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Torkel Loman

Torkel Loman

PhD Student

Sainsbury Laboratory
University of Cambridge
47 Bateman Street

Office Phone: +44(0)1223 761100

Research Interests

My research interests lie on the interface between mathematics and biology, and in particular the application of modern computational modelling to biological topics. I am currently designing and analysing mathematical models of the various stress response pathways in Bacillus subtilis for my PhD in James Locke’s research group at the Sainsbury Laboratory Cambridge University (SLCU).

B. subtilis responds to stress by activating a corresponding sigma factor, which acts as a transcription factor that helps the bacteria to survive a number of different environmental stresses. In addition to activating genes relating to the stress response, sigma factors typically activate their own transcription, as well as the transcription of their own anti-sigma factor. I am interested in developing mathematical models to help analysing how these mixed positive/negative feedback operate and how they are able to generate such a wide range of interesting behaviours as they do.

The simple nature of bacteria systems, combined with the relative ease with which we can generate high quality data from them, makes B. subtilis an ideal target for studying complex regulatory networks of gene expression in response to stress using mathematical models.

Before joining the Locke group at SLCU I studied Engineering Mathematics at Lund University (Master’s thesis: An investigation of WUSCHEL self-activation in the shoot apical meristem of plants), and later also bioinformatics (Master’s thesis: A Novel Method for Predicting Ribosomal RNA Genes in Prokaryotic Genomes), at the same university. I have also worked as a field assistant in surveys of the European adder (Vipera berus) in southern Sweden.

I am a co-developer of the Julia software package
DiffEqBiological.jl. The package is a tool facilitating the modelling of biochemical reaction networks. It allows the user to input their models in an intuitive and simple format and then serve as a link to the powerful suit of solvers implemented by DifferentialEquations.jl. It generates both deterministic and stochastic models. DiffEqBiological.jl also provide other tools useful to the analysis of biochemical reaction networks, such as bifurcation analysis.


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