eGUT: a Tool for Predictive Computer Simulation of the Gut Microbiota and Host Interactions

We propose the development of a generic computer simulation tool to reduce and replace animal experiments by simulating the gut microbiota and their interactions with the host. This tool, called eGUT, will be built by extending the individual-based Dynamics of Microbial Communities Simulator iDynoMiCS, which we have developed with many colleagues and published in Environmental Microbiology last year and made available as open source on Using an individual-based model (IbM) as the engine for the tool has the advantage that the tool will be very flexible, e.g., the number of species or the types of interactions or the rules of behaviour can be chosen by the scientist user - as required of a generic tool. Moreover, IbMs merely describe the activities of the individual organisms or host cells. Their behaviour is described by equations for the kinetics of growth and by rules for cell division (if larger than threshold divide) and other decisions. These kinetics and rules are typically based on laboratory experiments under defined conditions. It is quite important to realize that simple rules can lead to complex dynamics and spatial patterns so that complexity of a system does not mean that it cannot be understood or modelled because it is too complex. Complexity simply emerges from the multitude of local interactions of various individuals with the environment and with the host cells, as running an IbM will demonstrate. The gut microbiota has profound effects on our health, a realization leading to a surge of animal experiments in this area, so that the development of a computer simulation tool to reduce or replace these animal experiments is a gap that is becoming increasingly urgent to fill. Our specific aims are to build eGUT, apply eGUT to model the spread and replacement of antibiotic resistance plasmids in the gut, apply eGUT to two further test cases from our collaborators, and to disseminate eGUT to actively increase the user base.

Wright RJ et al. (2020). Damage Repair versus Aging in an Individual-Based Model of Biofilms. mSystems  5(5):e00018-20. doi: 10.1128/mSystems.00018-20

Kreft JU (2018). Editorial: The microbiome as a source of new enterprises and job creation. Microbial Biotechnology 11(1):145-8. doi: 10.1111/1751-7915.1302

Leveau JHJ et al. (2018). Editorial: The Individual Microbe: Single-Cell Analysis and Agent-Based Modelling. Frontiers in Microbiology 9:2825. doi: 10.3389/fmicb.2018.02825

Clegg RJ and Kreft JU (2017). Reducing discrepancies between 3D and 2D simulations due to cell packing density. Journal of Theoretical Biology 423:26-30. doi: 10.1016/j.jtbi.2017.04.016

Kreft JU et al. (2017). From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality. Frontiers in Microbiology 8:2299. doi: 10.3389/fmicb.2017.02299

Hellweger FL et al. (2016). Advancing microbial sciences by individual-based modelling. Nature Reviews Microbiology 14:461-71. doi: 10.1038/nrmicro.2016.62

Widder S et al. (2016). Challenges in microbial ecology: building predictive understanding of community function and dynamics. The ISME journal 10(11):2557-2568. doi: 10.1038/ismej.2016.45

Sinden N et al. (2015). α-1-Antitrypsin variants and the proteinase/antiproteinase imbalance in chronic obstructive pulmonary disease. American Journal of Physiology-Lung Cellular and Molecular Physiology 308(2). doi: 10.1152/ajplung.00179.2014

Clegg RJ et al. (2014). Repair rather than segregation of damage is the optimal unicellular aging strategy. BMC biology 12:52. doi: 10.1186/s12915-014-0052-x

Kreft JU et al. (2013). Mighty small: Observing and modeling individual microbes becomes big science. PNAS 110(45):18027-8. doi: 10.1073/pnas.1317472110



Back to top
Project grant



Principal investigator

Dr Jan-Ulrich Kreft


University of Birmingham


Professor Christopher Morton
Dr Shan He

Grant reference number


Award date

Jan 2013 - Jun 2016

Grant amount