Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

PLoS ONE

Volumn 8, Issue 3, 2013, Pages

  Sadot, Avital ^a   Sarbu, Septimia ^b   Kesseli, Juha ^b   Amir Kroll, Hila ^c   Zhang, Wei ^d   Nykter, Matti ^b   Shmulevich, Ilya ^a  

^a INSTITUTE FOR SYSTEMS BIOLOGY   (United States)

^b TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^c UNIVERSITY MEDICAL CENTER HAMBURG EPPENDORF   (Germany)

^d UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOLOGICAL MODEL; CELL DIFFERENTIATION; CELL FUNCTION; COMPUTER SIMULATION; CONCEPTUAL FRAMEWORK; DECISION MAKING; GENE SILENCING; IMMUNOCOMPETENT CELL; MOLECULAR DYNAMICS; PREDICTION; SYSTEM ANALYSIS; SYSTEMS BIOLOGY; THEORETICAL MODEL;

ALGORITHMS; HUMANS; IMMUNITY, CELLULAR; INFORMATION THEORY; MODELS, BIOLOGICAL; MOLECULAR DYNAMICS SIMULATION;

EID: 84875089912     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0059303     Document Type: Article

References (47)

1. Harel D, (2003) A Grand Challenge for Computing: Full Reactive Modeling of a Multi-Cellular Animal. Bull Eur Assoc Theor Comput Sci 81: 226-235.
2. Harel D, Pnueli A (1985) On the Development of Reactive Systems. Logics and Models of Concurrent Systems F: 477-498.
3. Fisher J, Henzinger TA, (2007) Executable cell biology. Nature biotechnology 25: 1239-1249.
4. Albert R, Othmer HG, (2003) The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. Journal of theoretical biology 223: 1-18.
5. Barjis J, Barjis I (1999) Formalization of the Protein Production by Means of Petri Nets. Information Intelligence and Systems: 4-9.
6. Calder M, Duguid A, Gilmore S, Hillston J. Stronger Computational Modelling of Signalling Pathways Using Both Continous and Discrete-State Methods. In: Priami C, editor; 2006; Trento, Italy. LNCS. 63-78.
7. Chaouiya C, (2007) Petri net modelling of biological networks. Briefings in bioinformatics 8: 210-219.
8. de Jong H, (2002) Modeling and simulation of genetic regulatory systems: a literature review. Journal of computational biology: a journal of computational molecular cell biology 9: 67-103.
9. D'Haeseleer P, Liang S, Somogyi R, (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16: 707-726.
10. Efroni S, Harel D, Cohen IR, (2007) Emergent dynamics of thymocyte development and lineage determination. PLoS computational biology 3: e13.
11. Fisher J, Piterman N, Hajnal A, Henzinger TA, (2007) Predictive modeling of signaling crosstalk during C. elegans vulval development. PLoS computational biology 3: e92.
12. Fisher J, Piterman N, Hubbard EJ, Stern MJ, Harel D, (2005) Computational insights into Caenorhabditis elegans vulval development. Proceedings of the National Academy of Sciences of the United States of America 102: 1951-1956.
13. Friedman N, Linial M, Nachman I, Pe'er D, (2000) Using Bayesian networks to analyze expression data. Journal of computational biology: a journal of computational molecular cell biology 7: 601-620.
14. Ideker TE, Thorsson V, Karp RM (2000) Discovery of regulatory interactions through perturbation: inference and experimental design. Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing: 305-316.
15. Kam N, Cohen IR, Harel D (2001) The Immune System as a Reactive System: Modeling T Cell Activation with Statecharts. Proc Visual Languages and Formal Methods: 5-7.
16. Lakin MR, Loic P, Phillips A, (2012) Stochastic Simulation of Multiple Process Calculi for Biology. Theoretical Computer Science 431: 181-206.
17. Li C, Ge QW, Nakata M, Matsuno H, Miyano S, (2007) Modelling and simulation of signal transductions in an apoptosis pathway by using timed Petri nets. Journal of biosciences 32: 113-127.
18. Li F, Long T, Lu Y, Ouyang Q, Tang C, (2004) The yeast cell-cycle network is robustly designed. Proceedings of the National Academy of Sciences of the United States of America 101: 4781-4786.
19. Priami C, Regev A, Shapiro EY, Silverman W, (2001) Application of a Stochastic Name-Passing Calculus to representation and Simulation of Molecular Processes. Inf Process Lett 80: 25-31.
20. Regev A, Silverman W, Shapiro E (2001) Representation and simulation of biochemical processes using the pi-calculus process algebra. Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing: 459-470.
21. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP, (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308: 523-529.
22. Sadot A, Fisher J, Barak D, Admanit Y, Stern MJ, et al. (2008) Toward verified biological models. IEEE/ACM transactions on computational biology and bioinformatics/IEEE, ACM 5: 223-234.
23. Setty Y, Cohen IR, Dor Y, Harel D, (2008) Four-dimensional realistic modeling of pancreatic organogenesis. Proceedings of the National Academy of Sciences of the United States of America 105: 20374-20379.
24. Simao E, Remy E, Thieffry D, Chaouiya C, (2005) Qualitative modelling of regulated metabolic pathways: application to the tryptophan biosynthesis in E.coli. Bioinformatics 21Suppl 2: ii190-196.
25. Meiss JD (2007) Differential Dynamical Systems; Haberman R, editor. Philadelphia: Society for Industrial and Applied Mathematics. 412 p.
26. Luque B, Sole V, (2000) Lyapunov exponents in random Boolean networks. Physica A: Statistical Mechanics and its Applications 284: 33-45.
27. Cover TM, Thomas JA (1991) Elements of Information Theory. Hoboken, New Jersey: John Wiley and Sons, Inc.
28. Li M, Vitanyi P (2008) An Introduction to Kolmogorov Complexity and its Applications: Springer Verlag.
29. Cilibrasi R, Vitanyi PMB, (2005) Clustering by Compression. IEEE Transactions on Information Theory 51: 1523-1545.
30. Nykter M, Price ND, Aldana M, Ramsey SA, Kauffman SA, et al. (2008) Gene expression dynamics in the macrophage exhibit criticality. Proceedings of the National Academy of Sciences of the United States of America 105: 1897-1900.
31. Nykter M, Price ND, Larjo A, Aho T, Kauffman SA, et al. (2008) Critical networks exhibit maximal information diversity in structure-dynamics relationships. Physical review letters 100: 058702.
32. Galas DJ, Nykter M, Carter GW, Price ND, Shmulevich I, (2010) Biological Information as Set-Based Complexity. IEEE Transactions on Information Theory 56: 667-677.
33. Amir-Kroll H, Sadot A, Zanin-Zhorov A, Cohen IR, Harel D, et al. (2012) Analyzing Immune Decision Making and Immune Cell Behavior by a Generic Cellular Interaction-Based Modeling Method (GemCell). Submitted.
34. Sadot A (2008) On Specific and Generic Modeling of Complex Biological Systems. Rehovot: The Weizmann Institute for Science. 77 p. http://www.cs.tut.fi/sgn/csb/execmodel.
35. Amir-Kroll H, Sadot A, Cohen IR, Harel D, (2008) GemCell: A generic Platform for Modeling Multi-Cellular Biological Systems. Theoretical Computer Science 391: 276-290.
36. Harel D, (1987) Statecharts: a visual formalism for complex systems. Science of Computer Programming 8: 231-274.
37. IBM website. Available: http://www.ibm.com/software/awdtools/rhapsody/Accssed 2013 Feb 13.
38. Li M, Chen X, Li X, Ma B, Vitanyi PMB, (2004) The Similarity Metric. IEEE Transactions on Information Theory 50: 3250-3264.
39. Botev ZI, Grotowski JF, Kroese DP, (2010) Kernel Density Estimation via Diffusion. The Annals of Statistics 38: 2916-2957.
40. Sakaguchi S, Yamaguchi T, Nomura T, Ono M, (2008) Regulatory T cells and immune tolerance. Cell 133: 775-787.
41. Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, et al. (2012) A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell 150: 389-401.
42. Shevach EM, (2009) Mechanisms of foxp3+ T regulatory cell-mediated suppression. Immunity 30: 636-645.
43. von Boehmer H, (2005) Mechanisms of suppression by suppressor T cells. Nature immunology 6: 338-344.
44. Wing K, Onishi Y, Prieto-Martin P, Yamaguchi T, Miyara M, et al. (2008) CTLA-4 control over Foxp3+ regulatory T cell function. Science 322: 271-275.
45. Paust S, Lu L, McCarty N, Cantor H, (2004) Engagement of B7 on effector T cells by regulatory T cells prevents autoimmune disease. Proceedings of the National Academy of Sciences of the United States of America 101: 10398-10403.
46. Rubtsov YP, Rasmussen JP, Chi EY, Fontenot J, Castelli L, et al. (2008) Regulatory T cell-derived interleukin-10 limits inflammation at environmental interfaces. Immunity 28: 546-558.
47. Zanin-Zhorov A, Cahalon L, Tal G, Margalit R, Lider O, et al. (2006) Heat shock protein 60 enhances CD4+ CD25+ regulatory T cell function via innate TLR2 signaling. The Journal of clinical investigation 116: 2022-2032.