Learning stochastic process-based models of dynamical systems from knowledge and data

BMC Systems Biology

Volumn 10, Issue 1, 2016, Pages

  Tanevski, Jovan ^a   Todorovski, Ljupčo ^b   Džeroski, Sašo ^a  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Compartmental epidemiological models; Dynamical systems; Genetic regulatory networks; Process based modeling; Stochastic models; Structural uncertainty

Indexed keywords

BIOLOGICAL MODEL; BIOLOGY; EPIDEMIC; GENE REGULATORY NETWORK; HUMAN; INFLUENZA, HUMAN; KINETICS; MARKOV CHAIN; PLAGUE; PROCEDURES; UNCERTAINTY;

COMPUTATIONAL BIOLOGY; DISEASE OUTBREAKS; GENE REGULATORY NETWORKS; HUMANS; INFLUENZA, HUMAN; KINETICS; MODELS, BIOLOGICAL; PLAGUE; STOCHASTIC PROCESSES; UNCERTAINTY;

EID: 84962339458     PISSN: None     EISSN: 17520509     Source Type: Journal    
DOI: 10.1186/s12918-016-0273-4     Document Type: Article

References (50)

1. Wilkinson DJ. Stochastic Modelling for Systems Biology. Boca Raton: CRC Press; 2006.
2. Lecca P, Laurenzi I, Jordan F. Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology. Cambridge: Woodhead Publishing; 2013.
3. McAdams HH, Arkin A. Stochastic mechanisms in gene expression. Proc Natl Acad Sci. 1997; 94(3):814-9.
4. Arkin A, Ross J, McAdams HH. Stochastic kinetic analysis of developmental pathway bifurcation in phage λ - infected escherichia coli cells. Genetics. 1998; 149(4):1633-48.
5. Samoilov M, Plyasunov S, Arkin AP. Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proc Natl Acad Sci. 2005; 102(7):2310-315.
6. Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf MPH. Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems. J R Soc Interface. 2009; 6:187-202.
7. Džeroski S, Todorovski L. Equation discovery for systems biology: finding the structure and dynamics of biological networks from time course data. Curr Opin Biotechnol. 2008; 19(4):360-8.
8. Regev A, Shapiro E. Cellular abstractions: cells as computation. Nature. 2002; 419(6905):343.
9. Gillespie DT. Stochastic simulation of chemical kinetics. Annu Rev Phys Chem. 2007; 58(1):35-55.
10. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, the rest of the SBML Forum, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novère N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J. The systems biology markup language (sbml): a medium for representation and exchange of biochemical network models. Bioinformatics. 2003; 19(4):524-31.
11. Faeder JR, Blinov ML, Hlavacek WS. Rule-based modeling of biochemical systems with BioNetGen. In: Systems Biology. Methods in Molecular Biology. New York: Humana Press: 2009. p. 113-67.
12. Danos V, Feret J, Fontana W, Harmer R, Krivine J. Rule-Based Modelling, Symmetries, Refinements In: Fisher J, editor. Formal Methods in Systems Biology. Lecture Notes in Computer Science. Berlin Heidelberg: Springer: 2008. p. 103-22.
13. Priami C, Quaglia P. Modelling the dynamics of biosystems. Brief Bioinform. 2004; 5(3):259-69.
14. Clark A, Gilmore S, Hillston J, Tribastone M. Stochastic process algebras. In: Formal Methods for Performance Evaluation. Lecture Notes in Computer Science. Berlin Heidelberg: Springer: 2007. p. 132-79.
15. Blossey R, Cardelli L, Phillips A. A compositional approach to the stochastic dynamics of gene networks. Trans Comput Syst Biol. 2006; 3939(3939):99-122.
16. Ciocchetta F, Hillston J. Bio-pepa: A framework for the modelling and analysis of biological systems. Theor Comput Sci. 2009; 410(33-34):3065-084.
17. Priami C, Quaglia P. Beta binders for biological interactions. In: Computational Methods in Systems Biology. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer: 2005. p. 20-33.
18. Bortolussi L, Policriti A. Modeling biological systems in stochastic concurrent constraint programming. Constraints. 2008; 13(1-2):66-90.
19. Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U. Copasi - a complex pathway simulator. Bioinformatics. 2006; 22(24):3067-074.
20. Mathworks SimBiology toolbox. http://www.mathworks.com/products/simbiology/. Accessed 01 Feb 2016.
21. Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D. How to infer gene networks from expression profiles. Mol Syst Biol. 2006; 3:78-8.
