Differentiable breeding: Automatic differentiation enables efficient gradient-based optimization of breeding strategies DOI Creative Commons
Kosuke Hamazaki, Hiroyoshi Iwata, Koji Tsuda

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

Abstract Conventional breeding methods often require extensive time to develop new cultivars, hindering rapid adaptation global challenges. While genomic selection has accelerated breeding, there remains substantial room for improvement. Recent studies have explored complex decision-making in schemes using various optimization techniques such as black-box optimization. However, these are challenged by constraints simultaneously optimizing multiple parameters necessary achieving more efficient and flexible To address limitations, this study implemented automatic differentiation of PyTorch. By treating the entire scheme a differentiable computational graph, we enabled gradient calculations final genetic gains relative progeny allocation each mating pair. We first validated our approaches comparing with analytical results simple gamete generation test case. Next, used perform gradient-based strategies, aiming maximize schemes. The strategy was then compared black-box-based optimized non-optimized strategies. Our framework successfully reduced number function evaluations needed approach outperformed terms gains. This demonstrates that effectively harnessed information via differentiation. Integrating into is expected enhance flexibility lay groundwork future methods. Author summary Plant plays crucial role addressing challenges like population growth climate change developing adaptable crop varieties. conventional several years produce making it difficult keep pace advancements techniques, selection, significantly enhanced accuracy speed. Despite improvements, real programs. explores application optimize specifically focusing on progenies allocated Automatic enables calculation derivatives functions, potentially accelerating process based PyTorch, graph. integrating optimization, enable exploration optimal solutions while greater parameters. novel method potential efficiency ultimately contributing development productive varieties food

Language: Английский

Elucidating the Interaction of Indole-3-Propionic Acid and Calf Thymus DNA: Multispectroscopic and Computational Modeling Approaches DOI Creative Commons

Yushi Wei,

Dan Zhang,

Junhui Pan

et al.

Foods, Journal Year: 2024, Volume and Issue: 13(12), P. 1878 - 1878

Published: June 14, 2024

Indole-3-propionic acid (IPA) is a plant growth regulator with good specificity and long action. IPA may be harmful to human health because of its accumulation in vegetables fruits. Therefore, this study, the properties interaction between calf thymus DNA (ctDNA) were systematically explored using multispectroscopic computational modeling approaches. Analysis fluorescence spectra showed that binding ctDNA spontaneously form complex was mainly driven by hydrogen bonds hydrophobic interaction. melting analysis, viscosity cleavage circular dichroism measurement revealed groove did not significantly change confirmation. Furthermore, molecular docking found attached A-T rich minor region DNA. Molecular dynamics simulation formed stable caused slight fluctuations for residues at site. Gel electrophoresis experiments disrupt structure. These findings provide useful information on potential toxicological effects environmental risk assessments residue food level.

Language: Английский

Citations

1
Tuning Colloidal Reactions DOI
Ryan Krueger, Ella M. King, Michael P. Brenner

et al.

Physical Review Letters, Journal Year: 2024, Volume and Issue: 133(22)

Published: Nov. 27, 2024

The precise control of complex reactions is critical for biological processes, yet our inability to design specific outcomes limits the development synthetic analogs. Here, we leverage differentiable simulators nontrivial reaction pathways in colloidal assemblies. By optimizing over external structures, achieve controlled disassembly and particle release from shells. Lastly, characterize role configurational entropy structure via both forward calculations optimization, inspiring new parameterizations designed reactions. locked icon Physics Subject Headings (PhySH)Applications soft matterOptimization problemsColloidsMolecular dynamics

Language: Английский

Citations

1
Differentiable breeding: Automatic differentiation enables efficient gradient-based optimization of breeding strategies DOI Creative Commons
Kosuke Hamazaki, Hiroyoshi Iwata, Koji Tsuda

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

Abstract Conventional breeding methods often require extensive time to develop new cultivars, hindering rapid adaptation global challenges. While genomic selection has accelerated breeding, there remains substantial room for improvement. Recent studies have explored complex decision-making in schemes using various optimization techniques such as black-box optimization. However, these are challenged by constraints simultaneously optimizing multiple parameters necessary achieving more efficient and flexible To address limitations, this study implemented automatic differentiation of PyTorch. By treating the entire scheme a differentiable computational graph, we enabled gradient calculations final genetic gains relative progeny allocation each mating pair. We first validated our approaches comparing with analytical results simple gamete generation test case. Next, used perform gradient-based strategies, aiming maximize schemes. The strategy was then compared black-box-based optimized non-optimized strategies. Our framework successfully reduced number function evaluations needed approach outperformed terms gains. This demonstrates that effectively harnessed information via differentiation. Integrating into is expected enhance flexibility lay groundwork future methods. Author summary Plant plays crucial role addressing challenges like population growth climate change developing adaptable crop varieties. conventional several years produce making it difficult keep pace advancements techniques, selection, significantly enhanced accuracy speed. Despite improvements, real programs. explores application optimize specifically focusing on progenies allocated Automatic enables calculation derivatives functions, potentially accelerating process based PyTorch, graph. integrating optimization, enable exploration optimal solutions while greater parameters. novel method potential efficiency ultimately contributing development productive varieties food

Language: Английский

Citations

0