Job Available: Statistical inference for mechanistic models

N.B. Applications for this position are now closed.

We are offering a 3 year position as a research fellow in statistical inference for mechanistic models. This is offered at either Postdoc or Senior postdoc level (equivalent grade to Assistant Professor) here in the Centre for Mathematical Medicine & Biology and Statistics & Probability groups, based in Mathematical Sciences, University of Nottingham.

We have started a Wellcome Trust funded project entitled “Developing cardiac electrophysiology models for drug safety studies”. This is an exciting opportunity to get involved in a substantial research team that will consist of at least two new postdoctoral research associate positions, together with Dominic Whittaker, me and a dedicated research software engineer Maurice Hendrix in collaboration with colleagues in statistics & probability within Nottingham (in particular Simon Preston and Theo Kypraois).

Over the last 10 years I’ve been doing cardiac modelling, I have come to think that our main problems in the field are related to reliably and reproducibly choosing and deriving biophysically-based mechanistic models from experimental data, and accounting for uncertainty whilst doing this. There are quite a few challenges involved, so many challenges that we held a month long residential programme on the challenges called the Fickle Heart at the Newton Institute in Cambridge this past summer (videos from final workshop available here).

You’ll find a lot of our open questions discussed in various past blog posts, but here are a few that we will be tackling in this grant:

  • Deciding appropriate baseline models for the ion currents (see my talk at Banff research station on this topic), and parameterising these models effectively is a big pre-requisite for our research, which we’ve been working on recently in these papers – sinusoidal protocols, high-throughput model building. Open challenges on how to do model selection as well as parameterisation, whilst accounting for all models being imperfect. Selecting appropriate noise models for use in likelihood-based methods is an interesting part of this.
  • Designing experiments to get information on drug binding to ion channels, and making sure that they can run on high-throughput automated machines. Open challenges in how to design these for (global) parameter optimisation, model selection, model validation, and to assess/capture/model the discrepancies.
  • Tailoring mathematical action potential models to particular cell types, to make predictions of what drugs might do in different species and cell types. Again, we think that doing more informative experiments (working with the Christini lab to build on this) will help a lot.
  • Considering all of this in a probabilistic/statistical framework that accounts for uncertainty and variability in a lot of different aspects:
    • our datasets and the underlying biological systems,
    • model parameters,
    • model structures/equations themselves,
    • discrepancy between models and reality,
    • our subsequent drug safety predictions.

We’ll be working closely with: industry labs (in particular at GlaxoSmithKline and Roche); pharmaceutical regulators (including the FDA); and academic labs (in particular Teun de Boer’s lab in UMC Utrecht in the Netherlands and Adam Hill & Jamie Vandenberg‘s labs in Victor Chang Cardiac Research Institute, Sydney, Australia). So candidates must enjoy teamwork, collaborative inter-disciplinary projects, and be prepared to get into the lab for a few weeks to really get to grips with the experiments we are trying to infer things from.

If any of that sounds interesting to you – please do apply! Feel free to contact me with informal enquiries.

You can apply online here: https://www.nottingham.ac.uk/jobs/currentvacancies/ref/SCI362219. The deadline is Tuesday 5th November.

This entry was posted in Action Potential Models, Drug action, Experimental Design, Ion Channel Models, Model Development, Numerics, Stats and Inference and tagged , , , , , , , , , , , , . Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s