Dynamics in Epistasis Analysis.

Finding regulatory relationships between genes, including the direction and nature of influence between them, is a fundamental challenge in the field of molecular genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes.

Erratum to: No training required: experimental tests support homology-based DNA assembly as a best practice in synthetic biology.

[This corrects the article DOI: 10.1186/s13036-015-0006-z.].

Genome-wide association between Six4, MyoD, and the histone demethylase Utx during myogenesis.

Adult skeletal muscles can regenerate after injury, due to the presence of satellite cells, a quiescent population of myogenic progenitor cells. Once activated, satellite cells repair the muscle damage by undergoing myogenic differentiation. The myogenic regulatory factors (MRFs) coordinate the process of progenitor differentiation in cooperation with other families of transcription factors (TFs).

No training required: experimental tests support homology-based DNA assembly as a best practice in synthetic biology.

The Registry of Standard Biological Parts imposes sequence constraints to enable DNA assembly using restriction enzymes. Alnahhas et al. (Journal of Biological Engineering 2014, 8:28) recently argued that these constraints should be revised because they impose an unnecessary burden on contributors that use homology-based assembly.

Identifiability and inference of pathway motifs by epistasis analysis.

The accuracy of genetic network inference is limited by the assumptions used to determine if one hypothetical model is better than another in explaining experimental observations. Most previous work on epistasis analysis-in which one attempts to infer pathway relationships by determining equivalences among traits following mutations-has been based on Boolean or linear models. Here, we delineate the ultimate limits of epistasis-based inference by systematically surveying all two-gene network motifs and use symbolic algebra with arbitrary regulation functions to examine trait equivalences.

Quantitative epistasis analysis and pathway inference from genetic interaction data.

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them.

Estimating the stochastic bifurcation structure of cellular networks.

High throughput measurement of gene expression at single-cell resolution, combined with systematic perturbation of environmental or cellular variables, provides information that can be used to generate novel insight into the properties of gene regulatory networks by linking cellular responses to external parameters. In dynamical systems theory, this information is the subject of bifurcation analysis, which establishes how system-level behaviour changes as a function of parameter values within a given deterministic mathematical model. Since cellular networks are inherently noisy, we generalize the traditional bifurcation diagram of deterministic systems theory to stochastic dynamical systems.