基因调节网络在随机稳定状态下的贝叶斯推理
bioRxiv : the preprint server for biology
|February 9, 2026
概括
我们开发了一种新的贝叶斯方法,使用化学朗格温方程 (CLE) 来推断基因调节网络 (GRN) 结构. 这种方法可以识别调节相互作用和动力参数,而不需要过渡数据.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 基因调控网络 (GRNs) 对于协调基因表达和生物系统功能至关重要.
- 当前的GRN推断方法往往忽视了推动基因表达动态的基本生化过程.
- 了解GRN架构对于破译生物系统和开发向疗法至关重要.
研究的目的:
- 为GRN结构识别提供一种新的贝叶斯推理方法.
- 使用化学朗格温方程 (CLE) 将生物化学动力学纳入GRN推理中.
- 提高GRN推理方法的生物解释性和结构识别性.
主要方法:
- 开发了一个贝叶斯推理框架,利用化学朗格温方程 (CLE) 来建模在随机平衡下的基因表达动态.
- 在处理GRN相互作用的稀疏性之前采用了规范化的马,从而能够选择性地缩小不受支持的相互作用.
- 使用合成基因表达数据与已知的基因真相进行基准测试,验证了该方法.
主要成果:
- 从基因表达数据中成功推断出运动参数并确定了网络结构.
- 证明了在不需要观察过渡动态的情况下推断调节周期的能力.
- 贝叶斯的方法为GRN推断提供了生物解释性和结构识别性.
结论:
- 基于CLE的贝叶斯新方法在GRN推断中取得了重大进展.
- 这种方法有效地模拟生化过程,提高推断网络结构的准确性和可靠性.
- 该方法为寻求可解释和可识别GRN模型的研究人员提供了有价值的替代方案.
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