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Abstract:

Background: The vast computational resources that became available during the past decade enabled the development and simulation of increasingly complex mathematical models of cancer growth. These models typically involve many free parameters whose determination is a substantial obstacle to model development. Direct measurement of biochemical parameters in vivo is often difficult and sometimes impracticable, while fitting them under data-poor onditions may result in biologically implausible values. Results: We discuss different methodological approaches to estimate parameters in complex biological models. We make use of the high computational power of the Blue Gene technology to perform an extensive study of the parameter space in a model of avascular tumor growth. We explicitly show that the landscape of the cost function used to optimize the model to the data has a very rugged surface in parameter space. This cost function has many local minima with unrealistic solutions, including the global minimum corresponding to the best fit. Conclusions: The case studied in this paper shows one example in which model parameters that optimally fit the data are not necessarily the best ones from a biological point of view. To avoid force-fitting a model to a dataset, we propose that the best model parameters should be found by choosing, among suboptimal parameters, those that match criteria other than the ones used to fit the model. We also conclude that the model, data and optimization approach form a new complex system and point to the need of a theory that addresses this problem more generally. © 2010 Fernandez Slezak et al.

Registro:

Documento: Artículo
Título:When the optimal is not the best: Parameter estimation in complex biological models
Autor:Fernández Slezak, D.; Suárez, C.; Cecchi, G.A.; Marshall, G.; Stolovitzky, G.
Filiación:Laboratorio de Sistemas Complejos, Depto. de Computación, FCEyN, Buenos Aires University (UBA), Buenos Aires, Argentina
Computational Biology Center, T. J. Watson Research Center, IBM, Yorktown Heights, New York, United States
Palabras clave:article; bioinformatics; mathematical model; mathematical parameters; process optimization; tumor growth; Cell Division; Humans; Models, Biological; Neoplasms
Año:2010
Volumen:5
Número:10
DOI: http://dx.doi.org/10.1371/journal.pone.0013283
Título revista:PLoS ONE
Título revista abreviado:PLoS ONE
ISSN:19326203
PDF:https://bibliotecadigital.exactas.uba.ar/download/paper/paper_19326203_v5_n10_p_FernandezSlezak.pdf
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19326203_v5_n10_p_FernandezSlezak

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Citas:

---------- APA ----------
Fernández Slezak, D., Suárez, C., Cecchi, G.A., Marshall, G. & Stolovitzky, G. (2010) . When the optimal is not the best: Parameter estimation in complex biological models. PLoS ONE, 5(10).
http://dx.doi.org/10.1371/journal.pone.0013283
---------- CHICAGO ----------
Fernández Slezak, D., Suárez, C., Cecchi, G.A., Marshall, G., Stolovitzky, G. "When the optimal is not the best: Parameter estimation in complex biological models" . PLoS ONE 5, no. 10 (2010).
http://dx.doi.org/10.1371/journal.pone.0013283
---------- MLA ----------
Fernández Slezak, D., Suárez, C., Cecchi, G.A., Marshall, G., Stolovitzky, G. "When the optimal is not the best: Parameter estimation in complex biological models" . PLoS ONE, vol. 5, no. 10, 2010.
http://dx.doi.org/10.1371/journal.pone.0013283
---------- VANCOUVER ----------
Fernández Slezak, D., Suárez, C., Cecchi, G.A., Marshall, G., Stolovitzky, G. When the optimal is not the best: Parameter estimation in complex biological models. PLoS ONE. 2010;5(10).
http://dx.doi.org/10.1371/journal.pone.0013283