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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...
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Updated: Mar 1, 2026

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Evolvoid: a genetic algorithm for shaping optimal cellular constructs.

Piera Mancini1, Flavio Fontana1,2, Ermes Botte1,2

  • 1Research Center "E. Piaggio", University of Pisa, Largo Lucio Lazzarino 1, 56122, Pisa, Italy.

Journal of Biological Engineering
|February 27, 2026
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Summary
This summary is machine-generated.

Evolvoid, a novel computational pipeline, uses genetic algorithms to create virtual 3D cell constructs. This cost-effective, in silico method optimizes cellular models for drug discovery and biological research.

Keywords:
3D cellular constructsDigital twinsGenetic algorithmsIn silico models

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Area of Science:

  • Computational Biology
  • Biophysics

Background:

  • Accurate cellular models are crucial for tissue function and drug discovery.
  • Current in vitro models (spheroids, organoids) are expensive, low-throughput, and lack reproducibility.
  • Evolvoid is a computational pipeline using genetic algorithms to generate virtual 3D cell constructs.

Purpose of the Study:

  • To develop a cost-effective and reproducible method for designing 3D cellular models.
  • To optimize virtual cell construct morphologies by simulating biophysical principles.
  • To reduce reliance on in vitro and animal models in research.

Main Methods:

  • Integrates finite element simulations with evolutionary principles.
  • Employs genetic algorithms to evolve populations of random shapes.
  • Utilizes a fitness function based on surface energy minimization and cell viability under oxygen gradients.

Main Results:

  • Evolvoid generates virtual 3D spheroid-like constructs with optimized morphologies.
  • Fitness function incorporates thermodynamic principles, nutrient transport, and cell uptake.
  • Evolved individuals exhibit shapes and sizes similar to in vitro spheroids.
  • Shannon entropy tracks increasing genome complexity consistent with evolutionary dynamics.

Conclusions:

  • Evolvoid is a modular, scalable, and tunable platform for in silico design of 3D cellular models.
  • Enables the creation of high-fidelity digital twins of biological systems.
  • Supports advancements in lab-on-a-laptop technologies and New Approach Methodologies (NAMs).