巴达丹:时间序列基因模块表达机制建模
Ben Noordijk1,2, Marcel Reinders2,3, Aalt D J van Dijk2,4
1Bioinformatics Group, Wageningen University & Research, Wageningen, The Netherlands.
Quantitative plant biology
|September 24, 2025
概括
了解植物应激反应需要模拟复杂的基因调节网络 (GRNs). 新的机器学习工具BADDADAN模拟大规模的GRN,以预测干旱和热压下的植物弹性.
科学领域:
- 植物生物学 植物生物学
- 系统生物学 系统生物学
- 计算生物学是一种计算生物学.
背景情况:
- 植物利用复杂的基因调节网络 (GRNs) 来应对环境压力,如热量和干旱.
- 由于传统的普通微分方程 (ODE) 模型中的参数估计负担很高,对大规模GRNs (>100基因) 的准确建模具有挑战性.
研究的目的:
- 开发一种新的计算方法,BADDADAN,用于在压力条件下建模大型植物GRNs.
- 通过预测基因模块动态来提高对植物弹性机制的理解.
主要方法:
- 巴达丹集成机器学习与机械模型.
- 它使用时间序列基因表达和先前的共同表达数据来识别基因模块.
- 构建一个ODE模型来预测基因模块动态.
主要成果:
- 在高温和干旱压力下,BADDADAN成功模拟了A. thaliana*的1000多个基因.
- 该方法确定了连贯和可解释的基因模块.
- 已知的机械见解得到恢复,并产生了新的假设.
结论:
- 巴达丹为分析植物中复杂的GRN提供了一种强大的方法,增强了我们对压力反应的理解.
- 这种结合机器学习和机械模型的方法可以深入了解植物和潜在的其他生物体中的GRNs.
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