Abstract
Ph.D. (Biochemistry)
The complementary roles of hypothesis- and data-driven (deductive and inductive) science in
the post-genomic era have become a reality, with a resurgence of interest in metabolism and
the explosion of systems biology methodologies. This has led to a paradigm shift on how
biology research is being conducted; and the aspiration and possibilities to understand the
nature of life at the molecular level have never looked better (Kell & Oliver, 2004; Strange,
2005; Ray, 2010). In the post-genomic era, biological science has thus become increasingly
more data-intensive (big-data) science, driving the “cycle of knowledge” via a data-driven
(inductive) reasoning to generate new hypotheses and insights. The systems biology
approaches – omics layers: genomics, transcriptomics, proteomics and metabolomics – have
become the essential strategies driving the search for fully describing and understanding the
complex and dynamic metabolism of a biological system in toto (Goodacre, 2005).
Metabolism, being a cornerstone of life – with its intertwined network of enzyme-catalysed
biochemical reactions – is also a major source of cellular information that integrates
environmental cues with intracellular signals to coordinate decisions in processes such as
nutrient utilisation, signalling or differentiation. Furthermore, the regulation of metabolism is
highly complex and dynamic as it entails several interconnected layers of translational and
transcriptional mechanisms to eventually affect enzyme thermodynamics and kinetics (Ray,
2010; Fuhrer & Zamboni, 2015). This reawakening of interest in metabolism stems from
acknowledging the etiolation of the field by the cloud of molecular biology (predominated by
the philosophical preference of deductive reasoning), and the realisation of the impossibility to
address and answer numerous biological questions without tapping into the dynamics of
metabolism. Recognising the reciprocal regulation of metabolism and other cellular processes
– investigated through systems biology strategies – has revolutionised and is advancing our
understanding of complex physiology, aiming at a comprehensive representation of biological
systems in their respective ever-changing environments (Vidal, 2009; McKnight, 2010; Ray,
2010; Nanda et al., 2011; Sévin et al., 2015)...