Theoretical Systems Biology

I am a PhD student, funded by the BBSRC. My primary focus is the inference of gene regulatory networks from single cell transcriptomic data, using machine learning methods based on information theory and Bayesian statistics.

I gained my MSc in Bioinformatics and Theoretical Systems Biology from Imperial College (with distinction) in 2015. Prior to this I completed the first three years of the MBBS in medicine at King’s College London, my current research being intercalated into this degree. I hold a BA in Music from the University of Cambridge, which I completed in 2007.

My interests outside of research include teaching maths and contributing to open source software projects, including work for Mozilla and the Wikimedia Foundation.

Gene regulatory network inference from single-cell data using multivariate information measures
Chan TE, Stumpf MPH, Babtie AC
Cell Systems (2017).

Stem Cell Differentiation as a Non-Markov Stochastic Process
Stumpf PS, Smith RCG, Lenz M, Schuppert A, Müller FJ, Babtie AC, Chan TE, Stumpf MPH, Please CP, Howison SD, Arai F, MacArthur BD
Cell Systems (2017).

Learning regulatory models for cell development from single-cell transcriptomic data
Babtie AC, Chan TE, Stumpf MPH
Current Opinion in Systems Biology (2017).

Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data
Chan TE, Pallaseni AV, Babtie AC, McEwen KR, Stumpf MPH
bioRxiv (2018).

Invited talk
Information theoretic approaches to single cell gene regulatory network inference (2018)
SMPGD (Statistical Methods for Post Genomic Data)
Université de Montpellier, France

Talk
Improving biological network inference with Julia (2017)
JuliaCon
University of California, Berkeley, USA

Talk
Unifying editing and visual diffs (2017)
Wikimania
Montréal, Canada

Talk
Network inference using multivariate information measures (2017)
MASAMB (Mathematical and Statistical Aspects of Molecular Biology)
Institute of Molecular Biotechnology, Vienna, Austria

Poster
Network inference from single cell data using mutivariate information (2016)
Crick Computational and Physical Biology Interest Group workshop
The Francis Crick Institute, London, UK