Bacterial genomes are the product of billions of years of evolutionary tinkering. This results in sprawling, context-dependent archives of solutions to problems the organism has encountered across diverse and fluctuating environments. The result is a wonderfully messy architecture: redundant pathways, cryptic regulatory networks, horizontally acquired elements, and hundreds to thousands of genes of unknown function. For the microbiologist, this complexity is a source of endless fascination. For the metabolic engineer, it can be an obstacle.
     Industrial biotechnology increasingly asks microbes to perform highly specified tasks such as converting defined substrates into target products with maximal efficiency and minimal metabolic overhead. Yet the organisms that are recruited for these purposes carry genomes shaped by entirely different selection pressures such as nutritional variation, environmental stressors, and heterogeneous and changing habitats. Some microbes have been domesticated over millennia through artificial selection, with yeast and lactic acid bacteria as canonical examples. But for many industrial applications, we need organisms to perform tasks for which no evolutionary precedent exists. We pluck them from soil, screen for the best performers, and deploy them, without addressing the fundamental mismatch between their evolutionary legacy and the objectives of our application.
     We are exploring an alternative approach that involves prediction of essential / non-essential genes in the genome, and systematically removes those determined to be non-essential. Rather than accepting the genome as given, we ask which components are necessary, which are expendable, and how we can systematically distinguish between them. The key challenge is functional uncertainty. A substantial fraction of genes in even the best-studied bacterial genomes remain of unknown function. Deletion-based phenotypic screening, combined with large-scale genome engineering via homologous recombination methods, offers a path forward: remove candidate elements, assess fitness and metabolic function, and iteratively build toward a streamlined chassis. Artificial Laboratory Evolution (ALE) experiments, coupled with rapid whole-genome sequencing, reveal which genomic regions can be further refined.
     As a case study, we will examine the engineering of the bacterium Pseudomonas putida KT2440 for conversion of dairy waste lactose into polyhydroxyalkanoate (PHA) bioplastic. P. putida is an attractive chassis. It is genetically tractable, metabolically versatile, and naturally tolerant of industrial stressors. But its genome encodes the capacity to transport and catabolize many carbon sources, none of which are relevant when lactose is the sole feedstock. This excess metabolic capacity consumes biosynthetic resources such as ATP, ribosomes, and membrane transporters, that could otherwise be directed toward product formation. Furthermore, it lacks the ability to metabolize both lactose and its breakdown product galactose. We discuss strategies for introducing lactose utilization capacity, redirecting central carbon flux toward PHA precursors, eliminating competing sinks, and reducing the genomic overhead that evolution has layered onto this organism over time.
     More broadly, this case study sits at the intersection of synthetic biology, artificial life, and evolutionary studies. Asking what a genome should contain to fulfil the requisite functions of the organism, rather than accepting what evolution built, provides insight into the evolutionary process itself. Furthermore, it provides an avenue to understanding how the principles of artificial life can guide us in building biological systems that are optimized for purpose rather than by history.