Dorier Julien, Crespo Isaac, Niknejad Anne, Liechti Robin, Ebeling Martin, Xenarios Ioannis
Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
Pharmaceutical Sciences/Translational Technologies and Bioinformatics, Roche Innovation Center Basel, 124 Grenzacherstrasse, 4070, Basel, Switzerland.
BMC Bioinformatics. 2016 Oct 6;17(1):410. doi: 10.1186/s12859-016-1287-z.
Prior knowledge networks (PKNs) provide a framework for the development of computational biological models, including Boolean models of regulatory networks which are the focus of this work. PKNs are created by a painstaking process of literature curation, and generally describe all relevant regulatory interactions identified using a variety of experimental conditions and systems, such as specific cell types or tissues. Certain of these regulatory interactions may not occur in all biological contexts of interest, and their presence may dramatically change the dynamical behaviour of the resulting computational model, hindering the elucidation of the underlying mechanisms and reducing the usefulness of model predictions. Methods are therefore required to generate optimized contextual network models from generic PKNs.
We developed a new approach to generate and optimize Boolean networks, based on a given PKN. Using a genetic algorithm, a model network is built as a sub-network of the PKN and trained against experimental data to reproduce the experimentally observed behaviour in terms of attractors and the transitions that occur between them under specific perturbations. The resulting model network is therefore contextualized to the experimental conditions and constitutes a dynamical Boolean model closer to the observed biological process used to train the model than the original PKN. Such a model can then be interrogated to simulate response under perturbation, to detect stable states and their properties, to get insights into the underlying mechanisms and to generate new testable hypotheses.
Generic PKNs attempt to synthesize knowledge of all interactions occurring in a biological process of interest, irrespective of the specific biological context. This limits their usefulness as a basis for the development of context-specific, predictive dynamical Boolean models. The optimization method presented in this article produces specific, contextualized models from generic PKNs. These contextualized models have improved utility for hypothesis generation and experimental design. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research. Our method was implemented in the software optimusqual, available online at http://www.vital-it.ch/software/optimusqual/ .
先验知识网络（PKNs）为计算生物学模型的开发提供了一个框架，包括调控网络的布尔模型，而这正是本研究的重点。PKNs是通过精心的文献编目过程创建的，通常描述了使用各种实验条件和系统（如特定细胞类型或组织）确定的所有相关调控相互作用。其中某些调控相互作用可能并非在所有感兴趣的生物学背景中都会出现，它们的存在可能会显著改变所得计算模型的动态行为，从而阻碍对潜在机制的阐明，并降低模型预测的实用性。因此，需要一些方法来从通用PKNs生成优化的上下文网络模型。
我们基于给定的PKN开发了一种生成和优化布尔网络的新方法。使用遗传算法，将模型网络构建为PKN的子网，并根据实验数据进行训练，以根据吸引子以及在特定扰动下它们之间发生的转变来重现实验观察到的行为。因此，所得的模型网络针对实验条件进行了上下文设定，并且与原始PKN相比，构成了一个更接近用于训练模型的观察到的生物学过程的动态布尔模型。然后可以对这样的模型进行询问，以模拟扰动下的响应，检测稳定状态及其性质，深入了解潜在机制并生成新的可测试假设。
通用PKNs试图综合在感兴趣的生物学过程中发生的所有相互作用的知识，而不考虑特定的生物学背景。这限制了它们作为开发特定上下文、预测性动态布尔模型基础的实用性。本文提出的优化方法从通用PKNs生成特定的、上下文设定的模型。这些上下文设定的模型在假设生成和实验设计方面具有更高的实用性。这种方法学方法的普遍适用性使其适用于各种生物系统，并且对生物学和医学研究具有普遍意义。我们的方法已在软件optimusqual中实现，可在http://www.vital-it.ch/software/optimusqual/在线获取。
