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Synthetic Biology02:55

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Using Synthetic Biology to Engineer Living Cells That Interface with Programmable Materials
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Bioengineering hybrid artificial life.

Innocent Sibanda1, Geoff Nitschke1

  • 1Department of Computer Science, University of Cape Town, Cape Town, South Africa.

Frontiers in Bioinformatics
|January 1, 2026
PubMed
Summary
This summary is machine-generated.

Synthetic biology aims to engineer life, but creating adaptable artificial life (ALife) faces challenges in directed evolution. A hybrid approach combining digital and biological methods is proposed to enable evolving synthetic ALife.

Keywords:
Artificial lifedirected evolutionevolutionary algorithmsfitness landscapesythetic biology

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Area of Science:

  • Bioengineering and synthetic biology focus on redesigning biological systems.
  • Artificial Life (ALife) explores life as it could be, with synthetic biology enabling engineered life.

Background:

  • Synthetic biology aims to engineer biological systems for specific applications.
  • Artificial Life (ALife) research seeks to understand and design living systems, including 'life as it could be'.
  • Current synthetic (biological) ALife lacks the adaptability of digital ALife.

Purpose of the Study:

  • To address limitations in directed evolution, fitness landscape mapping, and fitness approximation for synthetic ALife.
  • To review open challenges in directed evolution, genetic diversity, fitness mapping, and estimation for synthetic ALife.
  • To propose a hybrid synthetic ALife design methodology.

Main Methods:

  • Examining open challenges in directed evolution for synthetic ALife.
  • Reviewing methods for genetic diversity generation, fitness mapping, and estimation.
  • Outlining future directions for a hybrid synthetic ALife design methodology.

Main Results:

  • Significant advances in synthetic biology have not yet yielded practical, evolving, and adaptable synthetic ALife.
  • Limitations in directed evolution, fitness landscape mapping, and approximation hinder the development of problem-solving synthetic ALife.
  • Digital ALife demonstrates continuous adaptation and evolution, unlike current synthetic ALife.

Conclusions:

  • Overcoming directed evolution deficiencies is crucial for bioengineering problem-solving ALife.
  • A hybrid design methodology combining digital and synthetic evolutionary methods is proposed.
  • This hybrid approach aims to create evolving and adaptable synthetic ALife.