Integrated biological systems modeling: challenges and opportunities
Jialiang WU, Eberhard VOIT
Integrated biological systems modeling: challenges and opportunities
Most biological systems are by nature hybrids consist of interacting discrete and continuous components, which may even operate on different time scales. Therefore, it is desirable to establish modeling frameworks that are capable of combining deterministic and stochastic, discrete and continuous, as well as multi-timescale features. In the context of molecular systems biology, an example for the need of such a combination is the investigation of integrated biological pathways that contain gene regulatory, metabolic and signaling components, which may operate on different time scales and involve on-off switches as well as stochastic effects. The implementation of integrated hybrid systems is not trivial because most software is limited to one or the other of the dichotomies above. In this study, we first review the motivation for hybrid modeling. Secondly, by using the example of a toggle switch model, we illustrate a recently developed modeling framework that is based on the combination of biochemical systems theory (BST) and hybrid functional Petri nets (HFPN). Finally, we discuss remaining challenges and future opportunities.
biochemical systems theory / Petri net / hybrid modeling / hybrid functional Petri net / toggle switch / canonical modeling / stochastic delay
[1] |
Savageau M A. Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. Journal of Theoretical Biology, 1969, 25(3): 365-369
CrossRef
Google scholar
|
[2] |
Savageau M A. Biochemical systems analysis. II. The steady-state solutions for an n-pool system using a power-law approximation. Journal of Theoretical Biology, 1969, 25(3): 370-379
CrossRef
Google scholar
|
[3] |
Savageau M A. Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology. Reading: Addison-Wesley, 1976
|
[4] |
Voit E O. Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists. Cambridge: Cambridge University Press, 2000
|
[5] |
Torres N V, Voit E O. Pathway Analysis and Optimization inMetabolic Engineering. Cambridge: Cambridge University Press, 2002
|
[6] |
Kacser H, Burns J A. The control of flux. Symp. Soc. Exp. Biol., 1973, 27: 65-104
|
[7] |
Heinrich R, Rapoport T A. A linear steady-state treatment of enzymatic chains: General properties, control and effector strength. European Journal of Biochemistry, 1974, 42: 89-95
CrossRef
Google scholar
|
[8] |
Fell D A. Understanding the Control of Metabolism. London: Portland Press, 1997
|
[9] |
Hatzimanikatis V, Bailey J. MCA has more to say. Journal of Theoretical Biology, 1996, 182: 233-242
CrossRef
Google scholar
|
[10] |
Visser D, Heijnen J J. The mathematics of metabolic control analysis revisited. Metabolic Engineering, 2002, 4(2): 114-123
CrossRef
Google scholar
|
[11] |
Wang F-S, Ko C-L,
CrossRef
Google scholar
|
[12] |
Goss P J E, Peccoud J. Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. In: Proceedings of the National Academy of Sciences, 1998, 95: 6750-6755
CrossRef
Google scholar
|
[13] |
Haas P J. Stochastic Petri Nets. New York: Springer-Verlag, 2002
|
[14] |
D’Argenio P R, Katoen J-P. A theory of stochastic systems part I: Stochastic automata. Information and Computation, 2005, 203(1): 1-38
CrossRef
Google scholar
|
[15] |
Gillespie D T. A rigorous derivation of the chemical master equation. Physica A, 1992, 188: 404-425
CrossRef
Google scholar
|
[16] |
Gillespie D T. Stochastic simulation of chemical kinetics. Annual Review of Physical Chemistry, 2007, 58(1): 35-55
CrossRef
Google scholar
|
[17] |
Matsuno H, Tanaka Y,
|
