David S. Tourigny

Birmingham Fellow
School of Mathematics, University of Birmingham

I am a Birmingham Fellow (Assistant Professor equivalent) within the School of Mathematics, University of Birmingham. Prior to joining Birmingham, I spent five years as a Postdoctoral Fellow of the Simons Foundation and then Associate Research Scientist at Columbia University (Irving Medical Centre and Irving Institute for Cancer Dynamics). Before that, I was a Research Fellow (Peterhouse) in the Department of Applied Mathematics and Theoretical Physics, University of Cambridge where I also completed my PhD (Trinity College) under the supervision of Venki Ramakrishnan and Garib Murshudov (MRC Laboratory of Molecular Biology).

Research

  • Computational biology

    • My general research focus is geared towards understanding the collective behaviour of cellular populations. I have used both experimental and theoretical approaches to study this in a variety of contexts, ranging from microbial systems to neuronal networks. More recently, I have become interested in the way that stochasticity and heterogeneity at the single-cell or clonal level contribute to the development of cancer and precancerous lesions.

  • Mathematics

    • Most of my work involves mathematical and computational modelling based on Stochastic Processes, Bayesian Inference, Applied Dynamical Systems Theory and Constrained Optimisation. In the past, I also have worked on some of these topics in their own right with relatively less application. Specifically, this involved some geometric properties of certain dynamical systems.

  • Scientific software

    • I am continually developing software tools for my own research purposes as well as the wider scientific community. See Software for more information.

Publications

Here is a short list of selected publications. A full list can be found via Google Scholar

I have also contributed three chapters to the text book Economic Principles in Cell Biology.

Software

I develop and maintain the Python/C++ package dfba (see also paper here) for dynamic flux-balance analysis (DFBA) simulations. This is joint work with Moritz E. Beber and Jorge Carrasco Muriel that forms part of the openCOBRA code base for constraint-based reconstruction and analysis of metabolic models.

Other software and scripts for data processing can be accessed via GitLab.

During my PhD I worked on a maximum likelihood-based algorithm for clustering and inferring diffraction data from macromolecular crystals (today, this would probably be called machine learning). Details can be found in the second section of my thesis.

Teaching and supervision

Current and former research students: