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<p>Understanding how sequence changes shape biological function is a central challenge in bioengineering, but the vast number of possible sequences makes exhaustive experimental testing of genetic parts infeasible. Advances in high-throughput experimentation and machine learning allow new and disruptive ways to address this conundrum. We highlight how these technologies can synergize to enable a shift from empiric, case-by-case optimization to the systematic, data-driven design of genetic parts.</p>

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Genetische Bauteile à la carte – Lernen von Hochdurchsatz-Experimenten

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Nicolas A. Jaenisch,
  • Markus Jeschek

摘要

Understanding how sequence changes shape biological function is a central challenge in bioengineering, but the vast number of possible sequences makes exhaustive experimental testing of genetic parts infeasible. Advances in high-throughput experimentation and machine learning allow new and disruptive ways to address this conundrum. We highlight how these technologies can synergize to enable a shift from empiric, case-by-case optimization to the systematic, data-driven design of genetic parts.