Maximizing the information content of experiments in systems biology.
Maximizing the information content of experiments in systems biology.
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DOI：
10.1371/journal.pcbi.1002888
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发表时间：
2013
影响因子：
4.3
通讯作者：
Stumpf MP
中科院分区：
文献类型：
--
作者：
Liepe J;Filippi S;Komorowski M;Stumpf MP
我们对大多数生物系统的理解还处于起步阶段。了解它们的结构和复杂性充满了挑战，并且经常回避研究不同基因产物的功能，而与它们的生理背景隔离。然而，从实验数据构建和推断全局数学模型是系统生物学的核心。不同的实验设置提供了不同的见解，这样的系统。在这里，我们将展示如何将贝叶斯推理和信息论的联合收割机概念相结合，以确定最大化所得数据的信息内容的实验。这种方法使我们能够将初步的信息，它是全球性的，不受约束的参数空间中的一些局部邻域，它很容易产生参数的鲁棒性和信心的信息。在这里，我们开发的理论框架，并将其应用到一系列典型的问题，突出了我们如何可以提高实验研究的结构和动力学的生物系统及其行为。对于大多数生物信号和调节系统，我们仍然缺乏可靠的机制模型。在这种模型存在的地方，例如以微分方程的形式，我们通常只能粗略估计表征生化反应的参数。为了提高我们的知识，这样的系统，我们需要更好地估计这些参数，在这里，我们将展示如何明智的选择实验，结合模拟和信息理论分析的基础上，可以帮助我们。我们的方法建立在现有的，经常是基本的信息，并确定哪些实验设置提供了最多的额外信息的所有参数，或个别参数。我们还将考虑相关的，但微妙的不同的问题，需要进行实验，以减少在改变条件下的系统行为的不确定性。我们开发的理论框架在必要的细节之前，说明其用途，并将其应用到阻遏模型，调节Hes1和Akt通路中的信号转导。
Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior. For most biological signalling and regulatory systems we still lack reliable mechanistic models. And where such models exist, e.g. in the form of differential equations, we typically have only rough estimates for the parameters that characterize the biochemical reactions. In order to improve our knowledge of such systems we require better estimates for these parameters and here we show how judicious choice of experiments, based on a combination of simulations and information theoretical analysis, can help us. Our approach builds on the available, frequently rudimentary information, and identifies which experimental set-up provides most additional information about all the parameters, or individual parameters. We will also consider the related but subtly different problem of which experiments need to be performed in order to decrease the uncertainty about the behaviour of the system under altered conditions. We develop the theoretical framework in the necessary detail before illustrating its use and applying it to the repressilator model, the regulation of Hes1 and signal transduction in the Akt pathway.
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影响因子：
2.3
作者：
Casey, F. P.;Baird, D.;Sethna, J. P.
通讯作者：
Sethna, J. P.
影响因子：
4.1
作者：
Huan, Xun;Marzouk, Youssef M.
通讯作者：
Marzouk, Youssef M.
影响因子：
5.8
作者：
Hengl, S.;Kreutz, C.;Maiwald, T.
通讯作者：
Maiwald, T.
影响因子：
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作者：
Hirata, H;Yoshiura, S;Kageyama, R
通讯作者：
Kageyama, R
影响因子：
7.3
作者：
Fujita, Kazuhiro A.;Toyoshima, Yu;Kuroda, Shinya
通讯作者：
Kuroda, Shinya