: a computational tool to study the dynamics of biological systems , 2008, Briefings Bioinform..

[5]  Chi-Ying F. Huang,et al.  Ultrasensitivity in the mitogen-activated protein kinase cascade. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[6]  D. Gillespie.  A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions , 1976 .

[7]  Lawrence J. Fogel,et al.  Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming , 1999 .

[8]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[9]  Ann M Stock,et al.  Two-component signal transduction. , 2000, Annual review of biochemistry.

[10]  Sebastian Bonhoeffer,et al.  The Evolution of Connectivity in Metabolic Networks , 2005, PLoS biology.

[11]  E. Shapiro,et al.  Cellular abstractions: Cells as computation , 2002, Nature.

[12]  Corrado Priami,et al.  Application of a stochastic name-passing calculus to representation and simulation of molecular processes , 2001, Inf. Process. Lett..

[13]  Corrado Priami,et al.  Beta Binders for Biological Interactions , 2004, CMSB.

[14]  Sebastian Bonhoeffer,et al.  Evolution of complexity in signaling pathways , 2006, Proceedings of the National Academy of Sciences.

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

[16]  Aviv Regev,et al.  Representation and Simulation of Biochemical Processes Using the pi-Calculus Process Algebra , 2000, Pacific Symposium on Biocomputing.

[17]  V. Hakim,et al.  Design of genetic networks with specified functions by evolution in silico. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Corrado Priami,et al.  A Formal and Integrated Framework to Simulate Evolution of Biological Pathways , 2007, CMSB.

[19]  Corrado Priami,et al.  The Beta Workbench , 2007 .

[20]  R. Karp,et al.  From the Cover : Conserved patterns of protein interaction in multiple species , 2005 .

[21]  Luca Cardelli,et al.  A Correct Abstract Machine for the Stochastic Pi-calculus , 2004 .

[22]  S. Schuster,et al.  Game-theoretical approaches to studying the evolution of biochemical systems. , 2005, Trends in biochemical sciences.