When the optimal is not the best: Parameter estimation in complex biological models

Mostrar todas las versiones(3)

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 de...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Fernández Slezak, D., Suárez, C., Cecchi, G.A., Marshall, G., Stolovitzky, G.
Formato: JOUR
Materias:
article
bioinformatics
mathematical model
mathematical parameters
process optimization
tumor growth
Cell Division
Humans
Models, Biological
Neoplasms
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_19326203_v5_n10_p_FernandezSlezak
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_19326203_v5_n10_p_FernandezSlezak
record_format dspace
spelling todo:paper_19326203_v5_n10_p_FernandezSlezak2023-10-03T16:35:02Z When the optimal is not the best: Parameter estimation in complex biological models Fernández Slezak, D. Suárez, C. Cecchi, G.A. Marshall, G. Stolovitzky, G. article bioinformatics mathematical model mathematical parameters process optimization tumor growth Cell Division Humans Models, Biological Neoplasms 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. Fil:Fernández Slezak, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Suárez, C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_19326203_v5_n10_p_FernandezSlezak
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic article
bioinformatics
mathematical model
mathematical parameters
process optimization
tumor growth
Cell Division
Humans
Models, Biological
Neoplasms
spellingShingle article
bioinformatics
mathematical model
mathematical parameters
process optimization
tumor growth
Cell Division
Humans
Models, Biological
Neoplasms
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
topic_facet article
bioinformatics
mathematical model
mathematical parameters
process optimization
tumor growth
Cell Division
Humans
Models, Biological
Neoplasms
description 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.
format JOUR
author Fernández Slezak, D.
Suárez, C.
Cecchi, G.A.
Marshall, G.
Stolovitzky, G.
author_facet Fernández Slezak, D.
Suárez, C.
Cecchi, G.A.
Marshall, G.
Stolovitzky, G.
author_sort Fernández Slezak, D.
title When the optimal is not the best: Parameter estimation in complex biological models
title_short When the optimal is not the best: Parameter estimation in complex biological models
title_full When the optimal is not the best: Parameter estimation in complex biological models
title_fullStr When the optimal is not the best: Parameter estimation in complex biological models
title_full_unstemmed When the optimal is not the best: Parameter estimation in complex biological models
title_sort when the optimal is not the best: parameter estimation in complex biological models
url http://hdl.handle.net/20.500.12110/paper_19326203_v5_n10_p_FernandezSlezak
work_keys_str_mv AT fernandezslezakd whentheoptimalisnotthebestparameterestimationincomplexbiologicalmodels
AT suarezc whentheoptimalisnotthebestparameterestimationincomplexbiologicalmodels
AT cecchiga whentheoptimalisnotthebestparameterestimationincomplexbiologicalmodels
AT marshallg whentheoptimalisnotthebestparameterestimationincomplexbiologicalmodels
AT stolovitzkyg whentheoptimalisnotthebestparameterestimationincomplexbiologicalmodels
_version_ 1807317861165694976

Ejemplares similares