2024

Khanijou JK, Hee YT, Scipion CPM, Chen X, Selvarajoo K (2024). Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli. NPJ Syst Biol Appl., 10(1):109. doi:10.1038/s41540-024-00440-7

Yeo HC, Vijay V, Selvarajoo K (2024). Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises. BMC Biology, 22(1):235. doi:10.1186/s12915-024-02019-4

Pabis K, Barardo D, Gruber J, Sirbu O, Malavolta M, Selvarajoo K, Kaeberlein M, Kennedy BK (2004). 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, 101:102512. doi: 10.1016/j.arr.2024.102512

Rashid MM, Selvarajoo K (2024). Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data. Brief Bioinform., 25(4):bbae300. doi: 10.1093/bib/bbae300

Selvarajoo K, Maurer-Stroh S (2024). Towards multi-omics synthetic data integration. Brief Bioinform., 25(3):bbae213. 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., 87:103115. 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 (2024). Methods Mol Biol., 2745:3-19. doi: 10.1007/978-1-0716-3577-3_1

Elhalis H, Helmy M, Ho S, Leow S, Liu Y, Selvarajoo K, Chow Y (2024). Identifying Chlorella vulgaris and Chlorella sorokiniana as sustainable organisms to bioconvert glucosamine into valuable biomass, Biotechnology Notes, 55:13-22. doi.org/10.1016/j.biotno.2024.01.003

Pabis K, Barardo D, Sirbu O,  Selvarajoo K, Gruber J, Kennedy B (2024). A concerted increase in readthrough and intron retention drives transposon expression during aging and senescence. Elife, 12:RP87811, doi: doi.org/10.7554/eLife.87811.2.

Yusof Z, Tong YW, Selvarajoo K, Parakh SK, Foo SC (2025). Overcoming Challenges in Microalgal Bioprocessing through Data-driven and Computational Approaches. Curr Opin Food Sci. (in press) doi:10.1016/j.cofs.2024.101253 

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.