Biology subjects

Stumpf, M.

Publications and source records attributed to Stumpf, M..

3 recordsLinked to original sources

bioRxiv2018

Signalling pathways drive heterogeneity of ground state pluripotency

Pluripotent stem cells (PSCs) can self-renew indefinitely while maintaining the ability to generate all cell types of the body. This plasticity is proposed to require heterogeneity in gene expression, driving a metastable state which may allow flexible cell fate choices. Contrary to this, naive PSC grown in fully defined 2i environmental conditions, containing small molecule inhibitors of MEK and GSK3 kinases, show homogenous pluripotency and lineage marker expression. However, here we show that 2i induces greater genome-wide heterogeneity than traditional serum-containing growth environments at the population level across both male and female PSCs. This heterogeneity is dynamic and reversible over time, consistent with a dynamic metastable equilibrium of the pluripotent state. We further show that the 2i environment causes increased heterogeneity in the calcium signalling pathway at both the population and single-cell level. Mechanistically, we identify loss of robustness regulators in the form of negative feedback to the upstream EGF receptor. Our findings advance the current understanding of the plastic nature of the pluripotent state and highlight the role of signalling pathways in the control of transcriptional heterogeneity. Furthermore, our results have critical implications for the current use of kinase inhibitors in the clinic, where inducing heterogeneity may increase the risk of cancer metastasis and drug resistance.

molecular biology↗

bioRxiv2018

Parametric and Non-parametric Gradient Matching for Network Inference

Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. To avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching approach has been proposed to solve differential equations approximately. Based on this gradient matching approach, we evaluate the fits of parametric as well as non-parametric candidate models to the data under various settings for different inference objectives. We also use model averaging, based on the Bayesian Information Criterion (BIC), in order to combine the different inferences. We found that parametric methods can provide comparable, and often improved inference compared to non-parametric methods; the latter, however, require no kinetic information and are computationally more efficient.\n\nThe code used in this work is available at https://github.com/ld2113/Final-Project.

systems biology↗

bioRxiv2016

Network inference and hypotheses-generation from single-cell transcriptomic data using multivariate information measures

While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell data sets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available here: https://github.com/Tchanders/network_inference_tutorials. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

systems biology↗