Elucidation of Genetic Interactions in the Yeast GATA-Factor Network Using Bayesian Model Selection

Thermodynamic Modelling of Transcriptional Control: A Sensitivity Analysis

Modelling is a tool used to decipher the biochemical mechanisms involved in transcriptional control. Experimental evidence in genetics is usually supported by theoretical models in order to evaluate the effects of all the possible interactions that can occur in these complicated processes. Models derived from the thermodynamic method are critical in this labour because they are able to take into account multiple mechanisms operating simultaneously at the molecular micro-scale and relate them to transcriptional initiation at the tissular macro-scale. This work is devoted to adapting computational techniques to this context in order to theoretically evaluate the role played by several biochemical mechanisms. The interest of this theoretical analysis relies on the fact that it can be contrasted against those biological experiments where the response to perturbations in the transcriptional machinery environment is evaluated in terms of genetically activated/repressed regions. The theoretical reproduction of these experiments leads to a sensitivity analysis whose results are expressed in terms of the elasticity of a threshold function determining those activated/repressed regions. The study of this elasticity function in thermodynamic models already proposed in the literature reveals that certain modelling approaches can alter the balance between the biochemical mechanisms considered, and this can cause false/misleading outcomes. The reevaluation of classical thermodynamic models gives us a more accurate and complete picture of the interactions involved in gene regulation and transcriptional control, which enables more specific predictions. This sensitivity approach provides a definite advantage in the interpretation of a wide range of genetic experimental results.

TopoFilter: a MATLAB package for mechanistic model identification in systems biology

Background

To develop mechanistic dynamic models in systems biology, one often needs to identify all (or minimal) representations of the biological processes that are consistent with experimental data, out of a potentially large set of hypothetical mechanisms. However, a simple enumeration of all alternatives becomes quickly intractable when the number of model parameters grows. Selecting appropriate dynamic models out of a large ensemble of models, taking the uncertainty in our biological knowledge and in the experimental data into account, is therefore a key current problem in systems biology.

Results

The TopoFilter package addresses this problem in a heuristic and automated fashion by implementing the previously described topological filtering method for Bayesian model selection. It includes a core heuristic for searching the space of submodels of a parametrized model, coupled with a sampling-based exploration of the parameter space. Recent developments of the method allow to balance exhaustiveness and speed of the model space search, to efficiently re-sample parameters, to parallelize the search, and to use custom scoring functions. We use a theoretical example to motivate these features and then demonstrate TopoFilter’s applicability for a yeast signaling network with more than 250’000 possible model structures.

Conclusions

TopoFilter is a flexible software framework that makes Bayesian model selection and reduction efficient and scalable to network models of a complexity that represents contemporary problems in, for example, cell signaling. TopoFilter is open-source, available under the GPL-3.0 license at https://gitlab.com/csb.ethz/TopoFilter. It includes installation instructions, a quickstart guide, a description of all package options, and multiple examples.

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

Robust Transcriptional Response to Heat Shock Impacting Diverse Cellular Processes despite Lack of Heat Shock Factor in Microsporidia

The majority of fungal species prefer the 12° to 30°C range, and relatively few species tolerate temperatures higher than 35°C. Our understanding of the mechanisms underpinning the ability of some species to grow at higher temperatures is incomplete. Nosema ceranae is an obligate intracellular fungal parasite that infects honey bees and can cause individual mortality and contribute to colony collapse. Despite a reduced genome, this species is strikingly thermotolerant, growing optimally at the colony temperature of 35°C. In characterizing the heat shock response (HSR) in N. ceranae, we found that this and other microsporidian species have lost the transcriptional regulator HSF and possess a reduced set of putative core HSF1-dependent HSR target genes. Despite these losses, N. ceranae demonstrates robust upregulation of the remaining HSR target genes after heat shock. In addition, thermal stress leads to alterations in genes involved in various metabolic pathways, ribosome biogenesis and translation, and DNA repair. These results provide important insight into the stress responses of microsporidia. Such a new understanding will allow new comparisons with other pathogenic fungi and potentially enable the discovery of novel treatment strategies for microsporidian infections affecting food production and human health.IMPORTANCE We do not fully understand why some fungal species are able to grow at temperatures approaching mammalian body temperature. Nosema ceranae, a microsporidium, is a type of fungal parasite that infects honey bees and grows optimally at the colony temperature of 35°C despite possessing cellular machinery for responding to heat stress that is notably simpler than that of other fungi. We find that N. ceranae demonstrates a robust and broad response to heat shock. These results provide important insight into the stress responses of this type of fungus, allow new comparisons with other pathogenic fungi, and potentially enable the discovery of novel treatment strategies for this type of fungus.

BCM: toolkit for Bayesian analysis of Computational Models using samplers

BACKGROUND

Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model.

RESULTS

We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved.

CONCLUSIONS

BCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.