Systems biological Analysis of wateR treAtment proceSsing ViA Transcriptomic data and Bayesian Inference (SARASVATI)

In the water treatment process chain, waste water treatment companies clean up polluted sewage water before discharging into the environment, while drinking water companies treat surface or ground water for human consumption. Due the complex treatment steps in this process chain, it remains a challenge to monitor the microbiological activities and their causal effect to the water quality such as biofilm formation, turbidity and sedimentation in drinking water or micropollutant-degrading activities in waste water. While most of the chemical, physical and main microbial processes for water technology are well-known, this project will unravel the microbial activities to a much deeper level via automated kinetic reconstruction of carbon degradation pathways. We will develop virtual sensor systems based on the reconstructed kinetic models of these metabolic pathways which allow us to monitor water quality by detecting the presence and estimating the growth of microbial communities that maintain stoichiometric homeostasis involving carbon sources. It contributes to the development of an accurate digital twin of the water treatment process chain where metagenomic data and process data are integrated seamlessly with the glass- box/surrogate models.