Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

Konstantine Tchourine, Christine Vogel, Richard Bonneau

Research output: Contribution to journalArticle

Abstract

Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR's final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes. This work demonstrates that extending the biophysical accuracy of the assumed model of transcriptional regulation improves large-scale regulatory network inference. As a proof of concept, Tchourine et al. show that incorporating RNA degradation into the model results in better network recovery while simultaneously predicting accurate RNA degradation rates.

Original languageEnglish (US)
Pages (from-to)376-388
Number of pages13
JournalCell Reports
Volume23
Issue number2
DOIs
StatePublished - Apr 10 2018

Fingerprint

RNA Stability
RNA
Chaperonin Containing TCP-1
Degradation
Chaperonins
Genes
Gene Regulatory Networks
Proteasome Endopeptidase Complex
Eukaryota
Nucleic Acids
Saccharomyces cerevisiae
Transcription Factors
Transcription
Metabolism
Yeast
Recovery
Processing

Keywords

  • biophysical modeling
  • gene regulatory networks
  • machine learning
  • network inference
  • network remodeling
  • RNA degradation rates
  • RNA stability
  • saccharomyces cerevisiae
  • systems biology
  • transcriptional regulatory networks

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks. / Tchourine, Konstantine; Vogel, Christine; Bonneau, Richard.

In: Cell Reports, Vol. 23, No. 2, 10.04.2018, p. 376-388.

Research output: Contribution to journalArticle

@article{85d905fca2e04d11ba6e9b7e0474e518,
title = "Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks",
abstract = "Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR's final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes. This work demonstrates that extending the biophysical accuracy of the assumed model of transcriptional regulation improves large-scale regulatory network inference. As a proof of concept, Tchourine et al. show that incorporating RNA degradation into the model results in better network recovery while simultaneously predicting accurate RNA degradation rates.",
keywords = "biophysical modeling, gene regulatory networks, machine learning, network inference, network remodeling, RNA degradation rates, RNA stability, saccharomyces cerevisiae, systems biology, transcriptional regulatory networks",
author = "Konstantine Tchourine and Christine Vogel and Richard Bonneau",
year = "2018",
month = "4",
day = "10",
doi = "10.1016/j.celrep.2018.03.048",
language = "English (US)",
volume = "23",
pages = "376--388",
journal = "Cell Reports",
issn = "2211-1247",
publisher = "Cell Press",
number = "2",

}

TY - JOUR

T1 - Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

AU - Tchourine, Konstantine

AU - Vogel, Christine

AU - Bonneau, Richard

PY - 2018/4/10

Y1 - 2018/4/10

N2 - Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR's final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes. This work demonstrates that extending the biophysical accuracy of the assumed model of transcriptional regulation improves large-scale regulatory network inference. As a proof of concept, Tchourine et al. show that incorporating RNA degradation into the model results in better network recovery while simultaneously predicting accurate RNA degradation rates.

AB - Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR's final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes. This work demonstrates that extending the biophysical accuracy of the assumed model of transcriptional regulation improves large-scale regulatory network inference. As a proof of concept, Tchourine et al. show that incorporating RNA degradation into the model results in better network recovery while simultaneously predicting accurate RNA degradation rates.

KW - biophysical modeling

KW - gene regulatory networks

KW - machine learning

KW - network inference

KW - network remodeling

KW - RNA degradation rates

KW - RNA stability

KW - saccharomyces cerevisiae

KW - systems biology

KW - transcriptional regulatory networks

UR - http://www.scopus.com/inward/record.url?scp=85044844358&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044844358&partnerID=8YFLogxK

U2 - 10.1016/j.celrep.2018.03.048

DO - 10.1016/j.celrep.2018.03.048

M3 - Article

C2 - 29641998

AN - SCOPUS:85044844358

VL - 23

SP - 376

EP - 388

JO - Cell Reports

JF - Cell Reports

SN - 2211-1247

IS - 2

ER -