Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology

2026/01/01 by Bashar Ibrahim · 1 voice
Biochemistry, Genetics and Molecular Biology · #Microtubule and mitosis dynamics #Bioinformatics and Genomic Networks #Nuclear Structure and Function

paper · doi:10.1093/bib/bbaf729

openalex publication_date 2026/01/01 · openalex created_date 2026/01/16 · openalex updated_date 2026/08/01

Abstract

Mitotic checkpoints safeguard genomic integrity by orchestrating the precise segregation of chromosomes during cell division. Yet their complex, nonlinear dynamics have long defied full understanding through traditional experimental and computational approaches. In recent years, artificial intelligence (AI) has begun to transform this landscape. Machine learning and deep learning methods now achieve substantial accuracies in predicting cellular behaviors and uncovering novel regulatory mechanisms within checkpoint networks. Advances include transformer architectures capable of predicting spindle assembly checkpoint engagement with >95% accuracy, graph neural networks that decode kinetochore-microtubule dynamics at subpixel resolution, and hybrid AI-mechanistic models that reveal previously hidden feedback circuits. By integrating multi-omics data and bridging molecular mechanisms with clinical applications, AI-driven approaches are opening significant opportunities for precision medicine in cancer and other proliferative diseases. This review synthesizes emerging computational frameworks, highlights transformative AI-driven discoveries, and proposes a roadmap for developing predictive, personalized models of mitotic checkpoint control-charting a path from computational insight to clinical impact.

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Discussions

• New review highlights how #MachineLearning and #ArtificialIntelligence models are transforming mitotic checkpoint modelling, from predictive biology to clinical precision medicine. 🤖🧬⚕️ #CellBiology [bsky, 0 points, 0 comments]