Header

UZH-Logo

Maintenance Infos

A divide-and-conquer approach to analyze underdetermined biochemical models


Kotte, Oliver; Heinemann, Matthias (2009). A divide-and-conquer approach to analyze underdetermined biochemical models. Bioinformatics, 25(4):519-525.

Abstract

Motivation: To obtain meaningful predictions from dynamic computational models, their uncertain parameter values need to be estimated from experimental data. Due to the usually large number of parameters compared to the available measurement data, these estimation problems are often underdetermined meaning that the solution is a multidimensional space. In this case, the challenge is yet to obtain a sound system understanding despite non-identifiable parameter values, e.g. through identifying those parameters that most sensitively determine the model’s behavior. Results: Here, we present the so-called divide-and-conquer approach—a strategy to analyze underdetermined biochemical models. The approach draws on steady state omics measurement data and exploits a decomposition of the global estimation problem into independent subproblems. The solutions to these subproblems are joined to the complete space of global optima, which can be easily analyzed.We derive the conditions at which the decomposition occurs, outline strategies to fulfill these conditions and—using an example model—illustrate how the approach uncovers the most important parameters and suggests targeted experiments without knowing the exact parameter values.

Abstract

Motivation: To obtain meaningful predictions from dynamic computational models, their uncertain parameter values need to be estimated from experimental data. Due to the usually large number of parameters compared to the available measurement data, these estimation problems are often underdetermined meaning that the solution is a multidimensional space. In this case, the challenge is yet to obtain a sound system understanding despite non-identifiable parameter values, e.g. through identifying those parameters that most sensitively determine the model’s behavior. Results: Here, we present the so-called divide-and-conquer approach—a strategy to analyze underdetermined biochemical models. The approach draws on steady state omics measurement data and exploits a decomposition of the global estimation problem into independent subproblems. The solutions to these subproblems are joined to the complete space of global optima, which can be easily analyzed.We derive the conditions at which the decomposition occurs, outline strategies to fulfill these conditions and—using an example model—illustrate how the approach uncovers the most important parameters and suggests targeted experiments without knowing the exact parameter values.

Statistics

Citations

15 citations in Web of Science®
16 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

51 downloads since deposited on 10 Jul 2013
22 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > YeastX
Special Collections > SystemsX.ch > Research, Technology and Development Projects
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2009
Deposited On:10 Jul 2013 15:43
Last Modified:07 Dec 2017 21:37
Publisher:Oxford University Press
ISSN:1367-4803
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/bioinformatics/btp004
PubMed ID:19126574

Download

Download PDF  'A divide-and-conquer approach to analyze underdetermined biochemical models'.
Preview
Filetype: PDF
Size: 2MB
View at publisher