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BMC Bioinformatics. 2019 May 29;20(1):294. doi: 10.1186/s12859-019-2895-1.

Time varying causal network reconstruction of a mouse cell cycle.

Author information

1
Department of Electrical and Computer Engineering and Bioengineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
2
Department of Bioengineering and San Diego Supercomputer center, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
3
Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA.
4
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.

Abstract

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:

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:

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.

KEYWORDS:

Causal inference; Cell cycle; Change point detection; Dynamics; Temporal variation; Time series; Time varying network reconstruction

PMID:
31142274
PMCID:
PMC6542064
DOI:
10.1186/s12859-019-2895-1
[Indexed for MEDLINE]
Free PMC Article

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