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一个可微分的吉尔斯皮算法,用于模拟化学动力学,参数估计和设计合成生物电路

Krishna Rijal1, Pankaj Mehta1

  • 1Department of Physics, Boston University, Boston, United States.

eLife
|March 17, 2025
PubMed
概括

我们开发了一个可微分的吉尔斯皮算法 (DGA),使用深度学习来分析复杂的化学反应. 这种方法准确地学习动力学参数,设计生化网络,推进系统和合成生物学.

关键词:
细胞生物学 细胞生物学微分可能的吉尔斯皮算法.基因监管网络 基因监管网络没有,没有,没有.参数估计的参数估计.生物系统的物理生活系统的物理.随机模拟 随机模拟 随机模拟合成生物学 合成生物学

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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