(in the last 5 years)

Full publications:


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

Yeo HC, Vijay V, Selvarajoo K (2024) Identifying effective evolutionary strategies for uncovering reaction kinetic parameters under the effect of measurement noises. bioRxiv 2024.03.05.583637

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.

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:


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:

Selvarajoo, K. & Giuliani, A. (2023). Systems Biology and Omics Approaches for Complex Human Diseases. Biomolecules, 13(7), 1080, doi:

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:

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:

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:


Khanijou, J. K., Kulyk, H., Bergès, C. et al. (2022). Metabolomics and modelling approaches for systems metabolic engineering. Metabolic Engineering Communications, 15, e00209, doi:

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:

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:

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:


Selvarajoo, K. (2021). The need for integrated systems biology approaches for biotechnological applications. Biotechnology Notes, 2, 39-43, doi:

Helmy, M. & Selvarajoo, K. (2021). Systems Biology to Understand and Regulate Human Retroviral Proinflammatory Response. Frontiers in Immunology, doi:

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:

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:


Helmy, M., Smith, D. & Selvarajoo, K. (2020). Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering. Metabolic Engineering Communications, 11, e00149, doi:

Bui, T. T., Lee, D. & Selvarajoo, K. (2020). ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes. Scientific Reports, 10, doi:

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:

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:


Deveaux, W. & Selvarajoo, K. (2019). Searching for simple rules in Pseudomonas aeruginosa biofilm formation. BMC Research Notes, 12, doi:

Selvarajoo, K. (2019). Large-scale-free network organisation is likely key for biofilm phase transition. Engineering Biology, 3(4), 67-71, doi:

Deveaux, W., Hayashi, K. & Selvarajoo, K. (2019). Defining rules for cancer cell proliferation in TRAIL stimulation. NPJ Systems Biology and Applications, 5, doi:

Zou, T., Bui, T. T. & Selvarajoo, K. (2019). ABioTrans: A Biostatistical Tool for Transcriptomics Analysis. Frontiers in Genetics, 10, doi:

Piras, V., Chiow, A. & Selvarajoo, K. (2019). Long Range Order and Short Range Disorder in Saccharomyces cerevisiae Biofilm. Engineering Biology, 3, 12-19, doi:

Selvarajoo, K. (2019). Variability that causes collective behavior. Organisms. Journal of Biological Sciences, 3(1), 15, doi:


Bui, T. T., Giuliani, A. & Selvarajoo, K. (2018). Statistical Distribution as a Way for Lower Gene Expressions Threshold Cutoff. Organisms. Journal of Biological Sciences, 2(2), 55-58, doi:

Selvarajoo, K. (2018). Order Parameter in Bacterial Biofilm Adaptive Response. Frontiers in Microbiology, 9, doi:

Selvarajoo, K. (2018). Complexity of Biochemical and Genetic Responses Reduced Using Simple Theoretical Models. In M. Bizzarri (Ed.), Systems Biology. Methods in Molecular Biology, vol 1702 (pp. 171-201). New York: Humana Press, doi:

Selvarajoo, K., Piras, V. & Giuliani, A. (2018). Hints from Information Theory for Analyzing Dynamic and High-Dimensional Biological Data. In N. Rajewsky, S. Jurga & J. Barciszewski (Eds.), Systems Biology. RNA Technologies (pp. 313-336). Springer, Cham, doi: