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  Data-driven optimal prediction of bacteria growth


   Department of Mechanical, Aerospace and Civil Engineering

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

This project is devoted to an AI-based prediction of bacteria growth and its control by antibiotics. In synthetic biology, an improved understanding of bacterial regulatory circuits is required to develop complex biological systems with functionalities beyond existing in nature [1, 2]. Mathematical prediction of the behaviour of such systems is highly problematic because of the lack of reliable mathematical models and the sparsity of dynamic data. Such systems depend on numerous parameters with uncertainties. On the other hand, mathematical modelling of bacterial cooperative behaviour potentially is very attractive since it is much less costly and time-consuming than laboratory experiments.

To obtain a data-driven model, the dynamic mode decomposition, originally proposed by Schmid [3], is proposed to be used. DMD is a purely data-driven approach that only requires snapshots over time of measurement data at differently spaced control points. It does not require any preliminary mathematical model because it is supposed that DMD is originally trained on large enough data. The attractive feature of DMD is that it allows the analysis of dynamic data via their expansion into basic dynamic modes, with each mode characterised by its own frequency. Thus, DMD can capture key coherent spatio-temporal patterns from complex data. In addition, the method of optimal prediction [4] will be implemented to reduce the effect of uncertainties caused by incomplete data.

This approach effectively provides the expectation under uncertainties. To obtain the expectation for the bacterial growth, random initial populations should be considered. The use of DMD with the optimal prediction should allow the prediction to be achieved very efficiently, avoiding the time-consuming Monte Carlo method of classical ensemble modelling [5]. The developed algorithm should be compared with alternative approaches such as the supervised machine learning and existing mathematical models.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Funding

At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.

Before you apply

We strongly recommend that you contact the supervisors for this project before you apply.

How to apply

Apply online through our website: https://uom.link/pgr-apply-fap

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

  • Final Transcript and certificates of all awarded university level qualifications
  • Interim Transcript of any university level qualifications in progress
  • CV
  • Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
  • English Language certificate (if applicable)

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

Biological Sciences (4) Computer Science (8) Mathematics (25)

Funding Notes

This project is for self funded students. At The University of Manchester, we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers. Please see the project description for further details.

References

1. Fuqua C, Greenberg EP. Listening on bacteria: acyl-homoserine lactone signalling. Nat Rev Mol Cell Biol. 2002, 3:685–95.
2. Tsigkinopoulou A., Takano, E., Breitling R., Unravelling the γ-butyrolactone network in Streptomyces coelicolor by computational ensemble modelling, PLOS Comp. Biology, 2020, 16(7):e1008039.
3. Schmid PJ. Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 2010, 656: 5–28.
4. Katrutsa, A., Utyuzhnikov, S., Oseledets, I., Extension of Dynamic Mode Decomposition for dynamic systems with incomplete information based on t-model of optimal prediction, J. Computational Physics 2023, 476: 1–10.
5. Tsigkinopoulou A, Hawari A, Uttley M, Breitling R., Defining informative priors for ensemble modeling in systems biology. Nature Protoc. 2018, 13(11):2643–2663.

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