AI-enhanced framework for optimizing CRISPR-Cas gene editing in crop biotechnology addressing regulatory challenges and opportunities in global agricultural practices
The ARO is introduced, an AI enhanced framework designed to support CRISPR Cas genome editing in crop biotechnology under biologically, regulatorily, and contextually constrained conditions that combines constrained optimization refinement, probabilistic uncertainty modeling, and adaptive regulatory planning.
Abstract
Introduction The integration of CRISPR Cas genome editing with artificial intelligence (AI) offers significant potential for crop biotechnology by supporting more precise and adaptive strategies for trait improvement under complex agricultural and regulatory conditions. However, the global governance of gene edited crops remains highly heterogeneous, creating major challenges for the development of frameworks that can jointly support optimization, uncertainty management, and regulatory alignment. Conventional approaches often lack the ability to account for evolving regulatory requirements and multi source uncertainties in a unified manner. Methods In this paper, we introduce the Adaptive Regulatory Optimizer (ARO), an AI enhanced framework designed to support CRISPR Cas genome editing in crop biotechnology under biologically, regulatorily, and contextually constrained conditions. The ARO consists of three interconnected modules: the Manifold Constrained Gene Editor, the Agent Driven Regulatory Planner, and the Uncertainty Propagation Filter. Together, these modules embed editing decisions within biologically feasible manifolds, incorporate jurisdiction aware regulatory planning, and model interacting uncertainties associated with gene editing and deployment contexts. The The framework combines constrained optimization refinement, probabilistic uncertainty modeling, and adaptive regulatory planning to provide a structured basis for compliance aware and context sensitive decision support. Results and discussion Experimental results on the evaluated datasets indicate that the ARO achieves improved performance on the selected metrics relative to the compared methods, while its architecture is explicitly designed to integrate regulatory constraints into the optimization process. These findings suggest that the proposed framework provides a promising foundation for supporting more transparent, adaptive, and analytically grounded decision making in CRISPR Cas applications for crop biotechnology.
This review provides a comprehensive synthesis of a recent advances in CRISPR–Cas technologies and their strategic applications in crop genetics and hybrid breeding, and showcases how these technologies accelerate hybrid breeding by engineering male sterility systems, fixing heterosis, and generating high-throughput mutant libraries for trait discovery.
Syed Riaz Ahmed, Jahangir Khan, I. Ijaz et al.· Frontiers in Plant Science· 0 citations
ABSTRACT Global agriculture is increasingly challenged by climate instability, genetic erosion, emerging pathogens and rising food demands, exposing the limitations of conventional breeding and traditional domestication strategies. Recent advances in CRISPR‐based genome editing, pangenomic, synthetic biology, artificial intelligence (AI)‐assisted breeding and predictive phenomics are transforming de novo domestication from a slow evolutionary process into a programmable framework for rational crop redesign. This review synthesises recent advances in programmable de novo domestication and highlights how crop wild relatives and underutilised germplasm can be harnessed to develop resilient, climate‐adaptive and sustainable crop systems. The integration of multiplex genome editing, pan‐genomic variation discovery, AI‐driven genomic prediction and predictive breeding enables precise engineering of key domestication traits governing plant architecture, yield potential, stress resilience and nutritional quality. Furthermore, we propose a trajectory‐based framework for programmable domestication comprising Adaptive Rescue, Agronomic Refinement and Novel Chassis Engineering, which illustrates distinct evolutionary pathways, engineering complexity and crop redesign objectives. We also examine the major system level challenges that constrain programmable domestication, including cryptic genetic variation, epistasis, gene regulatory network complexity, genotype phenotype predictability, biodiversity conservation and regulatory considerations. Collectively, programmable domestication represents a transformative shift from conventional crop improvement towards system‐level engineering of next‐generation crops, providing a strategic foundation for enhancing global food security, agricultural sustainability and environmental resilience in the face of accelerating climate change.
Muhammad Mubashar Zafar, H. Firdous, A. Siddiqua et al.· Plant Biotechnology Journal· 0 citations
Clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing and artificial intelligence (AI) are increasingly presented as a unified route to precision plant breeding. Their convergence is scientifically plausible but unevenly demonstrated. CRISPR systems can create targeted sequence changes, whereas AI can prioritise candidate genes, integrate genomic and phenomic data, optimise guide RNAs and editors, predict editing outcomes, and support iterative genotype-to-phenotype learning. This critical narrative review evaluates the evidence linking these capabilities across the crop-improvement pipeline. Literature published from 1 January 2012 to 5 June 2026 was selected through transparent searches of accessible scholarly indexes and bibliographic resources, followed by citation tracking, metadata verification and thematic appraisal. Evidence is strongest for CRISPR-mediated improvement of discrete, biologically well-characterised traits, including disease resistance, quality attributes, plant architecture and selected stress responses. AI has also achieved useful performance in phenotyping, genomic prediction and CRISPR design, but superiority over conventional statistical or rule-based approaches is not consistent across datasets, species or prediction tasks. Direct evidence for fully integrated, AI-guided CRISPR breeding programmes that deliver stable field performance remains limited. Major constraints include uncertain causal target identification, small and non-representative training datasets, poor transferability across genetic backgrounds, polyploidy, genotype-by-environment interaction, transformation and regeneration bottlenecks, incomplete detection of unintended outcomes, and heterogeneous regulation. The most defensible interpretation is therefore that AI and CRISPR are complementary components of an emerging design-build-test-learn framework rather than a mature autonomous breeding platform. Progress will depend on plant-specific benchmark datasets, prospective validation, multi-environment field trials, interoperable data standards, equitable access to transformation and computational infrastructure, and governance focused on the properties and evidence of resulting products. Their integration can accelerate precision breeding, but biological causality, experimental validation and breeding judgement remain indispensable.
