When the optimal is not the best: Parameter estimation in complex biological models
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...
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paper:paper_19326203_v5_n10_p_FernandezSlezak2023-06-08T16:30:52Z When the optimal is not the best: Parameter estimation in complex biological models Fernández Slezak, Diego Suárez, Cecilia Ana 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. 2010 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19326203_v5_n10_p_FernandezSlezak http://hdl.handle.net/20.500.12110/paper_19326203_v5_n10_p_FernandezSlezak |
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Universidad de Buenos Aires |
| institution_str |
I-28 |
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R-134 |
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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 |
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article bioinformatics mathematical model mathematical parameters process optimization tumor growth Cell Division Humans Models, Biological Neoplasms Fernández Slezak, Diego Suárez, Cecilia Ana 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. |
| author |
Fernández Slezak, Diego Suárez, Cecilia Ana |
| author_facet |
Fernández Slezak, Diego Suárez, Cecilia Ana |
| author_sort |
Fernández Slezak, Diego |
| 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 |
| publishDate |
2010 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19326203_v5_n10_p_FernandezSlezak http://hdl.handle.net/20.500.12110/paper_19326203_v5_n10_p_FernandezSlezak |
| work_keys_str_mv |
AT fernandezslezakdiego whentheoptimalisnotthebestparameterestimationincomplexbiologicalmodels AT suarezceciliaana whentheoptimalisnotthebestparameterestimationincomplexbiologicalmodels |
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1768544059440234496 |