This program consists of 4 linked and interrelated sub-projects (SP) that involve 2 PhDs and 1 postdoc. The sub-projects include:

SP 1: Advanced analytics on computational experiments with a purpose to screen and interpret Design recommendations
SP 2: Reduction and clustering of in-silico curated kinetics with a purpose to connect theoretical and experimental experiments
SP 3: Mathematical models for the simultaneous optimization of reaction pathways and process intensification
SP 4: Formulation of Design models with a purpose to build biochemical systems that are resistant to industrial environments

PhD 1 will undertake research in SP1 & 2 that is motivated by data analytics and semantics (Fig.5). The purpose would be to extract knowledge from data, automate and systematize interactions between DBTL stages, and provide generic means to consolidate knowledge from Design experiments. PhD 2 will undertake research in SP 3 & 4 that is challenged by modelling and mathematical optimization. The purpose would be to expand metabolic engineering formulations to account for different reactor types, also to account for a simultaneous selection of pathways and process engineering. A post-doc with experience in advanced analytics would be necessary to support the two students, prepare datasets for training and communicate with the IBISBA platform managers. SP1 will explore supervised and unsupervised machine learning technology experimenting with different ML technologies, norms, graphs, and Linked Open Data structures originated from the TasCu ontology of IBISBA. SP2 will combine space reduction methods, clustering, and visualization to systematically connect observables of Test stages with hermodynamically curated kinetics produced in the parameter space of Design. SP3 will explore new discrete-continuous optimization models setting up superstructure schemes that will expand the existing set of degrees of freedom providing for selections of process equipment simultaneously with the reaction pathways. SP4 will convert conventional models on metabolomics to models accounting for engineering aspects such as micro-mixing. The models will be formulated as optimization models to suggest reaction modifications that yield robust choices for reaction and reactors.