Scholarlink

<p>Edible mushrooms face persistent challenges in yield optimization, bioactive compound production, and climate resilience that conventional breeding methods struggle to address. Traditional approaches&#xa0;such as&#xa0;cross-breeding, protoplast fusion, and mutagenesis&#xa0;are limited by genetic noise, laborious screening, and unstable trait inheritance. This review proposes a transformative paradigm built upon converging advances in molecular biology and data science: the bio-digital feedback loop (BDFL) framework, integrating multi-omics, CRISPR-engineered chassis strains, and predictive phenomics for precision mushroom breeding. Our framework employs multi-omics to decipher gene networks governing critical traits, such as substrate degradation enzymes, developmental synchrony regulators, and secondary metabolite pathways. CRISPR-Cas9 and synthetic biology tools then deploy these insights to verify and design modular gene circuits in pre-engineered "plug-and-play" chassis strains, enabling conflict-free stacking of desirable traits. Artificial intelligence serves as the linchpin, not only automating high-throughput phenotyping through advanced imaging but also accelerating the entire breeding cycle by predicting trait heritability from omics data and optimizing the design of CRISPR guide RNAs and genetic constructs for efficient editing. The BDFL we describe iteratively refines strains by feeding phenomics data back into AI algorithms, enabling rapid trait optimization cycles. This transcends the trial-and-error limitations of classical methods, accelerating development of climate-smart mushrooms for circular bioeconomies&#xa0;including&#xa0;strains engineered to thrive on agricultural waste, overproduce immunomodulatory compounds, or resist emerging pathogens. The integration of predictive genomics, AI-driven phenomics, and CRISPR-edited chassis strains heralds a new era of precision mycology, where mushrooms are computationally designed as sustainable solutions for global food security, pharmaceutical innovation, and ecological resilience,&#xa0;ultimately transforming fungi into programmable biological factories tailored to address pressing agricultural and ecological challenges.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Bio-digital feedback loop systems: a synergistic integration of predictive genomics, genome editing, and AI-driven phenomic synthesis for next-generation edible and medicinal mushroom breeding

  • 期刊论文

  • 发表日期：

    2025年10月10日

查看全文
  • Ankan Das,
  • Sandip Debnath,
  • Sourish Pramanik,
  • Fakhrul Islam Monshi,
  • Mehdi Rahimi

摘要

Edible mushrooms face persistent challenges in yield optimization, bioactive compound production, and climate resilience that conventional breeding methods struggle to address. Traditional approaches such as cross-breeding, protoplast fusion, and mutagenesis are limited by genetic noise, laborious screening, and unstable trait inheritance. This review proposes a transformative paradigm built upon converging advances in molecular biology and data science: the bio-digital feedback loop (BDFL) framework, integrating multi-omics, CRISPR-engineered chassis strains, and predictive phenomics for precision mushroom breeding. Our framework employs multi-omics to decipher gene networks governing critical traits, such as substrate degradation enzymes, developmental synchrony regulators, and secondary metabolite pathways. CRISPR-Cas9 and synthetic biology tools then deploy these insights to verify and design modular gene circuits in pre-engineered "plug-and-play" chassis strains, enabling conflict-free stacking of desirable traits. Artificial intelligence serves as the linchpin, not only automating high-throughput phenotyping through advanced imaging but also accelerating the entire breeding cycle by predicting trait heritability from omics data and optimizing the design of CRISPR guide RNAs and genetic constructs for efficient editing. The BDFL we describe iteratively refines strains by feeding phenomics data back into AI algorithms, enabling rapid trait optimization cycles. This transcends the trial-and-error limitations of classical methods, accelerating development of climate-smart mushrooms for circular bioeconomies including strains engineered to thrive on agricultural waste, overproduce immunomodulatory compounds, or resist emerging pathogens. The integration of predictive genomics, AI-driven phenomics, and CRISPR-edited chassis strains heralds a new era of precision mycology, where mushrooms are computationally designed as sustainable solutions for global food security, pharmaceutical innovation, and ecological resilience, ultimately transforming fungi into programmable biological factories tailored to address pressing agricultural and ecological challenges.