Biomedical Engineering Reference
In-Depth Information
to both the number of parameters included in the model and model sensitivity to
parameters value variations. Among the parameter estimation techniques, the evo-
lutionary computations proved to be a good solution to handle this task. As the
number of parameters increases, it is crucial to use high performance computing or
high throughput computing to find candidate solutions to this problem.
Acknowledgements
The authors are supported by the NET2DRUG, MIUR-FIRB LITBIO
(RBLA0332RH),
ITALBIONET
(RBPR05ZK2Z),
BIOPOPGEN
(RBIN064YAT)
and
CNR-
BIOINFORMATICS initiatives.
References
1. Alfieri, R., Merelli, I., Mosca, E., Milanesi, L.: A data integration approach for cell cycle
analysis oriented to model simulation in systems biology. BMC Syst Biol 1 , 35 (2007).
doi:10.1186/1752-0509-1-35. http://dx.doi.org/10.1186/1752-0509-1-35
2. Back, T.: Evolution strategies: an alternative evolutionary algorithm. In: Artificial evolution,
Lecture Notes in Computer Science, vol. 1063, pp. 3-20. Springer, Berlin (1995)
3. Balsa-Canto, E., Alonso, A.A., Banga, J.R.: Dynamic optimization of bioprocess: deterministic
and stochastic strategies. In: Proceedings of ACoFop IV (1998)
4. Cao, Y., Gillespie, D.T., Petzold, L.R.: Efficient step size selection for the tau-leaping simula-
tion method. J Chem Phys 124 (4), 044109 (2006). doi:10.1063/1.2159468. http://dx.doi.org/
10.1063/1.2159468
5. Chalmers, D.J.: The re-emergence of emergence. In: Chap. Strong and weak emergence.
Oxford University Press, London (2006)
6. Conrad, E.D., Tyson, J.J.: System modelling in cellular biology: from concepts to nuts and
bolt. In: Chap. Modelling molecular interaction networks with nonlinear ordinary differential
equations, pp. 97-125. MIT, MA (2006)
7. Corning, P.A.: Holistic Darwinism: synergy, cybernetics, and the bioeconomics of evolution.
University of Chicago Press, IL (2005)
8. Dawkins, R.: Climbing mount improbable (1997)
9. Faisal, A.A., Selen, L.P.J., Wolpert, D.M.: Noise in the nervous system. Nat Rev Neurosci
9 (4), 292-303 (2008). doi:10.1038/nrn2258. http://dx.doi.org/10.1038/nrn2258
10. Fogel, D.B.: Evolutionary computation: toward a new philosophy of machine intelligence, 3rd
edn. Wiley, NY (2006)
11. Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions.
J Phys Chem
81 (25), 2340-2361 (1977). http://dx.doi.org/10.1021/j100540a008
12. Gillespie, D.T., Petzold, L.R.: System modeling in cellular biology, from concepts to nuts and
bolts. In: Chap. Numerical simulation for biochemical kinetics, pp. 331-353. MIT, MA (2006)
13. Hoffmeister, F., Back, T.: Genetic algorithms and evolution strategies: similarities and differ-
ences. In: Lecture notes in computer science, vol. 496, pp. 455-469. Springer, Berlin (1991)
14. Kitano, H.: Systems biology: a brief overview. Science 295 (5560), 1662-1664 (2002)
15. Klamt, S., Stelling, J.: System modelling in cellular biology: from concepts to nuts and bolt.
In: Chap. Stoichiometric and constraint-based modeling, pp. 73-96. MIT, MA (2006)
16. Leloup, J.C., Goldbeter, A.: Modeling the circadian clock: from molecular mechanism to phys-
iological disorders. Bioessays 30 (6), 590-600 (2008). doi:10.1002/bies.20762. http://dx.doi.
org/10.1002/bies.20762
17. Liebermeister, W., Klipp, E.: Biochemical networks with uncertain parameters.
In: Systems
Biology, IEE Proceedings, vol. 152, pp. 97-107 (2005)
18. Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways:
a comparison of global optimization methods. Genome Res 13 (11), 2467-2474 (2003).
doi:10.1101/gr.1262503. http://dx.doi.org/10.1101/gr.1262503
Search WWH ::




Custom Search