Stochastic sensitivity analysis and kernel inference via distributional data.
Stochastic sensitivity analysis and kernel inference via distributional data.
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DOI：
10.1016/j.bpj.2014.07.025
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发表时间：
2014-09
影响因子：
3.4
通讯作者：
Bochong Li;L. You
中科院分区：
文献类型：
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作者：
Bochong Li;L. You
由于生物化学反应的随机性，细胞过程是嘈杂的。因此，不可能在单细胞水平上预测分子的确切数量或其他属性。然而，分子在群体中的分布通常是确定性的，并且由与感兴趣的细胞功能相关的潜在调控网络控制。最近的研究已经开始利用这个属性来推断网络状态。为了方便在一般的实验设置的分布数据的分析，我们引入了一个计算框架，有效地表征分布输出的敏感性，在外部刺激的变化。此外，我们建立了一个基于概率发散的核回归模型，以准确地推断信号水平的分布测量的基础上。我们的方法是适用于任何生物系统的随机动力学，并可用于阐明如何人口为基础的信息处理可能有助于生物体水平的功能。它还为工程合成生物系统奠定了基础，这些系统利用群体解码来更稳健地执行各种生物计算任务，例如疾病诊断和环境污染物传感。
Cellular processes are noisy due to the stochastic nature of biochemical reactions. As such, it is impossible to predict the exact quantity of a molecule or other attributes at the single-cell level. However, the distribution of a molecule over a population is often deterministic and is governed by the underlying regulatory networks relevant to the cellular functionality of interest. Recent studies have started to exploit this property to infer network states. To facilitate the analysis of distributional data in a general experimental setting, we introduce a computational framework to efficiently characterize the sensitivity of distributional output to changes in external stimuli. Further, we establish a probability-divergence-based kernel regression model to accurately infer signal level based on distribution measurements. Our methodology is applicable to any biological system subject to stochastic dynamics and can be used to elucidate how population-based information processing may contribute to organism-level functionality. It also lays the foundation for engineering synthetic biological systems that exploit population decoding to more robustly perform various biocomputation tasks, such as disease diagnostics and environmental-pollutant sensing.