Publications
(in the last 5 years)
Full publications: https://loop.frontiersin.org/people/21134/publications
2024
Khanijou JK, Hee YT, Scipion CPM, Chen X, Selvarajoo K. Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli. (2024) NPJ Syst Biol Appl. doi:10.1038/s41540-024-00440-7
Yeo HC, Vijay V, Selvarajoo K. Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises. (2024) BMC Biology. doi:10.1186/s12915-024-02019-4
Kamil Pabis, Diogo Barardo, Jan Gruber, Olga Sirbu, Marco Malavolta, Kumar Selvarajoo, Matt Kaeberlein, Brian K. Kennedy,
The impact of short-lived controls on the interpretation of lifespan experiments and progress in geroscience – through the lens of the “900-day rule”, Ageing Research Reviews, 2024, 102512, ISSN 1568-1637. doi: 10.1016/j.arr.2024.102512
Rashid MM, Selvarajoo K. Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data. Brief Bioinform. 2024 May 23;25(4):bbae300. doi: 10.1093/bib/bbae300
Selvarajoo, K., Maurer-Stroh, S. (2024) Towards multi-omics synthetic data integration. Brief Bioinform. doi:.10.1093/bib/bbae213
Helmy M, Elhalis H, Rashid MM, Selvarajoo K (2024) Can Digital Twin Efforts Shape Microorganisms-based Alternative Food? Curr Opin Biotechnol. doi:10.1016/j.copbio.2024.103115
Khanijou JK, Hee YT, Selvarajoo K. Identifying Key In Silico Knockout for Enhancement of Limonene Yield Through Dynamic Metabolic Modelling. Methods Mol Biol. 2024;2745:3-19. doi: 10.1007/978-1-0716-3577-3_1
Hosam Elhalis, Mohamed Helmy, Sherilyn Ho, Sharon Leow, Yan Liu, Kumar Selvarajoo, Yvonne Chow. Identifying Chlorella vulgaris and Chlorella sorokiniana as sustainable organisms to bioconvert glucosamine into valuable biomass, Biotechnology Notes, 2024; 55:13-22. https://doi.org/10.1016/j.biotno.2024.01.003
Pabis, K., Barardo, D., Sirbu,O., Selvarajoo, K., Gruber, J. & Kennedy, B. K. (2024). A concerted increase in readthrough and intron retention drives transposon expression during aging and senescence. eLife, 12:RP87811, doi: https://doi.org/10.7554/eLife.87811.2.
2023
Pabis,K., Barardo, D., Gruber, J., Sirbu, O., Selvarajoo, K., Kaeberlein, M. & Kennedy, B. K. (2023). The impact of short-lived controls on the interpretation of lifespan experiments and progress in geroscience. bioRxiv 2023.10.08.561459; doi: https://doi.org/10.1101/2023.10.08.561459
Selvarajoo, K. & Giuliani, A. (2023). Systems Biology and Omics Approaches for Complex Human Diseases. Biomolecules, 13(7), 1080, doi: https://doi.org/10.3390/biom13071080.
Sirbu, O., Helmy, M., Giuliani, A., & Selvarajoo, K. (2023). Globally invariant behavior of oncogenes and random genes in cell populations but not at single cell level. npj Systems Biology & Applications, doi: https://doi.org/10.1038/s41540-023-00290-9.
Helmy, M., Elhalis, H., Liu, Y., Chow, Y. & Selvarajoo, K. (2023). Perspective: Multi-omics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae. Advances in Nutrition, doi: https://doi.org/10.1016/j.advnut.2022.11.002.
Helmy, M. & Selvarajoo, K. (2023). Application of GeneCloudOmics: Transcriptomics Data Analytics for Synthetic Biology. In K. Selvarajoo (Ed.), Methods in Molecular Biology (pp. 221-264). New York: Springer, ISBN: 978-1071626160, doi: https://doi.org/10.1007/978-1-0716-2617-7_12.
2022
Khanijou, J. K., Kulyk, H., Bergès, C. et al. (2022). Metabolomics and modelling approaches for systems metabolic engineering. Metabolic Engineering Communications, 15, e00209, doi: https://doi.org/10.1016/j.mec.2022.e00209.
Selvarajoo, K. (Ed.). (2022). Computational Biology and Machine Learning Approaches for Metabolic Engineering and Synthetic Biology. Methods in Molecular Biology, Springer, New York, ISBN: 978-1071626160.
Yeo, H. C . & Selvarajoo, K. (2022). Machine learning alternative to systems biology should not solely depend on data. Briefings in Bioinformatics, doi: https://doi.org/10.1093/bib/bbac436.
Smith, D. J., Helmy, M., Lindley, N. D. & Selvarajoo, K. (2022). The transformation of our food system using cellular agriculture: What lies ahead and who will lead it? Trends in Food Science & Technology, doi: https://doi.org/10.1016/j.tifs.2022.04.015.
Giuliani, A., Bui, T. T., Helmy, M. & Selvarajoo, K. (2022). Identifying toggle genes from transcriptome-wide scatter: A new perspective for biological regulation. Genomics, 114(1), 215-228, doi: https://doi.org/10.1016/j.ygeno.2021.11.027.
2021
Selvarajoo, K. (2021). The need for integrated systems biology approaches for biotechnological applications. Biotechnology Notes, 2, 39-43, doi: https://doi.org/10.1016/j.biotno.2021.08.002.
Helmy, M. & Selvarajoo, K. (2021). Systems Biology to Understand and Regulate Human Retroviral Proinflammatory Response. Frontiers in Immunology, doi: https://doi.org/10.3389/fimmu.2021.736349.
Helmy, M., Rahul, A., Mohamed, S., Ali Javed, Bui, T. T. & Selvarajoo, K. (2021). GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis. Frontiers in Bioinformatics, doi: https://doi.org/10.3389/fbinf.2021.693836.
Selvarajoo, K. (2021). Searching for unifying laws of general adaptation syndrome: Comment on "Dynamic and thermodynamic models of adaptation" by Gorban et al. Physics of Life Reviews, 37, 97-99, doi: https://doi.org/10.1016/j.plrev.2021.04.001.
2020
Helmy, M., Smith, D. & Selvarajoo, K. (2020). Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering. Metabolic Engineering Communications, 11, e00149, doi: https://doi.org/10.1016/j.mec.2020.e00149.
Bui, T. T., Lee, D. & Selvarajoo, K. (2020). ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes. Scientific Reports, 10, doi: https://doi.org/10.1038/s41598-020-74564-1.
Selvarajoo, K. (2020). Systems Biology Approaches for Understanding Biofilm Response. In S. S. Dhiman (Ed.), Quorum Sensing - Microbial Rules of Life (pp. 9-29). Washington: ACS Publications, doi: https://doi.org/10.1021/bk-2020-1374.ch002.
Bui, T. T. & Selvarajoo, K. (2020). Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli. Scientific Reports, 10, doi: https://doi.org/10.1038/s41598-020-62804-3.