Rapid Forward Engineering of Biological Systems: Part 2 Combinatorial Optimization

Many of the governing rules of biology are still not known nor well understood. Thus, forward engineering of biological systems will continue to rely on empirical methods. A central challenge in forward engineering is to find genetic interventions that lead to improved function. Combinatorial optimization (fueled by large-scale diversity generation) takes ideas that show promise and recombines them in novel configurations to leverage the principles of evolutionary optimization. This optimization process is further accelerated through strategies that incorporate machine learning. Such models are interrogated to efficiently guide new library designs. Strategies and tools to effectively apply the principles of diversity generation (and combinatorial optimization) have been developed over the last two decades and are now routinely used to rapidly engineer biological systems.