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Project properties |
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| Title | Designing stability-optimised synthetic microbial communities |
| Group | Systems and Synthetic Biology |
| Project type | thesis |
| Credits | 36 |
| Supervisor(s) | Rob Smith |
| Examiner(s) | robert1.smith@wur.nl |
| Contact info | Robert1.smith@wur.nl |
| Begin date | 2025/08/20 |
| End date | |
| Description | At SSB, our projects are either offered as computational or experimental projects. Nearly all projects can be altered for either BSc or MSc students (as required). For more information, please contact Rob Smith (robert1.smith@wur.nl). Note that we will evaluate applications based on discussing the match between a student’s current competencies and the competencies needed for the project – sending a CV is not required.
This thesis project will be a computational project. The project will be supervised by Rob Smith. Microbial communities are found across many life science research domains: from soil communities impacting plant development, to gut microbes regulating human and animal health. As a system, microbial communities need to respond to a range of input signals whilst maintaining stable performance, with break down of a community system being linked to negative physiological changes. Researchers are currently utilising a range of methods to try and understand how microbial communities balance sensitivity with stability. One way to achieve this is by using minimal “toy” systems that can mimic the behaviour of larger communities. Examples include engineering of minimal microbial communities that produced, for example, oscillatory behaviour over time (Miano et al. 2020), a memory of environmental signals (Khalighi et al. 2022) or tipping points (Faust et al. 2025). Mathematical models – using ordinary differential equations – are a good way to quickly assess whether system’s can perform certain functions and design networks with desired characteristics. This is often referred to as an optimisation or design problem: i.e. we find a way to efficiently explore different network structures to find the network with optimal functionalities. We have done this already with genetic circuits (Smith, van Sluijs et al. 2017) but re-designing such an algorithm for communities provides another layer of complexity since synthetic communities can be engineered in terms of the present microbes but also in how those individual microbes have been genetically engineered. This project works towards developing a computational algorithm for synthetic community design. Problems to solve in this project include how to write a generalised mathematical model of microbial metabolism (e.g., Millard et al. 2021), how to define stability or explore the “design space” efficiently, how to store model information in a FAIR and memory-friendly manner using tools such as SBML and PeTab, and how to infer experimental strategies from the results. References related to project: Smith, van Sluijs et al. (2017) doi: 10.1186/s12918-017-0499-9 Miano et al. (2020) doi: 10.1038/s41467-020-15056-8 Millard et al. (2021) doi: 10.7554/eLife.63661 Khalighi et al. (2022) doi: 10.1371/journal.pcbi.1009396 Faust et al. (2025) doi: 10.21203/rs.3.rs-5544319/v1 |
| Used skills | |
| Requirements | Interested students should be comfortable with mathematical modelling using ordinary differential equations and programming in Python (or Julia). The ability to work with FAIR data is useful but not a mandatory requirement. |