Generative AI agents are transforming biology research: high resolution functional genome annotation for multiscale understanding of life

Life is inherently multimodal and governed by interactions between DNA, RNA, proteins and metabolites that vary in time and space, from cell to cell, and genome to biome.This complexity prevents us from understanding many biological systems and has brought heuristic analytical techniques to their limits.Holistic approaches driven by narrow AI have over the last decade shown promise for overcoming those limitations and offered new ways of explaining and designing biological systems.The evolution of Large Language Model (LLM)-based generative AI techniques and the currently unfolding Cambrian Explosion of novel agentic AI tools (Agents) allow biologists to bundle AI, biological data, and advanced life sciences technologies into a suite of scientific and engineering capabilities.Referred to as generative biology, this rapidly expanding field fundamentally redefines our ability to make sense of life from genome to biome.The genome is the blueprint of an organism.To understand its biology, the quantitative functions, characteristics, and interactions of the encoded gene products need to be known and annotated in the genome.Whilst many biological systems have been well characterised through extensive experimentation, we lack the detailed understanding of the function of every gene.With the vast amount of genomic sequence data and the expansion of high-throughput experimental methods (e.g.biofoundries), LLMs are playing a growing role in predicting functions from gene sequences and improving genome annotations.The insights that are unlocked accelerate drug development and other valuable applications, such as the engineering of new biological systems and organisms.This Commentary describes current key applications of generative AI in biology and explains how progress in AI and agent technology will shape the future of biology and the role of biologists.AI and biology are made for each other

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Publication Details

Journal
EBioMedicine
Published
2024-11-01
DOI
https://doi.org/10.1016/j.ebiom.2024.105446
Citations
7
Primary Topic
Genetics, Bioinformatics, and Biomedical Research
Type
article
Field-Weighted Citation Impact
1.64

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article

Generative AI agents are transforming biology research: high resolution functional genome annotation for multiscale understanding of life

Stefan Harrer, Rahul Rane, Robert E. Speight
7 citations
EBioMedicine
Genetics, Bioinformatics, and Biomedical Research
1.64
article

Generative AI agents are transforming biology research: high resolution functional genome annotation for multiscale understanding of life

Stefan Harrer, Rahul Rane, Robert E. Speight
article en
7 citations

Abstract

Life is inherently multimodal and governed by interactions between DNA, RNA, proteins and metabolites that vary in time and space, from cell to cell, and genome to biome.This complexity prevents us from understanding many biological systems and has brought heuristic analytical techniques to their limits.Holistic approaches driven by narrow AI have over the last decade shown promise for overcoming those limitations and offered new ways of explaining and designing biological systems.The evolution of Large Language Model (LLM)-based generative AI techniques and the currently unfolding Cambrian Explosion of novel agentic AI tools (Agents) allow biologists to bundle AI, biological data, and advanced life sciences technologies into a suite of scientific and engineering capabilities.Referred to as generative biology, this rapidly expanding field fundamentally redefines our ability to make sense of life from genome to biome.The genome is the blueprint of an organism.To understand its biology, the quantitative functions, characteristics, and interactions of the encoded gene products need to be known and annotated in the genome.Whilst many biological systems have been well characterised through extensive experimentation, we lack the detailed understanding of the function of every gene.With the vast amount of genomic sequence data and the expansion of high-throughput experimental methods (e.g.biofoundries), LLMs are playing a growing role in predicting functions from gene sequences and improving genome annotations.The insights that are unlocked accelerate drug development and other valuable applications, such as the engineering of new biological systems and organisms.This Commentary describes current key applications of generative AI in biology and explains how progress in AI and agent technology will shape the future of biology and the role of biologists.AI and biology are made for each other

EBioMedicineVol. 109
Commonwealth Scientific and Industrial Research Organisation (AU), CSIRO Health and Biosecurity (AU), Data61 (AU)
Commonwealth Scientific and Industrial Research Organisation
Zero hunger
Openalex Percentile: Top 16%
Genetics, Bioinformatics, and Biomedical Research
1.64
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