If you made any changes in Pure these will be visible here soon.

Personal profile

Academic Biography

I obtained my undergraduate degree in Biological Sciences from Teesside University and went on to study Quantitative Genetics at the University of Edinburgh, returning to Teesside to pursue a challenging and fulfilling research project as part of my PhD.

My research involves the computational and mathematical modelling of microbial metabolism through the application of constraint-based reconstruction and analysis (COBRA) techniques to garner a better understanding of complex biological interactions. This involves the integration of data from large multi-omic datasets to improve model predictability and the application of machine learning techniques to extract more meaning from outputs.

The most exciting aspect of my research is its interdisciplinary nature – I have the opportunity to utilise and develop my skills in a wide range of subject areas including computer science, statistics, machine learning, systems biology and bioinformatics.

Education/Academic qualification

University of Edinburgh

26 Sep 201420 Jun 2015

Award Date: 20 Jun 2015

Bachelor, Teesside University

20 Sep 20111 Jun 2014

Award Date: 1 Jun 2014

Fingerprint Dive into the research topics where Supreeta Vijayakumar is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 3 Similar Profiles

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output

Open Access
  • 46 Downloads (Pure)

    Combining metabolic modelling with machine learning accurately predicts yeast growth rate

    Culley, C., Vijayakumar, S., Zampieri, G. & Angione, C., Jun 2019, (Accepted/In press).

    Research output: Contribution to conferencePaper

    Open Access
  • 311 Downloads (Pure)

    Machine and deep learning meet genome-scale metabolic modeling

    Zampieri, G., Vijayakumar, S., Yaneske, E. & Angione, C., 11 Jul 2019, In : PLoS Computational Biology. 15, 7, 29 p., e1007084.

    Research output: Contribution to journalArticle

    Open Access
  • 122 Downloads (Pure)

    Social Dynamics Modeling of Chrono-nutrition

    Di Stefano, A., Scata, M., Vijayakumar, S., Angione, C., La Corte, A. & Lio, P., 30 Jan 2019, In : PLoS Computational Biology. 15, 1, p. e1006714 e1006714.

    Research output: Contribution to journalArticle

    Open Access
  • 174 Downloads (Pure)

    Optimisation of multi-omic genome-scale models: methodologies, hands-on tutorial and perspectives

    Vijayakumar, S., Conway, M., Lió, P. & Angione, C., 1 Jan 2018, Metabolic Network Reconstruction and Modeling. Springer, p. 389-408

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Open Access
  • 74 Downloads (Pure)