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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 modelling (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

Bachelor, Teesside University

20 Sep 20111 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.

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Learning systems Engineering & Materials Science
Genome Medicine & Life Sciences
Genes Engineering & Materials Science
Social Dynamics Mathematics
Metabolic engineering Engineering & Materials Science
Nutrition Mathematics
Human Behavior Mathematics
Dynamic Modeling Mathematics

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Research Output 2017 2019

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 conferencePaperResearchpeer-review

Open Access
File
Metabolic engineering
Gene expression
Yeast
Industrial applications
Learning systems

Machine and deep learning meet genome-scale metabolic modelling

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

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
artificial intelligence
Learning systems
Machine Learning
Genome
learning
21 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 journalArticleResearchpeer-review

Open Access
File
Social Dynamics
human behavior
Nutrition
Human Behavior
Dynamic Modeling

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 proceedingChapterResearch

Open Access
File
Genome
Systems Biology
Metabolic Networks and Pathways
Codon
Shock
1 Citation (Scopus)

Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives

Vijayakumar, S., Conway, M., Lió, P. & Angione, C., 1 Jan 2018, Methods in Molecular Biology. Humana Press Inc., p. 389-408 20 p. (Methods in Molecular Biology; vol. 1716).

Research output: Chapter in Book/Report/Conference proceedingChapterResearch

Genome
Systems Biology
Metabolic Networks and Pathways
Codon
Shock