BackgroundAccurate cellular models are critical for understanding tissue function and accelerating drug discovery. While in vitro systems like spheroids and organoids are widely used, they are costly, low-throughput, and often lack reproducibility. Here, we introduce Evolvoid, a computational pipeline based on genetic algorithms that generates virtual 3D spheroid-like cell constructs with optimized morphologies by simulating key biophysical principles - such as thermodynamic principles, nutrient transport and their uptake by cells.ResultsBy integrating finite element simulations with evolutionary principles, our in silico platform evolves populations of randomly generated shapes, iteratively selecting individuals that best satisfy a biophysically informed fitness function. The fitness function encodes selection pressures based on general biophysical constraints - namely, the minimization of surface energy and the maintenance of cell viability under variable oxygen conditions. The Shannon entropy is used to track genome complexity over generations. In consistence with evolutionary dynamics, the complexity increases progressively. Moreover, albeit based on basic biophysical optimization rules, the fittest individuals generated by Evolvoid closely resemble the shape and size of cellular spheroids obtained in vitro.ConclusionEvolvoid is modular, scalable, and tunable to different construct sizes, cell types, or culture conditions, and thus provides a versatile platform for designing and optimizing 3D cellular models entirely in silico. It offers a foundation for developing high-fidelity, cost-effective digital shape twins of biological systems, supporting the advancement of lab-on-a-laptop technologies and new approach methodologies, thereby reducing the reliance on in vitro and animal models.
更多
原文链接
分享
引用
