Evolvoid: A genetic algorithm for shaping optimal cellular constructs

We describe an in-silico pipeline, Evolvoid, based on Genetic Algorithms (GAs) for identifying the optimal morphologies of cell-laden constructs. Driven by an ad hoc selection rule (i.e., the so-called fitness function (FF)), Evolvoid iteratively identifies the characteristics (i.e., the genome) of the survival of the fittest individual of a given population throughout generations. The FF is based on universally observed biophysical laws, representing the optimal trade-off between i) high cell viability and robustness to changes in environmental oxygen and ii) a low surface energy. The Shannon entropy is used to evaluate genome complexity, with the most complex fittest individuals showing quantitative and qualitative biological resemblance to in vitro constructs. Evolvoid paves the way for the development of "lab on a laptop": high-fidelity and cost-effective digital twins of cellular constructs which could augment or even substitute costly in vitro models.