2023

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.

Pabis, K., Barardo, D., Selvarajoo, K., Gruber, J. & Kennedy, B. K. (2023). 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.87811.1.

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.

2019

Deveaux, W. & Selvarajoo, K. (2019). Searching for simple rules in Pseudomonas aeruginosa biofilm formation. BMC Research Notes, 12, doi: https://doi.org/10.1186/s13104-019-4795-x.

Selvarajoo, K. (2019). Large-scale-free network organisation is likely key for biofilm phase transition. Engineering Biology, 3(4), 67-71, doi:  https://doi.org/10.1049/enb.2019.0012.

Deveaux, W., Hayashi, K. & Selvarajoo, K. (2019). Defining rules for cancer cell proliferation in TRAIL stimulation. NPJ Systems Biology and Applications, 5, doi: https://doi.org/10.1038/s41540-019-0084-5.

Zou, T., Bui, T. T. & Selvarajoo, K. (2019). ABioTrans: A Biostatistical Tool for Transcriptomics Analysis. Frontiers in Genetics, 10, doi: https://doi.org/10.3389/fgene.2019.00499.

Piras, V., Chiow, A. & Selvarajoo, K. (2019). Long Range Order and Short Range Disorder in Saccharomyces cerevisiae Biofilm. Engineering Biology, 3, 12-19, doi: https://doi.org/10.1101/451062.

Selvarajoo, K. (2019). Variability that causes collective behavior. Organisms. Journal of Biological Sciences, 3(1), 15, doi: https://doi.org/10.13133/2532-5876_5.4.

2018

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: https://doi.org/10.13133/2532-5876_4.6.

Selvarajoo, K. (2018). Order Parameter in Bacterial Biofilm Adaptive Response. Frontiers in Microbiology, 9, doi: https://doi.org/10.3389/fmicb.2018.01721.

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: https://doi.org/10.1007/978-1-4939-7456-6_9.

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: https://doi.org/10.1007/978-3-319-92967-5_16.