Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation.
Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation.
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DOI：
10.1186/s12918-016-0324-x
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发表时间：
2016-08-22
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通讯作者：
H Stumpf MP
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文献类型：
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作者：
Lenive O;W Kirk PD;H Stumpf MP
众所周知，基因表达是一个内在的随机过程，在任何给定的时间，细胞中都可能涉及个位数的mRNA分子。这种过程的建模需要使用精确的随机模拟方法，最著名的是吉莱斯皮算法。然而，这种随机性，也被称为“固有噪声”，并不能解释在同质环境中生长的遗传相同的细胞之间的所有变异性。尽管进行了大量的实验工作，但确定合适的模型参数仍然是一个挑战。基于近似贝叶斯计算的方法可以用来获得给定观测数据的后验参数分布。然而，这样的推理过程需要对模型进行大量的模拟，并且精确的随机模拟的计算代价很高。在这项工作中，我们集中在特定情况下，试图根据给定时间点的单个细胞中的分子数量的测量来推断描述反应速率和外部噪声的模型参数。为了使问题在计算上变得容易处理，我们为常用的基因表达的两状态模型开发了一种精确的、特定于模型的随机模拟算法。该算法依赖于模型的某些假设和有利性质，放弃了对系统中蛋白质数量的整个时间轨迹的模拟，而是只返回特定时间点存在于系统中的蛋白质和mRNA分子的数量。计算收益与系统中产生的蛋白质分子的数量成正比，对于涉及数百或数千个蛋白质分子的系统来说，计算收益变得非常重要。我们使用这种模拟算法和近似贝叶斯计算，从已公布的基因表达数据中联合推断模型的速率和噪声参数。我们的分析表明，对于大多数基因来说，外部因素对噪音的贡献将是小到中等的，但肯定是不可忽视的。本文的在线版本(doi：10.1186/s12918-0160324-x)包含补充材料，授权用户可以使用。
Gene expression is known to be an intrinsically stochastic process which can involve single-digit numbers of mRNA molecules in a cell at any given time. The modelling of such processes calls for the use of exact stochastic simulation methods, most notably the Gillespie algorithm. However, this stochasticity, also termed “intrinsic noise”, does not account for all the variability between genetically identical cells growing in a homogeneous environment. Despite substantial experimental efforts, determining appropriate model parameters continues to be a challenge. Methods based on approximate Bayesian computation can be used to obtain posterior parameter distributions given the observed data. However, such inference procedures require large numbers of simulations of the model and exact stochastic simulation is computationally costly. In this work we focus on the specific case of trying to infer model parameters describing reaction rates and extrinsic noise on the basis of measurements of molecule numbers in individual cells at a given time point. To make the problem computationally tractable we develop an exact, model-specific, stochastic simulation algorithm for the commonly used two-state model of gene expression. This algorithm relies on certain assumptions and favourable properties of the model to forgo the simulation of the whole temporal trajectory of protein numbers in the system, instead returning only the number of protein and mRNA molecules present in the system at a specified time point. The computational gain is proportional to the number of protein molecules created in the system and becomes significant for systems involving hundreds or thousands of protein molecules. We employ this simulation algorithm with approximate Bayesian computation to jointly infer the model’s rate and noise parameters from published gene expression data. Our analysis indicates that for most genes the extrinsic contributions to noise will be small to moderate but certainly are non-negligible. The online version of this article (doi:10.1186/s12918-016-0324-x) contains supplementary material, which is available to authorized users.