Date/Time: 16th of October 2018, 13:00-14:00

Venue: CBCB Baddiley-Clark Building, large meeting room level 2

Abstract

In order to accelerate the discovery and optimization of biosynthetic pathways, we have developed an integrated Design–Build-Test–Learn (DBTL) pipeline for microbial chemical production, which is designed to be compound agnostic and automated throughout. This talk will focus on the Design and Learn steps of the cycle and how they interface with the rest of the pipeline. By focusing on specific areas of the design space, machine-learning can provide transferability of results from the set of circuit prototypes characterized in the initial pilot tests into the design of the circuitry employed at the industrial phase. The resulting toolbox of predictive design tools underpins the pipeline by enabling chemical diversity expansion through pathway discovery and the selection and fine-tuning of genetic circuits. Results of the application of the predictive pipeline to the production of high-value bio-based compounds will be showcased during the talk.

 

Short Bio

Dr. Pablo Carbonell is a senior staff scientist at SynBioChem Centre, Manchester Institute of Biotechnology (The University of Manchester). PhD in Control Engineering from Polytechnic University of Valencia, Research Habilitation in Systems Biology from Paris-Saclay. Pablo has held a number of fellowships and visiting appointments, e.g., at the Institute of Systems and Synthetic Biology (iSSB) in France, New York University Polytechnic School of Engineering (NYU-Poly), Barcelona Pompeu Fabra University and Fujirebio Inc (Tokyo). Research areas include automated design for metabolic engineering and synthetic biology, machine learning and control engineering. Pablo has contributed to the development of several bioretrosynthesis-based pathway design tools, including RetroPath, XTMS, EcoliTox, enzyme selection Selenzyme, biosensor design SensiPath and protein design Promis.