Anilkumar Lalasing Chavan, Pavan Rathod G. P., Chandana Suresh K. S. et al.· Plant cell biotechnology and...· 0 citations
Soybean is a strategic crop for global protein and vegetable oil supply chains; however, genetic improvement remains constrained by genotype-dependent regeneration, variable transformation efficiency, and regulatory concerns regarding stable transgene integration. This review synthesizes emerging DNA-free and genotype-independent genome-editing frameworks for soybean, where genotype independence is defined as the ability to recover fertile, non-chimeric edited plants across elite germplasm. We critically examine the soybean genome-editing toolbox, including CRISPR-Cas9, Cas12a, multiplex editing systems, base editing, and prime editing, and discuss persistent bottlenecks associated with target selection, off-target assessment, editability, and plant recovery. Particular emphasis is placed on artificial intelligence (AI)-assisted approaches that integrate genomic, epigenomic, chromatin-accessibility, and multi-omics datasets to improve target prioritization, guide RNA design, off-target prediction, and locus- and genotype-specific editability assessment. We further evaluate DNA-free genome-editing technologies, including CRISPR-Cas ribonucleoproteins, transient RNA-based systems, and nanocarrier-mediated delivery platforms, highlighting their potential to generate non-integrative edits while reducing prolonged nuclease exposure. In addition, we discuss regeneration reprogramming strategies based on developmental regulators and morphogenic modules, including BBM-WUS, GRF-GIF, de novo meristem induction, and somatic embryogenesis, as enabling technologies for overcoming cultivar-dependent regeneration barriers. Importantly, this review proposes an integrated AI-to-field framework that connects target discovery, editability prediction, DNA-free editing, regeneration reprogramming, phenotypic validation, and breeding deployment into a unified soybean improvement pipeline. We further highlight emerging opportunities in multi-omics-guided target discovery, genotype-aware prediction models, regeneration-aware editing strategies, and closed-loop machine-learning systems that continuously improve editing decisions through experimental feedback. Collectively, these convergent innovations provide a practical foundation for accelerating the development of climate-resilient, nutritionally enhanced, and industry-ready soybean cultivars.
H. Kim, Jia Chae, S. Han et al.· Plants· 0 citations
The application of genome editing, CRISPR/Cas9 has revolutionized plant breeding by enabling precise, efficient, and targeted modification of native genes, significantly accelerating the development of improved agronomic traits of crops. Therefore, CRISPR/Cas9 technology currently the most extensively used genome editing technique worldwide because of its simple design, cost-effectiveness, high efficiency, good reproducibility, high engineering feasibility, ability to create gene knockout, RNA editing, and quick cycle. It is used to knock in or knock out genes of interest and for generating models for genetic studies. The main components of the CRISPR/Cas9 system are an RNA-guided Cas9 endonuclease and a single-guide RNA (sgRNA). The workflow of CRISPR/Cas9 gene editing comprises selecting target sites, designing and synthesizing sgRNA, introducing transformation constructs or ribonucleoprotein (RNP) in plant cells, followed by transformation and identification of edited lines. This approach bypasses the formal regulations on GMOs, thus encouraging the widespread adoption RNA-guided gene editing in agricultural sciences and biotechnology. The system is now being utilized in the biofortification of cereal crops such as rice, wheat, barley, and maize, including vegetable crops such as potato and tomato. The world's first genome-edited rice varieties are DRR Dhan 100 (Kamala) and Pusa DST Rice 1 developed by the Indian Council of Agricultural Research (ICAR), New Delhi, India in 2025 with the objective of bringing about revolutionary changes in terms of higher production, climate adaptability, and water conservation. The CRISPR/Cas9-based crop genome editing has been utilized in imparting/producing qualitative enhancement in aroma, shelf life, sweetness, and quantitative improvement in starch, protein, gamma-aminobutyric acid (GABA), oleic acid, anthocyanin, phytic acid, gluten, and steroidal glycoalkaloid contents. Some varieties have even been modified to become disease and stress-resistant. Therefore, CRISPR/Cas9 is aiding in developing climate-ready crops and improving crop quality parameters such as appearance, palatability, nutritional components, and other preferred traits. Gene editing tools are used to generate changes to the native genetic material. Unlike GMOs, which introduce novel configurations of genetic materials typically derived from other organisms, gene editing methods modify existing genetic material in ways that can yield beneficial outcomes.
Ravindra B. Malabadi, Raju K. Chalannavar· World Journal of Advanced Re...· 0 citations
The convergence of CRISPR-Cas9 genome editing and nanozyme engineering is revolutionizing synthetic biology, biotechnology, and medical science. CRISPR-Cas9, a precise and programmable tool, enables targeted genetic modifications that enhance nanozyme functionality, stability, and catalytic efficiency. Through the site-specific mutagenesis, metabolic pathway regulation, and synthetic biology strategies, researchers have significantly improved nanozyme performance for diverse applications, including environmental remediation, biomedical diagnostics, and industrial catalysis. This article explores the fundamental mechanisms of CRISPR-based genome editing, its role in nanozyme optimization, and the latest breakthroughs in enzyme engineering. It also critically examines challenges such as off-target effects, biosafety concerns, and ethical implications associated with gene-edited nanozymes. As advancements in AI-driven predictive modeling and next-generation gene-editing tools emerge, CRISPR-Cas9 is poised to unlock unprecedented possibilities in bioengineering. The integration of genetic precision with catalytic innovation marks a transformative era, redefining the frontiers of molecular biotechnology and paving the way for groundbreaking applications in medicine, industry, and sustainable technology.
Modamori I.O, Okanlawon T.S, Ebhomienlen J.O et al.· Biological and Environmental...· 0 citations