### 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 language | English (US) |
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Title of host publication | Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 |

Pages | 5448-5451 |

Number of pages | 4 |

DOIs | |

State | Published - 2009 |

Event | 31st 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 2009 → Sep 6 2009 |

### Other

Other | 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 |
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Country | United States |

City | Minneapolis, MN |

Period | 9/2/09 → 9/6/09 |

### Fingerprint

### ASJC Scopus subject areas

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

### Cite this

*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] https://doi.org/10.1109/IEMBS.2009.5334018

**The inferelator 2.0 : A scalable framework for reconstruction of dynamic regulatory network models.** / Madar, Aviv; Greenfield, Alex; Ostrer, Harry; Vanden-Eijnden, Eric; Bonneau, Richard.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009.*, 5334018, pp. 5448-5451, 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, Minneapolis, MN, United States, 9/2/09. https://doi.org/10.1109/IEMBS.2009.5334018

}

TY - GEN

T1 - The inferelator 2.0

T2 - A scalable framework for reconstruction of dynamic regulatory network models

AU - Madar, Aviv

AU - Greenfield, Alex

AU - Ostrer, Harry

AU - Vanden-Eijnden, Eric

AU - Bonneau, Richard

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77950991905&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77950991905&partnerID=8YFLogxK

U2 - 10.1109/IEMBS.2009.5334018

DO - 10.1109/IEMBS.2009.5334018

M3 - Conference contribution

SN - 9781424432967

SP - 5448

EP - 5451

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

ER -