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. While classical epistasis analysis has yielded deep insights on numerous genetic pathways, it is not without limitations. Here, we explore the possibility of dynamic epistasis analysis, in which, in addition to performing genetic perturbations of a pathway, we drive the pathway by a time-varying upstream signal. We explore the theoretical power of dynamical epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We find that even relatively simple input dynamics greatly increases the power of epistasis analysis to discriminate alternative network structures. Further, we explore the question of experiment design, and show that a subset of short time-varying signals, which we call dynamic primitives, allow maximum discriminative power with a reduced number of experiments.

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). The Six1 and Six4 homeodomain TFs are expressed in developing and adult muscle and Six1 is critical for embryonic and adult myogenesis. However, the lack of a muscle developmental phenotype in Six4-null mice, which has been attributed to compensation by other Six family members, has discouraged further assessment of the role of Six4 during adult muscle regeneration. By employing genome-wide approaches to address the function of Six4 during adult skeletal myogenesis, we have identified a core set of muscle genes coordinately regulated in adult muscle precursors by Six4 and the MRF MyoD. Throughout the genome of differentiating adult myoblasts, the cooperation between Six4 and MyoD is associated with chromatin repressive mark removal by Utx, a demethylase of histone H3 trimethylated at lysine 27. Among the genes coordinately regulated by Six4 and MyoD are several genes critical for proper in vivo muscle regeneration, implicating a role of Six4 in this process. Using in vivo RNA interference of Six4, we expose an uncompensated function of this TF during muscle regeneration. Together, our results reveal a role for Six4 during adult muscle regeneration and suggest a widespread mechanism of cooperation between Six4 and MyoD.

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. To add to this debate, we tested four different homology-based methods, and found that students using these methods on their first attempt have a high probability of success. Because of their ease of use and high success rates, we believe that homology-based assembly is a best practice of Synthetic Biology, and recommend that the Registry implement the changes proposed by Alnahhas et al. to better support their use.

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. Our analysis divides the motifs into equivalence classes, where different genetic perturbations result in indistinguishable experimental outcomes. We demonstrate that this partitioning can reveal important information about network architecture, and show, using simulated data, that it greatly improves the accuracy of genetic network inference methods. Because of the minimal assumptions involved, equivalence partitioning has broad applicability for gene network inference.

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. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ~80% of the known relationships without any false positives.

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. We demonstrate how statistical methods for density estimation, in particular, mixture density and conditional mixture density estimators, can be employed to establish empirical bifurcation diagrams describing the bistable genetic switch network controlling galactose utilization in yeast Saccharomyces cerevisiae. These approaches allow us to make novel qualitative and quantitative observations about the switching behavior of the galactose network, and provide a framework that might be useful to extract information needed for the development of quantitative network models.