Optimal experiment selection for parameter estimation in biological differential equation models

BMC Bioinformatics

Mark K Transtrum¹  Peng Qiu¹ 

[1] Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Cneter, Houston Texas, USA

关键词: Data fitting;    Parameter estimation;    Experimental design;    Differential equation models;    Systems biology;   

Others  :  1088189
DOI  :  10.1186/1471-2105-13-181

 received in 2012-01-31, accepted in 2012-07-12,  发布年份 2012

【 摘 要 】

Background

Parameter estimation in biological models is a common yet challenging problem. In this work we explore the problem for gene regulatory networks modeled by differential equations with unknown parameters, such as decay rates, reaction rates, Michaelis-Menten constants, and Hill coefficients. We explore the question to what extent parameters can be efficiently estimated by appropriate experimental selection.

Results

A minimization formulation is used to find the parameter values that best fit the experiment data. When the data is insufficient, the minimization problem often has many local minima that fit the data reasonably well. We show that selecting a new experiment based on the local Fisher Information of one local minimum generates additional data that allows one to successfully discriminate among the many local minima. The parameters can be estimated to high accuracy by iteratively performing minimization and experiment selection. We show that the experiment choices are roughly independent of which local minima is used to calculate the local Fisher Information.

Conclusions

We show that by an appropriate choice of experiments, one can, in principle, efficiently and accurately estimate all the parameters of gene regulatory network. In addition, we demonstrate that appropriate experiment selection can also allow one to restrict model predictions without constraining the parameters using many fewer experiments. We suggest that predicting model behaviors and inferring parameters represent two different approaches to model calibration with different requirements on data and experimental cost.

【 授权许可】

   
2012 Transtrum and Qiu; licensee BioMed Central Ltd.

【 图 表 】

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