[18] |
Wu J, Voit E O. Hybrid modeling in biochemical systems theory by means of functional Petri nets. Journal of Bioinformatics and Computational Biology, 2009 (in press)
CrossRef
Google scholar
|
[19] |
Elowitz M B, Levine A J,
CrossRef
Google scholar
|
[20] |
Blake W J, Kaern M,
CrossRef
Google scholar
|
[21] |
McAdams H H, Arkin A P. Stochastic mechanisms in gene expression. In: Proceedings of National Academy of Sciences, 1997, 94: 814-819
CrossRef
Google scholar
|
[22] |
Schnell S, Turner T E. Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws. Progress in Biophysics and Molecular Biology, 2004, 85: 235-260
CrossRef
Google scholar
|
[23] |
Minton A P. Molecular crowding and molecular recognition. Journal Molecular Recognition, 1993, 6: 211-214
CrossRef
Google scholar
|
[24] |
Minton A P. Molecular crowding: analysis of effects of high concentrations of inert cosolutes on biochemical equilibria and rates in terms of volume exclusion. Methods Enzymol. 1998, 295: 127-149
CrossRef
Google scholar
|
[25] |
Luby-Phelps K, Castle P E,
CrossRef
Google scholar
|
[26] |
Scalettar B A, Abney J R,
CrossRef
Google scholar
|
[27] |
Verkman A S. Solute and macromolecule diffusion in cellular aqueous compartments. Trends in Biochemical Science, 2002, 27: 27-33
CrossRef
Google scholar
|
[28] |
Clegg J S. Properties and metabolism of the aqueous cytoplasm and its boundaries. American Journal Physiology, 1984, 246: R133-R151
|
[29] |
Srere P, Jones M E, Matthews C K, eds. Structural and Organizational Aspects of Metabolic Regulation. New York: Alan R. Liss, 1989
|
[30] |
Gillespie D T. A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computation Physics, 1976, 22: 403-434
CrossRef
Google scholar
|
[31] |
Qian H, Elson E L. Single-molecule enzymology: stochastic Michaelis-Menten kinetics. Biophysical Chemistry, 2002, 101-102: 565-576
CrossRef
Google scholar
|
[32] |
Kuthan H. Self-organisation and orderly processes by individual protein complexes in the bacterial cell. Progress in Biophysics andMolecular Biology, 2001, 75: 1-17
|
[33] |
Hirata H, Yoshiura S,
CrossRef
Google scholar
|
[34] |
Monk N A. Oscillatory expression of Hes1, p53, and NF-kappaB driven by transcriptional time delays. Current Biology, 2003, 13(16): 1409-1413
CrossRef
Google scholar
|
[35] |
Tian T, Burrage K,
CrossRef
Google scholar
|
[36] |
Kiehl T R, Mattheyses R M,
CrossRef
Google scholar
|
[37] |
Mocek W T, Rudnicki R,
CrossRef
Google scholar
|
[38] |
Miyano S. Cell Illustrator website. http://www.cellillustrator.com /, 2008
|
[39] |
Gardner T S, Cantor C R,
CrossRef
Google scholar
|
[40] |
Tian T, Burrage K. Stochastic models for regulatory networks of the genetic toggle switch. In: Proceedings of the National Academy of Sciences, 2006, 103(22): 8372-8377
CrossRef
Google scholar
|
[41] |
Savageau M A, Voit E O. Recasting nonlinear differential equations as S-systems: a canonical nonlinear form. Mathematical Biosciences, 1987, 87(1): 31-113
CrossRef
Google scholar
|
[42] |
Voit E O. Smooth bistable S-systems. In: Proceedings of IEEE Systems Biology, 2005, 152: 207-213
CrossRef
Google scholar
|
[43] |
Clarke E M, Grumberg O,
|
[44] |
Nagasaki M, Yamaguchi R,
|
[45] |
Neapolitan R E. Learning Bayesian Networks. Prentice Hall, 2003
|
[46] |
Jiang X, Cheng D C,
|
[47] |
Williams B C, Millar W. Decompositional, Model-based learning and its Analogy to Diagnosis. AAAI/IAAI, 1998
|
[48] |
Koh G, Teong H,
CrossRef
Google scholar
|
[49] |
Alves R, Savageau MA. Extending the method of mathematically controlled comparison to include numerical comparisons. Bioinformatics, 2000, 16(9): 786-798
CrossRef
Google scholar
|
/
〈 | 〉 |