ArticleJournal of biological engineering2026
Evolvoid: a genetic algorithm for shaping optimal cellular constructs.
Article in Journal of biological engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A hybrid computational model of cancer spheroid growth with ribose-induced collagen stiffening.Frontiers in bioengineering and biotechnology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.