Design of synthetic plant and mammal gene regulatory networks using nonparametric Bayesian approaches

This project aims to develop a suite of computational tools for the rational data-driven (re)design of gene regulatory networks that can be used to guide synthetic biology efforts.

The Idea

Despite some impressive success stories, synthetic biology approaches still require a significant element of trial and error. Our ability to design and predict is still poor. This is largely due to the challenge of simulating large networks and using them effectively to inform experiments. Here, we aim to develop a suite of computational tools for the rational data-driven (re)design of gene regulatory networks (GRNs) that can be used to guide synthetic biology efforts. Our approaches will be validated in silico, and using data from well characterised plant and mammalian systems. As a proof of principle, GRNs associated with stress responses in Arabidopsis thaliana will be synthetically rewired and tested in protoplasts, whilst future work will aim to engineer synthetic switches in human embryonic stem cells to rationally direct their cell fate decisions.

The Team

Dr Christopher Andrew Penfold,
Postdoctoral Researcher, Wellcome/CRUK Gurdon Insitute, University of Cambridge

Dr Marc Jones,
Postdoctoral Researcher, Computational and Systems Biology, John Innes Centre, Norwich

Ms Iulia Gherman,
PhD Student, Department of Biology, University of York

Ms Anastasiya Sybirna,
PhD Student, Wellcome/CRUK Gurdon Institute, University of Cambridge


Project Outputs

Project Report

Summary of the project's achievements and future plans

Project Proposal

Original proposal and application

software.png

Project Resources

Github code is available at:

https://github.com/cap76/BranchingGPs (branching processes)

https://github.com/cap76/PGCPseudotime (pseudotime)

https://github.com/cap76/NeRD (dynamical systems)

Several papers have been published on this work:

Penfold CA, Sybirna A, Reid JE, Huang Y, Wernisch L, Ghahramani Z, Grant M, Surani MA (2018). Branch-recombinant Gaussian processes for analysis of perturbations in biological time series. Bioinformatics; 34(17), pp i1005–i1013.

Penfold CA, Gherman I, Sybirna A, Wild DL (2019). Inferring Gene Regulatory Networks from Multiple Datasets. Methods in Molecular Biology book series, volume 1883, Gene Regulatory Networks, pp 251-282.

Penfold CA, Sybirna A, Reid J, Castillo Venzor A, Drousioti E, Huang Y, Grant M, Wernisch L, Ghahramani Z, Surani MA (2018). Bayesian inference of transcriptional branching identifies regulators of early germ cell development in humans. BioRxiv pre-print https://doi.org/10.1101/167684