Developing and validating a computational model of the gut microbiota-mucosa interactions to replace and reduce animal experiments

Many recent discoveries revealed the fascinating and important effects that the bacteria living in our gut have on our health. For example, they train our immune system, affect weight gain and even our mood and behaviour. As a consequence, the number of studies of gut bacteria has risen sharply. As many of these studies use animal experiments, more and more animals are being used.

Our long-term goal is to replace many of these animal experiments with computer simulations, and we have been developing software that allows scientists who have no programming experience to run such computer simulations. We call this software eGUT for electronic gut. We estimate that eGUT could ultimately replace 75,000 animal experiments worldwide each year.

The specific aim of this studentship is to fill an important gap in our eGUT software. The gap is a computational model of the gut mucosa. The mucosa is the gut wall where human tissue cells and bacteria meet. We will develop this computational mucosa model and use experiments with an engineered (non-animal) model and an insect model to check whether our eGUT model can make correct predictions. We will collaborate with Profs Wilmes (Luxembourg) and Moya (Spain), the experts who developed these models, to do the experiments in their labs. At the end of the studentship, researchers in the UK company Probiotics International and the two world-leading labs of Profs Hardt and Stecher will test our eGUT software and help us improve it. This way, more and more researchers will want to use it because it will make their research better. It is important that our software will help other researchers to better understand the systems they investigate because that will motivate them to replace animal experiments with computer simulations.

We have two very good reasons why computer simulations will improve the science. One is that the gut, with its thousands of different types of bacteria, the thousands of chemical components of the dozens of different types of food we eat, and the many ways in which our body organs and cells interact with these bacteria and chemicals, is a very complex system. Such complex systems are very difficult to understand without simulating mathematical models (that is simplifications) of these systems, in a computer. If the computer simulation results are similar to the results obtained in experiments, one can regard the simplified model as appropriate. If the results are different, one can find out what was wrong with the simplification and fix it. The complexity of the system also means that there is a tremendous number of factors that could affect results. One should therefore change all these factors to investigate their effect, which would make it necessary to use a huge number of animals.

The other good reason to use computer simulations is that animal models are often not very good models of the human. Mice guts for example are quite different from our guts. Not only are they much smaller so that food does not spend as much time in the gut to be digested, they also have large caeca, blind sacs where bacteria ferment fibres and other elements of the diet that are difficult to digest. Humans do not have these caeca. There are many other differences between mouse and human guts, which is why mice harbour different bacteria than we do. As a consequence, one cannot be sure that results of interventions in humans will be the same as in mice.

The studentship will also train the student in developing and using computational and engineered and insect models as alternatives to animal models. This will be an excellent preparation for a career at the forefront of science and we expect the student to keep developing and using eGUT in the future and thus become the world leader in using computational models instead of animal experiments.

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PhD Studentship


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Principal investigator

Dr Jan-Ulrich Kreft


University of Birmingham


Professor Tariq Iqbal

Grant reference number


Award date:

Apr 2018 - Apr 2021

Grant amount