Franck Delaplace
ABSTRACT
Signaling and regulatory pathways coordinate multiple cellular functions in response to environmental variations. Discovering the pathways governing functionally specific responses is essential for understanding of biological systems. It aims at determining the causal cascades of regulations leading to the observed responses. Their characterization by computational methods remains an important and challenging question. The presented cabin (Causal Analysis of Biological Interaction Network) method determines a causal model view composed of a subnetwork and a set of agent states deduced from observations with regards to a model of network dynamics. The validity of the results is ensured by formally checking the conditions of correctness of a model with respect to observations. State-based and symbolic versions of the algorithm have been implemented and used for a biological case study.
- }}J. Aracena, J. Demongeot, and E. Goles. Positive and negative circuits in discrete neural networks. IEEE Transactions on Neural Networks, 15(1):77--83, 2004. Google Scholar
Digital Library
- }}F. Ay, F. Xu, and T. Kahveci. Scalable steady state analysis of Boolean biological regulatory networks. PloS one, 4(12):79--92, December 2009.Google ScholarCross Ref
- }}C. Chettaoui, F. Delaplace, P. Lescanne, M. Vestergaard, and R. Vestergaard. Rewriting game theory as a foundation for state-based models of gene regulation. In CMSB06. Springer Verlag, October 2006. Google ScholarDigital Library
- }}E. S. Coen and E. M. Meyerowitz. The war of the whorls: genetic interactions controlling flower development. Nature, 353(6339):31--7, september 1991.Google ScholarCross Ref
- }}F. Corblin, S. Tripodi, E. Fanchon, D. Ropers, and L. Trilling. A declarative constraint-based method for analyzing discrete genetic regulatory networks. Bio Systems, 98(2):91--104, 2009.Google ScholarCross Ref
- }}J. Demongeot, E. Goles, M. Morvan, M. Noual, and S. Sené. Attraction Basins as Gauges of Robustness Against Boundary Conditions in Biological Complex Systems. PloS one, (to appear), 2010.Google Scholar
- }}V. Devloo. Identification of all steady states in large networks by logical analysis. Bulletin of Mathematical Biology, 65(6):1025--1051, 2003.Google ScholarCross Ref
- }}A. Finkelstein. Computational Challenges of Systems Biology. Computer, 2004. Google ScholarDigital Library
- }}M. J. Fischer and M. O. Rabin. Super-exponential complexity of Presburger arithmetic. (TM-43), Februar 1974. Google ScholarDigital Library
- }}A. Garg, I. Xenarios, L. Mendoza, and G. Demicheli. An Efficient Method for Dynamic Analysis of Gene Regulatory Networks and in silico Gene Perturbation Experiments. In Research in Computational Molecular Biology, pages 62--76, 2007. Google ScholarDigital Library
- }}E. Goles and S. Martínez. Neural and automata networks: dynamical behavior and applications. Kluwer Academic Publishers, Norwell, MA, USA, 1990. Google ScholarDigital Library
- }}M. Kanehisa and S. Goto. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic acids research, 28(1):27--30, January 2000.Google Scholar
- }}R. Leclerc. Survival of the sparsest: robust gene networks are parsimonious. Molecular Systems Biology, 4(213):2--6, 2008.Google Scholar
- }}L. Mendoza and E. R. Alvarez-Buylla. Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis. Journal of theoretical biology, 193(2):307--19, July 1998.Google Scholar
- }}L. Mendoza, D. Thieffry, and E. R. Alvarez-Buylla. Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis. Bioinformatics (Oxford, England), 15(7--8):593--606, 1999.Google Scholar
- }}D. Noble. Genes and causation. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 366(1878):3001--15, 2008.Google Scholar
- }}Z. N. Oltvai and A.-L. Barabási. Systems biology. Life's complexity pyramid. Science (New York, N.Y.), 298(5594):763--4, 2002.Google ScholarCross Ref
- }}C. Priami. Algorithmic systems biology. Communications of the ACM, 52(5):80, May 2009. Google ScholarDigital Library
- }}W. Pugh. The Omega test: a fast and practical integer programming algorithm for dependence analysis. In Proceedings of the 1991 ACM/IEEE conference on Supercomputing, number August, pages 4--13, New York, New York, USA, 1991. ACM New York, NY, USA. Google ScholarDigital Library
- }}M. A. Schaub, T. A. Henzinger, and J. Fisher. Qualitative networks: a symbolic approach to analyze biological signaling networks. BMC systems biology, 1:4, 2007.Google Scholar
- }}T. Schlitt and A. Brazma. Modelling in molecular biology: describing transcription regulatory networks at different scales. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 361(1467):483--94, March 2006.Google Scholar
- }}T. Shiple, J. Kukula, and R. Ranjan. A comparison of Presburger engines for EFSM reachability. Lecture Notes in Computer Science, 1427:280--292, 1998. Google ScholarDigital Library
- }}cabin: Programs & Examples. www.ibisc.univ-evry.fr/~delapla/cabin.html, April 2010.Google Scholar
- }}D. Thieffry. Dynamical roles of biological regulatory circuits. Briefings in bioinformatics, 8(4):220--5, 2007.Google ScholarCross Ref
- }}D. Thieffry and R. Thomas. Dynamical behaviour of biological regulatory networks-II. Immunity control in bacteriophage lambda. Bulletin of Mathematical Biology, 57(2):277--297, 1995.Google Scholar
- }}R. Thomas. Regulatory networks seen as asynchronous automata: A logical description. Journal of Theoretical Biology, 153, 1991.Google Scholar
- }}R. Thomas, D. Thieffry, and M. Kaufman. Dynamical behaviour of biological regulatory networks I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. Bulletin of Mathematical Biology, 57(2):247--276, March 1995.Google ScholarCross Ref
- }}D. Weigel, J. Alvarez, D. R. Smyth, M. F. Yanofsky, and E. M. Meyerowitz. LEAFY controls floral meristem identity in Arabidopsis. Cell, 69(5):843--59, May 1992.Google Scholar
Index Terms
- Discrete causal model view of biological networks
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