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Rare-event sampling of epigenetic landscapes and phenotype transitions
- Source :
- PLoS Computational Biology, Vol 14, Iss 8, p e1006336 (2018), PLoS Computational Biology
- Publication Year :
- 2018
- Publisher :
- Public Library of Science (PLoS), 2018.
-
Abstract
- Stochastic simulation has been a powerful tool for studying the dynamics of gene regulatory networks, particularly in terms of understanding how cell-phenotype stability and fate-transitions are impacted by noisy gene expression. However, gene networks often have dynamics characterized by multiple attractors. Stochastic simulation is often inefficient for such systems, because most of the simulation time is spent waiting for rare, barrier-crossing events to occur. We present a rare-event simulation-based method for computing epigenetic landscapes and phenotype-transitions in metastable gene networks. Our computational pipeline was inspired by studies of metastability and barrier-crossing in protein folding, and provides an automated means of computing and visualizing essential stationary and dynamic information that is generally inaccessible to conventional simulation. Applied to a network model of pluripotency in Embryonic Stem Cells, our simulations revealed rare phenotypes and approximately Markovian transitions among phenotype-states, occurring with a broad range of timescales. The relative probabilities of phenotypes and the transition paths linking pluripotency and differentiation are sensitive to global kinetic parameters governing transcription factor-DNA binding kinetics. Our approach significantly expands the capability of stochastic simulation to investigate gene regulatory network dynamics, which may help guide rational cell reprogramming strategies. Our approach is also generalizable to other types of molecular networks and stochastic dynamics frameworks.<br />Author summary Cell phenotypes are controlled by complex interactions between genes, proteins, and other molecules within a cell, along with signals from the cell’s environment. Gene regulatory networks (GRNs) describe these interactions mathematically. In principle, a GRN model can produce a map of possible cell phenotypes and phenotype-transitions, potentially informing experimental strategies for controlling cell phenotypes. Such a map could have a profound impact on many medical fields, ranging from stem cell therapies to wound healing. However, analytical solution of GRN models is virtually impossible, except for the smallest networks. Instead, time course trajectories of GRN dynamics can be simulated using specialized algorithms. However, these methods suffer from the difficulty of studying rare events, such as the spontaneous transitions between cell phenotypes that can occur in Embryonic Stem Cells or cancer cells. In this paper, we present a method to expand current stochastic simulation algorithms for the sampling of rare phenotypes and phenotype-transitions. The output of the computational pipeline is a simplified network of a few stable phenotypes, linked by potential transitions with quantified probabilities. This simplified network gives an intuitive representation of cell phenotype-transition dynamics, which could be useful for understanding how molecular processes impact cellular responses and aid interpretation of experimental data.
- Subjects :
- 0301 basic medicine
Epigenomics
Computer science
Molecular Networks (q-bio.MN)
Gene regulatory network
Gene Expression
01 natural sciences
Biochemistry
Animal Cells
Stochastic simulation
Biochemical Simulations
Data Mining
Gene Regulatory Networks
Quantitative Biology - Molecular Networks
Rare Event Sampling
lcsh:QH301-705.5
Network model
010304 chemical physics
Ecology
Stem Cells
Simulation and Modeling
Quantitative Biology::Molecular Networks
Cell Differentiation
Cellular Reprogramming
Quantitative Biology::Genomics
Phenotypes
Phenotype
Computational Theory and Mathematics
Modeling and Simulation
Data Interpretation, Statistical
Physical Sciences
Epigenetics
Cellular Types
Network Analysis
Network analysis
Research Article
Pluripotency
Computer and Information Sciences
Markov Models
Cell Potency
Computational biology
Markov model
Research and Analysis Methods
Models, Biological
03 medical and health sciences
Cellular and Molecular Neuroscience
0103 physical sciences
DNA-binding proteins
Genetics
Computer Simulation
Gene Regulation
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Embryonic Stem Cells
Probability
Stochastic Processes
Models, Genetic
Stochastic process
Biology and Life Sciences
Proteins
Computational Biology
Cell Biology
Probability Theory
Regulatory Proteins
Kinetics
030104 developmental biology
Gene Expression Regulation
lcsh:Biology (General)
FOS: Biological sciences
Software
Mathematics
Transcription Factors
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 14
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....4e8c04f05baad9636bbdb7f0b734a817