DNA methylation is a ubiquitous epigenetic mark that plays important yet disparate roles in gene regulation. On the one hand, genome-wide methylation patterns help establish and maintain distinct cell types, and these patterns are stably maintained. On the other hand, patterns in some loci are dynamic, facilitating nimble cellular responses to environmental stimuli. This talk will present our...
A key challenge in inferring gene regulatory networks (GRNs) governing cellular processes, such as differentiation and reprogramming, from experimental data lies in the impossibility of directly observing protein trajectories at the single-cell level, which prevents establishing causal relationships between regulator activity and target responses.
In this talk, we present CardamomOT, a new...
Within a cell, gene expression levels are governed by molecular regulators interacting with each other in gene regulatory networks (GRNs). In this talk, we present an efficient computational framework for analyzing stochastic GRNs, in which cellular behavior is characterized by multiple metastable phenotypes and rare transitions between them. The dynamics of stochastic GRNs is described by the...
Rare transitions between metastable states in stochastic gene regulatory networks are difficult to resolve in practice, as direct stochastic simulations require large amounts of data to capture these events reliably. In this talk, we present a neural network–based approach (ISOKANN) to learn low-dimensional reaction coordinates that describe the slow dynamics of such systems...
Gene expression is inherently stochastic and controlled at multiple stages, including epigenetic and transcriptional regulation. These processes are complex and often result in multimodal distributions. For example, the presence or absence of DNA methylation at a given genomic location exhibits a generally bimodal distribution, which can be linked to transcriptional variability. Likewise, gene...
