QTB Team Details

Photo of Suraj  Sharma


Suraj Sharma M.Tech

Quantitative und Theoretische Biologie
Heinrich-Heine-Universität Düsseldorf
Universitätsstraße 1
Gebäude: 25.32
Etage/Raum: 03.30
Tel.: +49 211 81-10174


PhD Project: Mathematical models of glucosinolate metabolism in plants

Glucosinolates (GSLs) are sulphur- and nitrogen-rich secondary metabolites, predominantly found in plants of the Brassicaceae family. Their breakdown products facilitate resistance to herbivores and pathogens. Therefore, understanding GSL metabolism is key to understand plant-microbe interactions. Considering the number of interactions in secondary metabolism, in principle, an infinite number of products could be produced. However, as a consequence of heterogenous natural selection >130 structurally-different GSLs are found across different plant species. A primary difficulty in the analysis of secondary metabolites is the vast diversity of chemical structures. Apparently, developing models in which all possible structures are represented as a single variable is very challenging. In the present study, we developed a mathematical model of the biosynthesis of the aliphatic GSLs, which are derived from methionine (Met), found in Arabidopsis thaliana. Our model exemplifies how the biosynthetic fluxes in the system depend on all other metabolite concentrations, a behaviour originating from the broad substrate specificity of the involved enzymes. Furthermore, the observed variation in GSL accumulation across various Arabidopsis ecotypes could be a result of the allelic composition at different glucosinolate biosynthetic loci. Addressing the diversity induced by the chain-elongation mechanism, our model elucidates how a specific class of chain-elongated products with a particular frequency is produced. Moreover, by relating the allelic differences to metabolic diversity, which in turn corresponds to different GSL profiles, our model provides a framework wherein the connection between genotype and phenotype can be investigated.

Side projects: Development of artificial intelligence based modelling applications

The principal aim is to employ feature-rich and attractive artificial intelligence (AI) based formalisms, as a promising candidate for the development of data-driven modelling applications for bioprocesses. As very often, the detailed knowledge about the physico-chemical phenomena (kinetics, mass transfer, thermodynamics, etc.) underlying the process is not available or gaining this knowledge is a tedious and costly task owing to the complex nature of the process and the extensive experimentation involved in collecting the necessary data. Thus, phenomenological modelling of such a system becomes very difficult. In such a situation, data-driven modelling can be resorted to for the development of modelling applications.

undefinedShort CV of Suraj Sharma


1.     Sharma, S., & Tambe, S. S. (2014). Soft-sensor development for biochemical systems using genetic programming. Biochemical Engineering Journal, 85, 89-100.

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