Computational Biology & Omics Lab



The emergence of systems biology field in recent decades marks a radical methodological and conceptual shift in the way living systems 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 tools and techniques of systems biology are increasingly allowing this new field of research to make reasonably accurate estimations about the inner regulations 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 applications. Thus, our team members are highly skilled in mathematics, computer programming and data analytics; to develop original methods for the analyses of highthroughput 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. Transcriptomic data analyses uncover shifts in gene expression profiles in response to external stimuli, identifying genes crucial for cell state transition (Figure 3). The use of machine learning algorithms such as selforganizing maps (SOM) allow us to compare and differentiate the transcriptomic signatures between cancers and healthy cells (Figure 4). Lastly, for the purpose of pathway optimization, we are looking to develop hybrid AI and mechanistic approaches that are efficient but remain explainable (contrary to blackbox modelling; Yeo & Selvarajoo, 2022).

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 1: Dynamic modelling, using differential equations, for enhancing limonene biosynthesis, with corresponding experimental data to test model predictions.

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

Figure 3: Scatterplots between untreated and treated samples show a transcriptomic shift in response to the external stimuli of a drug.
Figure 4: SOM of high-grade ovarian cancer samples vs healthy fallopian tube tissue from different patients. The SOM portraits show high variability between samples of the same cancer type and low variability between normal samples from different patients.


Senior Principal Investigator SELVARAJOO Kumar   |    [View Bio]  
Scientist RASHID Md Mamunur 
Senior Scientist YEO Hock Chuan 
Research Officer SIRBU Olga 
PhD Student PABIS Kamil Konrad
PhD Student LEE  Shi Mun
PhD Student SIM Clarence

Selected Publications