Back

Evolvoid: A genetic algorithm for shaping optimal cellular constructs

Mancini, P.; Fontana, F.; Botte, E.; Magliaro, C.; Ahluwalia, A.

2024-09-26 bioengineering
10.1101/2024.09.24.614676 bioRxiv
Show abstract

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.

Published in Journal of Biological Engineering (predicted rank #18) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.