The inferelator 2.0: A scalable framework for reconstruction of dynamic regulatory network models

Aviv Madar, Alex Greenfield, Harry Ostrer, Eric Vanden-Eijnden, Richard Bonneau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Current methods for reconstructing biological networks often learn either the topology of large networks or the kinetic parameters of smaller networks with a well-characterized topology. We have recently described a network characterized reconstruction algorithm, the Inferelator 1.0, that given a set of genome-wide measurements as input, simultaneously learns both topology and kinetic-parameters. Specifically, it learns a system of ordinary differential equations (ODEs) that describe the rate of change in transcription of each gene or gene-cluster, as a function of environmental and transcription factors. In order to scale to large networks, in Inferelator 1.0 we have approximated the system of ODEs to be uncoupled, and have solved each ODE using a one-step finite difference approximation. Naturally, these approximations become crude as the simulated time-interval increases. Here we present, implement, and test a new Markov-Chain-Monte-Carlo (MCMC) dynamical modeling method, Inferelator 2.0, that works in tandem with Inferelator 1.0 and is designed to relax these approximations. We show results for the prokaryote Halobacterium that demonstrate a marked improvement in our predictive performance in modeling the regulatory dynamics of the system over longer time-scales.

Original languageEnglish (US)
Title of host publicationProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEngineering the Future of Biomedicine, EMBC 2009
PublisherIEEE Computer Society
Pages5448-5451
Number of pages4
ISBN (Print)9781424432967
DOIs
StatePublished - 2009
Event31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 - Minneapolis, MN, United States
Duration: Sep 2 2009Sep 6 2009

Publication series

NameProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009

Other

Other31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
CountryUnited States
CityMinneapolis, MN
Period9/2/099/6/09

ASJC Scopus subject areas

  • Cell Biology
  • Developmental Biology
  • Biomedical Engineering
  • Medicine(all)

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  • Cite this

    Madar, A., Greenfield, A., Ostrer, H., Vanden-Eijnden, E., & Bonneau, R. (2009). The inferelator 2.0: A scalable framework for reconstruction of dynamic regulatory network models. In Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (pp. 5448-5451). [5334018] (Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009). IEEE Computer Society. https://doi.org/10.1109/IEMBS.2009.5334018