Abstract
BackgroundVisualizing the complex probability landscape of stochastic gene regulatory networks can further biologists’ understanding of phenotypic behavior associated with specific genes.ResultsWe present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks.ConclusionsWe demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.
Highlights
Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists’ understanding of phenotypic behavior associated with specific genes
M j=1 nj n, where n is the number of species, m is the number of Molecular Equivalence Groups (MEGs)— the number of molecular species subgroups in the network, such that member species of a subgroup can be transformed into each other through one or more mass-balanced reactions, nj is the number of species belonging to group MEGj, and b is the maximum number of molecules in MEGj
We evaluate the effectiveness of PRODIGEN through two case studies completed with two experts, who are co-authors on this manuscript
Summary
Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists’ understanding of phenotypic behavior associated with specific genes. Gene regulatory networks encode those interactions among genes and proteins that regulate cellular processes, such as the expression of messenger RNA (mRNA). These interactions dictate the expression levels of genes as well as the production of particular proteins, and play a critical role in regulating biological functions, from metabolism to cell differentiation. Such networks typically involve small copy numbers of the molecular species and large differences in the species reaction rates. The formulation of the discrete Chemical Master Equation (CME) can be used to analyze the dynamics and stochastic nature of gene regulatory
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