Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

Jonathan R Karr,Alex H Williams,Clemens Kreutz,Andreas Raue,Markus W Covert,Pablo Meyer,Gustavo A Stolovitzky,Dream8 Parameter Estimation Challenge Consortium ,Brian M Bot,Brandon A Allgood,Simon Wilkinson,Jeremy D Zucker,Jens Timmer,Bruce R Hoff,Michael R Kellen,Bernhard Steiert

doi:10.1371/journal.pcbi.1004096

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.

Highlights

Mechanistic modeling is a powerful tool for understanding and engineering biological behavior at the molecular level
Davidson et al have used Boolean modeling to understand Drosophila developmental patterning [1]; Orth et al have used flux-balance analysis (FBA) to predict Escherichia coli metabolism at the genomic scale [2]; Barkai and Leibler have used ordinary differential equations (ODEs) to model E. coli chemotaxis [3]; Arkin et al have used stochastic ODEs to understand the bacteriophage λ lysis/lysogeny switch [4]; and many others have used mechanistic models to study a wide range of cell physiology
We only modified three types of parameters: the RNA polymerase promoter binding probabilities and RNA half-lives, which control RNA expression and in turn metabolic enzyme expression, and the metabolic reaction turnover numbers. We focused on these three types of parameters because these parameters uniquely map onto changes in specific observables and are structurally identifiable, and because these parameters have the most direct influence on the metabolic submodel, and in turn the predicted growth rate

Summary

Introduction

Mechanistic modeling is a powerful tool for understanding and engineering biological behavior at the molecular level. Davidson et al have used Boolean modeling to understand Drosophila developmental patterning [1]; Orth et al have used flux-balance analysis (FBA) to predict Escherichia coli metabolism at the genomic scale [2]; Barkai and Leibler have used ordinary differential equations (ODEs) to model E. coli chemotaxis [3]; Arkin et al have used stochastic ODEs to understand the bacteriophage λ lysis/lysogeny switch [4]; and many others have used mechanistic models to study a wide range of cell physiology Despite these successes, no one mathematical formalism is capable of explaining all biological behaviors.

Methods

Results

Discussion

Conclusion