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Publication Detail
Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Karr JR, Williams AH, Zucker JD, Raue A, Steiert B, Timmer J, Kreutz C, Hu Y, Baron M, Bryson K, Barker B, Bogart E, Wang Y, Chandramohan D, Huang L, Zawack K, Shestov AA, Makadia H, DeCicco D, Yin A, Wang M, Li SC, Swistak M, Cygan M, Kazakiewicz D, Kursa MB, Korytkowski P, Plewczynski D, Yang J, Li Y, Tang H, Wang T, Liu Y, Xie Y, Xiao G, Bello J, Botero Rozo DO, Cañas-Duarte SJ, Castro JC, Gomez F, Valdes I, Vivas LG, Bernal A, Pedraza Leal JM, Restrepo S, Muñoz AR, Wilkinson S, Allgood BA, Bot BM, Hoff BR, Kellen MR, Covert MW, Stolovitzky GA, Meyer P
  • Publication date:
    01/05/2015
  • Journal:
    PLoS Computational Biology
  • Volume:
    11
  • Issue:
    5
  • Status:
    Published
  • Print ISSN:
    1553-734X
Abstract
© 2015 Karr et al.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.
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