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

PLoS Comput Biol
Authors
Keywords
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.

Year of Publication
2015
Journal
PLoS Comput Biol
Volume
11
Issue
5
Pages
e1004096
Date Published
2015 May
ISSN
1553-7358
URL
DOI
10.1371/journal.pcbi.1004096
PubMed ID
26020786
PubMed Central ID
PMC4447414
Links
Grant list
DP1 LM011510 / LM / NLM NIH HHS / United States
DP1 OD006413 / OD / NIH HHS / United States
5DP1LM01150-05 / DP / NCCDPHP CDC HHS / United States