Computational Biology & Omics Lab

The emergence of the systems biology field in recent decades marks a radical methodological and conceptual shift in the way living organisms are studied. Where much has and continues to be learned from traditional approaches in understanding the individual processes of living organisms, systems biology aims to shed light on the unknowns of gene regulations, signal transductions and metabolic networks by considering them more holistically. Though generally thought to be too convoluted to permit predictability, the mathematical and computational tools and techniques of systems biology are increasingly allowing this new field of research to make reasonably accurate estimations about the inner regulations and behaviors of life.

The Computational Biology and Omics laboratory embraces systems biology, and is interested in understanding biological complexities such as disease onset and progression, non-linear self-organization, emergent behaviors as well as synthetic biology and biotechnological applications. Thus, our team members are highly skilled in mathematics, computer programming, data analytics and machine learning; to develop original methods for the analyses of high-throughput and time-series transcriptomics, proteomics, and metabolomics datasets and for modeling biological network dynamics across diverse cell types. To briefly illustrate, dynamic metabolic networks models are developed to predict how we can increase production of certain compounds of interest such as limonene (Figure 1). Space-time models are also used to infer post-treatment effects and reveal state transitions (Figure 2). Such approaches reduce laborious experiments, thereby saving valuable time and cost for projects.

On the other hand, to tackle large-scale omics data, we are developing cutting-edge methodologies to interpret the complex and multi-dimensional datasets. For example, data-driven and process- driven synthetic data generators are being developed to predict the transcriptional patterns and mechanisms of differentially expressed genes between control and disease (Figure 3). Moreover, to integrate dynamic models with machine learning models, we are working on Digital Twin approaches to deal with personalized variations between individuals in any disease and for biotechnological applications (Figure 4).

Our lab closely collaborates and works with different groups within A*STAR, local and international universities as well as with industries. Although we take pride in our primary research goals in systems biology, we share our experiences with other teams in a highly collaborative manner for synergistic outcomes.

Figure 2: Space-time simulation of cancer proliferation in untreated and treated condition, revealing epithelial- mesenchymal state transition [Deveaux et al, 2019]

• Selvarajoo K* & Maurer-Stroh. Towards multi-omics synthetic data integration. Brief Bioinform. 25:3, bbae213, (2024).


• Helmy M, Elhalis H, Rashid MM, Selvarajoo K*. Can Digital Twin Efforts Shape Microorganisms-based Alternative Food? Curr Opin Biotechnol. 87:103115 (2024).


• Khanijou JK, Hee YT, Selvarajoo K*. Methods Mol Biol., 2745:3-19 (2024). Identifying Key In Silico Knockout for Enhancement of Limonene Yield Through Dynamic Metabolic Modelling.


• Pabis K, Barardo D, Sirbu O, Selvarajoo K, Gruber J & Kennedy BK*. A concerted increase in readthrough and intron retention drives transposon expression during aging and senescence.  eLife, 12:RP87811 (2024).


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


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


• Sirbu O, Helmy M, Giuliani A, Selvarajoo K*. Globally invariant behavior of oncogenes and random genes at population but not at single cell level. NPJ Syst Biol Appl. 9:28 (2023).


• Helmy M, Elhalis H, Yan L, Chow Y, Selvarajoo K*. Perspective: Multi-omics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae. Adv Nutr., 14:1-11 (2023).


• Khanijou JK, Kulyk H, Bergès C, Khoo LW, Ng P, Yeo HC, Helmy M, Bellvert F, Chew W, Selvarajoo K. Metabolomics and modelling approaches for systems metabolic engineering. Metabolic Engineering Communications, 15, e00209 (2022).


• Selvarajoo K. (Ed.). Computational Biology and Machine Learning Approaches for Metabolic Engineering and Synthetic Biology. Methods in Molecular Biology, Springer, New York, ISBN: 978-1071626160 (2022).


• Helmy M, Selvarajoo K. 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 (2022).


• Yeo HC, Selvarajoo K. Machine learning alternative to systems biology should not solely depend on data. Briefings in Bioinformatics, 23: bbac436 (2022).


• Smith DJ, Helmy M, Lindley ND, Selvarajoo K. The transformation of our food system using cellular agriculture: What lies ahead and who will lead it? Trends in Food Science & Technology, 127:368-376 (2022).


• Guiliani A, Bui TT, Helmy M, Selvarajoo K. Identifying toggle genes from transcriptome-wide scatter: A new perspective for biological regulation. Genomics, 114:215-228 (2022).


• Helmy M, Agrawal R, Soudy M, Bui TT, Selvarajoo K. GeneCloudOmics: A Data Analytic Cloud Platform for High-throughput Gene Expression Analysis. Front Bioinform., 1:693836 (2021).


• Helmy M, Selvarajoo K. Systems Biology to Understand and Regulate Human Retroviral Proinflammatory Response. Front Immunol., 12:736349 (2021).


• Selvarajoo K. The Need for Integrated Systems Biology Approaches for Biotechnological Applications. Biotechnol. Notes, 2:39-43 (2021).


• Selvarajoo K. Searching for unifying laws of general adaptation syndrome: Comment on "Dynamic and thermodynamic models of adaptation" by Gorban et al. Phys Life Rev., 37:97-99 (2021).


• Helmy M, Smith D, Selvarajoo K. Systems biology approaches integrated with artificial intelligence for optimized food-focused metabolic engineering.  Metab Eng Commun., 11: e00149 (2020).


• Bui TT, Selvarajoo K.  Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli.  Sci Rep., 10:5878 (2020).


• Deveaux W, Hayashi K, Selvarajoo K.  Defining Rules for Cancer Cell Proliferation in TRAIL Stimulation.  NPJ Syst Biol& Appl., 5:5 (2019).


• Deveaux W, Selvarajoo K. Searching for Simple Rules in Pseudomonas aeruginosa Biofilm Formation. BMC Res Notes, 12:763 (2019).


• Selvarajoo K.  Complexity of Biochemical and Genetic Responses Reduced Using Simple Theoretical Models.  Methods Mol Biol., 1702:171-201 (2018).


• Piras V, Selvarajoo K.  The Reduction of Gene Expression Variability from Single Cells to Populations follows Simple Statistical Laws.  Genomics, 105(3):137-144 (2015).


• Piras V, Tomita M, Selvarajoo K.  Transcriptome-wide Variability in Single Embryonic Development Cells.  Sci. Rep., 4:7137 (2014).


• Selvarajoo K, Tomita M.  Physical Laws Shape Biology.  Science 339:646 (2013).


• Selvarajoo K.  Immuno Systems Biology: A Macroscopic Approach for Immune Cell Signaling.  Springer New York, ISBN: 978-1461474593 (2013).


• Selvarajoo K.  Discovering Differential Activation Machinery of the Toll-Like Receptor (TLR) 4 Signaling Pathways in MyD88 Knockouts.  FEBS Lett., 580:1457-1464 (2006).