An Intuitive Automated Modelling Interface for Systems Biology

We introduce a natural language interface for building stochastic p calculus models of biological systems. In this language, complex constructs describing biochemical events are built from basic primitives of association, dissociation and transformation. This language thus allows us to model biochemical systems modularly by describing their dynamics in a narrative-style language, while making amendments, refinements and extensions on the models easy. We give a formal semantics for this language and a translation algorithm into stochastic p calculus that delivers this semantics. We demonstrate the language on a model of Fcg receptor phosphorylation during phagocytosis. We provide a tool implementation of the translation into a stochastic p calculus language, Microsoft Research’s SPiM, which can be used for simulation and analysis. 1 2

[1]  Luca Cardelli,et al.  A Process Model of Rho GTP-binding Proteins in the Context of Phagocytosis , 2009, FBTC@CONCUR.

[2]  Corrado Priami,et al.  An Automated Translation from a Narrative Language for Biological Modelling into Process Algebra , 2007, CMSB.

[3]  Luca Cardelli,et al.  On the Computational Power of Biochemistry , 2008, AB.

[4]  Ashish Tiwari,et al.  Analyzing Pathways Using SAT-Based Approaches , 2007, AB.

[5]  Corrado Priami,et al.  BlenX Static and Dynamic Semantics , 2009, CONCUR.

[6]  Luca Cardelli,et al.  Compositionality, stochasticity, and cooperativity in dynamic models of gene regulation , 2007, HFSP journal.

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

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

[9]  Erick García-García,et al.  Signal transduction during Fc receptor‐mediated phagocytosis , 2002, Journal of leukocyte biology.

[10]  Corrado Priami,et al.  Stochastic pi-Calculus , 1995, Comput. J..

[11]  Monika Heiner,et al.  Petri Nets for Systems and Synthetic Biology , 2008, SFM.

[12]  Emmanuelle Caron,et al.  Dissociation of Recruitment and Activation of the Small G-protein Rac during Fcγ Receptor-mediated Phagocytosis* , 2006, Journal of Biological Chemistry.

[13]  Luca Cardelli,et al.  Efficient, Correct Simulation of Biological Processes in the Stochastic Pi-calculus , 2007, CMSB.

[14]  Luca Cardelli,et al.  A Process Model of Actin Polymerisation , 2009, Electron. Notes Theor. Comput. Sci..

[15]  Davide Sangiorgi,et al.  Communicating and Mobile Systems: the π-calculus, , 2000 .

[16]  J. Swanson,et al.  The coordination of signaling during Fc receptor‐mediated phagocytosis , 2004, Journal of leukocyte biology.

[17]  J. Weinstein,et al.  Depicting combinatorial complexity with the molecular interaction map notation , 2006, Molecular systems biology.

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

[19]  Vincent Danos,et al.  Rule-Based Modelling of Cellular Signalling , 2007, CONCUR.

[20]  Vincent Danos,et al.  Internal coarse-graining of molecular systems , 2009, Proceedings of the National Academy of Sciences.

[21]  Cosimo Laneve,et al.  From Biochemistry to Stochastic Processes , 2009, Electron. Notes Theor. Comput. Sci..

[22]  D. Koshland,et al.  An amplified sensitivity arising from covalent modification in biological systems. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Vincent Danos Agile Modelling of Cellular Signalling , 2008 .

[24]  Vincent Danos,et al.  Abstract Interpretation of Cellular Signalling Networks , 2008, VMCAI.

[25]  Vincent Danos,et al.  Rule-Based Modelling, Symmetries, Refinements , 2008, FMSB.