Time varying causal network reconstruction of a mouse cell cycle.


Journal

BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194

Informations de publication

Date de publication:
29 May 2019
Historique:
received: 01 05 2019
accepted: 13 05 2019
entrez: 31 5 2019
pubmed: 31 5 2019
medline: 12 7 2019
Statut: epublish

Résumé

Biochemical networks are often described through static or time-averaged measurements of the component macromolecules. Temporal variation in these components plays an important role in both describing the dynamical nature of the network as well as providing insights into causal mechanisms. Few methods exist, specifically for systems with many variables, for analyzing time series data to identify distinct temporal regimes and the corresponding time-varying causal networks and mechanisms. In this study, we use well-constructed temporal transcriptional measurements in a mammalian cell during a cell cycle, to identify dynamical networks and mechanisms describing the cell cycle. The methods we have used and developed in part deal with Granger causality, Vector Autoregression, Estimation Stability with Cross Validation and a nonparametric change point detection algorithm that enable estimating temporally evolving directed networks that provide a comprehensive picture of the crosstalk among different molecular components. We applied our approach to RNA-seq time-course data spanning nearly two cell cycles from Mouse Embryonic Fibroblast (MEF) primary cells. The change-point detection algorithm is able to extract precise information on the duration and timing of cell cycle phases. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Estimation Stability with Cross Validation (ES-CV), we were able to, without any prior biological knowledge, extract information on the phase-specific causal interaction of cell cycle genes, as well as temporal interdependencies of biological mechanisms through a complete cell cycle. The temporal dependence of cellular components we provide in our model goes beyond what is known in the literature. Furthermore, our inference of dynamic interplay of multiple intracellular mechanisms and their temporal dependence on one another can be used to predict time-varying cellular responses, and provide insight on the design of precise experiments for modulating the regulation of the cell cycle.

Sections du résumé

BACKGROUND BACKGROUND
Biochemical networks are often described through static or time-averaged measurements of the component macromolecules. Temporal variation in these components plays an important role in both describing the dynamical nature of the network as well as providing insights into causal mechanisms. Few methods exist, specifically for systems with many variables, for analyzing time series data to identify distinct temporal regimes and the corresponding time-varying causal networks and mechanisms.
RESULTS RESULTS
In this study, we use well-constructed temporal transcriptional measurements in a mammalian cell during a cell cycle, to identify dynamical networks and mechanisms describing the cell cycle. The methods we have used and developed in part deal with Granger causality, Vector Autoregression, Estimation Stability with Cross Validation and a nonparametric change point detection algorithm that enable estimating temporally evolving directed networks that provide a comprehensive picture of the crosstalk among different molecular components. We applied our approach to RNA-seq time-course data spanning nearly two cell cycles from Mouse Embryonic Fibroblast (MEF) primary cells. The change-point detection algorithm is able to extract precise information on the duration and timing of cell cycle phases. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Estimation Stability with Cross Validation (ES-CV), we were able to, without any prior biological knowledge, extract information on the phase-specific causal interaction of cell cycle genes, as well as temporal interdependencies of biological mechanisms through a complete cell cycle.
CONCLUSIONS CONCLUSIONS
The temporal dependence of cellular components we provide in our model goes beyond what is known in the literature. Furthermore, our inference of dynamic interplay of multiple intracellular mechanisms and their temporal dependence on one another can be used to predict time-varying cellular responses, and provide insight on the design of precise experiments for modulating the regulation of the cell cycle.

Identifiants

pubmed: 31142274
doi: 10.1186/s12859-019-2895-1
pii: 10.1186/s12859-019-2895-1
pmc: PMC6542064
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

294

Subventions

Organisme : NIDDK NIH HHS
ID : U01 DK097430
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL106579
Pays : United States
Organisme : National Institute of Health
ID : 5R01CA195613-20
Organisme : NLM NIH HHS
ID : R01 LM012595
Pays : United States
Organisme : NIH HHS
ID : R01LM012595, U01 DK097430, U01 CA200147, U01 CA198941, U19 AI090023, R01 HL106579, R01HL108735, R01 HD084633, R01 DK109365
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA195613
Pays : United States
Organisme : National Science Foundation
ID : STC-0939370
Organisme : NIDDK NIH HHS
ID : R01 DK109365
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD084633
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA014195
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA200147
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA198941
Pays : United States
Organisme : NIAID NIH HHS
ID : U19 AI090023
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL108735
Pays : United States

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Auteurs

Maryam Masnadi-Shirazi (M)

Department of Electrical and Computer Engineering and Bioengineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.

Mano R Maurya (MR)

Department of Bioengineering and San Diego Supercomputer center, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.

Gerald Pao (G)

Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA.

Eugene Ke (E)

Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA.

Inder M Verma (IM)

Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA.

Shankar Subramaniam (S)

Department of Bioengineering, Departments of Computer Science and Engineering, Cellular and Molecular Medicine, and the Graduate Program in Bioinformatics, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA. shankar@ucsd.edu.

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Classifications MeSH