Summary of the DREAM8 parameter estimation challenge: toward parameter identification for whole-cell models

Karr, J. R. et al. (2015) Summary of the DREAM8 parameter estimation challenge: toward parameter identification for whole-cell models. PLoS Computational Biology, 11(5), e1004096. (doi: 10.1371/journal.pcbi.1004096) (PMID:26020786) (PMCID:PMC4447414)

Abstract

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

• Item Type: Articles
• Additional Information: Kevin Bryson is a member of the DREAM8 Parameter Estimation Challenge Consortium.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Bryson, Dr Kevin
• Authors: Karr, J. R., Williams, A. H., Zucker, J. D., Raue, A., Steiert, B., Timmer, J., Kreutz, C., DREAM8 Parameter Estimation Challenge Consortium, , Wilkinson, S., Allgood, B. A., Bot, B. M., Hoff, B. R., Kellen, M. R., Covert, M. W., Stolovitzky, G. A., and Meyer, P.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: PLoS Computational Biology
• Publisher: Public Library of Science
• ISSN: 1553-734X
• ISSN (Online): 1553-7358
• Copyright Holders: Copyright © 2015 Karr et al.
• First Published: First published in PLoS Computational Biology 11(5): e1004096
• Publisher Policy: Reproduced under a Creative Commons License