Keywords: mathematical modeling, gene regulatory networks, single-cell RNA sequencing, quantitative biology,

Requirements: Background in systems biology, mathematical modeling or computational biology.

Network reconstruction based on quantitative perturbation data

collaboration with Annalisa Marsico

Biological networks can process quantitative information to elicit the appropriate cellular response. To identify the regulatory principles that govern quantitative information processing in mammalian cells, we study how information on X-chromosomal dosage and the differentiation state of the cell is integrated to initiate X-chromosome inactivation only in female cells and at the right developmental time. X inactivation is an essential developmental process, where one randomly chosen X chromosome is silenced in each female cell to ensure dosage compensation for X-linked genes between the sexes. The master regulator of X inactivation is the long non-coding RNA Xist. We have developed a mathematical model of the Xist regulatory network that predicts the regulator types required to set up the correct Xist expression pattern (Mutzel et al., BioRxiv, 2017). Moreover, we have identified regulators of Xist that transmit information on X-dosage and differentiation through a series of pooled CRISPR screens.

Based on the previously developed abstract model of the Xist regulatory network, several mechanistic models will be developed of how information on X-dosage and differentiation might be integrated at the molecular level. To distinguish between those models, stochastic simulations will be compared with highly multiplexed perturbation data, generated with pooled CRISPR libraries either combined with quantitative cell sorting for single-cell RNA sequencing. One goal of the project is to develop a theoretical framework that allows the use of such high-dimensional perturbation data sets to constrain mechanistic mathematical models. The project is a collaboration with the Howard lab In Norwich, UK (https://www.jic.ac.uk/people/professor-martin-howard/).

For more information visit the website of the Schulz lab.

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