22. Wahl SA, Haunschild MD, Oldiges M, Wiechert W. Unravelling the regulatory structure of biochemical networks using stimulus response experiments and large-scale model selection. IEE Proc Syst Biol. 2006; 153:275-8510.
23. Oates CJ, Dondelinger F, Bayani N, Korkola J, Gray JW, Mukherjee S. Causal network inference using biochemical kinetics. Bioinformatics. 2014; 30(17):468-74.
24. Henriques D, Rocha M, Saez-Rodriguez J, Banga JR. Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach. Bioinformatics. 2015; 31(18):2999-3007.
25. Bonneau R, Reiss D, Shannon P, Facciotti M, Hood L, Baliga N, Thorsson V. The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 2006; 7(5):36.
26. Langley P, Simon HA, Bradshaw GL, Zytkow JM. Scientific Discovery: Computational Explorations of the Creative Processes. Cambridge: MIT Press; 1987.
27. Džeroski S, Todorovski L. Encoding and using domain knowledge on population dynamics for equation discovery In: Magnani L, Nersessian NJ, editors. Logical and Computational Aspects of Model-Based Reasoning. Netherlands: Springer: 2002. p. 227-47.
28. Bridewell W, Langley P, Todorovski L, Džeroski S. Inductive process modelling. Mach Learn. 2008; 71:109-30.
29. Todorovski L, Bridewell W, Shiran O, Langley P. Inducing hierarchical process models in dynamic domains. In: Proceedings of the Twentieth National Conference on Artificial Intelligence. Pittsburgh: AAAI Press: 2005. p. 892-7.
30. Čerepnalkoski D. Process-based models of dynamical systems: representation and induction. PhD thesis, Jožef Stefan International Postgraduate School, Ljubljana, Slovenia. 2013.
31. Džeroski S, Todorovski L. Modeling the dynamics of biological networks from time course data In: Choi S, editor. Systems Biology for Signaling Networks. Berlin Heidelberg: Springer: 2010. p. 275-94.
32. Tanevski J, Todorovski L, Kalaidzidis Y, Dzeroski S. Domain-specific model selection for structural identification of the rab5-rab7 dynamics in endocytosis. BMC Syst Biol. 2015; 9(1):31.
33. Elowitz MB, Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature. 2000; 403:335-8.
34. Sanft KR, Gillespie DT, Petzold LR. Legitimacy of the stochastic michaelis-menten approximation. IET Syst Biol. 2011; 5(1):58-69.
35. Gillespie DT. A rigorous derivation of the chemical master equation. Physica A Stat Mech Appl. 1992; 188(1-3):404-25.
36. Gillespie DT. The chemical langevin equation. J Chem Phys. 2000; 113(1):297-306.
37. Rice JA. Mathematical Statistics and Data Analysis. Boston: Cengage Learning; 2006.
38. Todorovski L, Džeroski S. Integrating domain knowledge in equation discovery In: Džeroski S, Todorovski L, editors. Computational Discovery of Scientific Knowledge. Berlin Heidelberg: Springer: 2007. p. 69-97.
39. Bridewell W, Langley P. Two kinds of knowledge in scientific discovery. Top Cogn Sci. 2010; 2(1):36-52.
40. Čerepnalkoski D, Taškova K, Todorovski L, Atanasova N, Džeroski S. The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems. Ecol Model. 2012; 245:136-65.
41. Ramsey S, Orrell D, Bolouri H. Dizzy: Stochastic simulation of large-scale genetic regulatory networks. J Bioinforma Comput Biol. 2005; 03(02):415-36.
42. Price K, Storn RM, Lampinen JA. Differential Evolution: A Practical Approach to Global Optimization. Berlin Heidelberg: Springer; 2005.
43. Tomshine J, Kaznessis YN. Optimization of a stochastically simulated gene network model via simulated annealing. Biophys J. 2006; 91(9):3196-205.
44. Brauer F, van den Driessche P, Wu J, Allen LJS. Mathematical Epidemiology. Berlin Heidelberg: Springer; 2008.
45. Ragget GF. Modelling the eyam plague. IMA J. 1982; 18:221-6.
46. Hammond BJ, Tyrrell DAJ. A mathematical model of common-cold epidemics on tristan da cunha. J Hyg. 1971; 69:423-33.
47. Shibli M, Gooch S, Lewis HE, Tyrrell DAJ. Common colds on tristan da cunha. J Hyg. 1971; 69:255-62.
48. Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP. Universally sloppy parameter sensitivities in systems biology models. PLoS Comput Biol. 2007; 3(10):1-8.
49. Moles CG, Mendes P, Banga JR. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 2003; 13:2467-474.
50. Sun J, Garibaldi JM, Hodgman C. Parameter estimation using metaheuristics in systems biology: A comprehensive review. IEEE/ACM Trans Compu Biol Bioinforma. 2012; 9(1):185-202.