A retrosynthetic biology approach to metabolic pathway design for therapeutic production

In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.Synthetic biology is being used for therapeutic production either to develop cell factories using industrial microorganisms [1,2] or to synthesize genetic circuits allowing in situ therapeutic delivery [3]. Recombinant DNA technology has already provided the ability to genetically engineer cell strains in order to import pathways from other organisms capable of producing small molecule chemicals into microbial chassis. Moreover, to estimate the efficiency of the overall process, metabolic engineering-based tools consider models of cell metabolism as a whole, allowing the identification and redesign of bottlenecks in the biosynthetic pathways. Therefore, t