"The widespread use of fossil based fuels and plastics is a major cause of environmental pollution, and their production depends on limited non-renewable oil resources. The immense diversity of catalytic processes encoded in the genomes of sequenced organisms holds the promise of developing biotechnological systems for the efficient production of renewable energy sources and degradable materials via genetic engineering. However, the combinatorial possibilities for integrating enzyme-coding genes from different organisms into a microbial host cannot be explored systematically by classical approaches, which rely on the manual review of functional gene annotations.We propose to integrate a novel computational method for predicting combinations of genes facilitating efficient product yield with experimental approaches for genetic engineering. The method takes into account virtually all combinations of genes from sequenced organisms while avoiding the combinatorial explosion of their enumeration. We iteratively validate and refine the method by application to the genetic engineering of the well-characterised P. putida KT2440 for the production of polyhydroxyalkanoates, important precursors for biodegradable materials. Next, we apply the refined method to the solvent tolerant P. putida DOT-T1E for the production of n-Butanol, a promising next generation biofuel due to its high energy density, safe handling, and the possibility of direct use in gasoline motors.The interdisciplinary integration of theoretical and experimental research within the project is a promising approach for addressing the modern challenges in developing new biotechnological production systems. The project will lead to the discovery of engineered strains for the efficient production of biofuel and degradable materials, enabling novel industrial processes in green chemistry with the potential of alleviating environmental pollution and promoting economic independence from oil production."