Genome-scale models of metabolism (GEM) represent biochemical, genetic and genomic (BiGG) knowledge bases that can be utilised in a wide variety of theoretical and practical computational studies. These may range from the discovery of unidentified metabolic reactions, to the exploration of host/pathogen interactions. Recently, these models have been extended via the incorporation of additional biological information, such as protein structural information, thus giving birth to genome-scale models with protein structures (GEM-PRO).
本文在两个不同的生物上生成并应用了Gem-Pro方法论：大肠杆菌和Thermotoga maritima. It is a multi-scale attempt that creates multiple links between genes (and their products), biochemical reactions and phenotypic functions, that are further enhanced by molecular-level information about individual proteins. Essentially, GEM-PRO lies at the intersection of systems biology and structural biology and offers insight into the physical embodiment of an organism’s genotype.
为了帮助理解和进一步应用该方法论，具体的教程，显示如何将与蛋白质相关的信息链接到基因组规模模型，并可以在公共GitHub存储库中访问（https://github.com/sbrg/gempro/tree/master/gempro_recon/).
Feedback control in planarian stem cell systems
Planarian Flatems对环境条件的响应具有惊人的再生机制，并且在过去的100多年中，它们已经进行了系统的研究。但是，目前可以从基因水平一直到生物级的干细胞活性追踪其干细胞活性，从而为“系统生物学”方法提供了独特的机会。
本文介绍了结合反馈控制的扁虫干细胞系统的非线性动力学模型。它得出了上述动物的大小，信号系统和死亡率的动力学的结论，并为planary蜂窝动力学的完整概念框架建立了基础。基于该模型，科学界可以开始了解损伤期间细胞迁移的机制，分化细胞的体内平衡水平的表征以及相关的随机作用。
高通量技术的快速发展以及大量可用的“ OMICS”数据培养了系统级病理研究的理想环境。在本文中，Calderone等人。展示了一种最先进的算法，称为“ Infomap”，该算法能够利用网络社区结构并建立在网络理论基础上。
它适用于两种与年龄相关的神经退行性疾病，阿尔茨海默氏病和帕金森氏病，以确定其各自的网络蛋白质和途径的相似性和差异。在已知和未知过程中获得了重要的见解，但尤其是关于线粒体功能障碍和代谢的信息。关于这种方法论方法的非凡事实是，它可以应用于任何一对生物网络的比较。
Where next for the reproducibility agenda in computational biology?
“可重复性”一词是计算生物学社区的灰色方面。不幸的是，它通常（错误地）对解释开放，并且可以具有一系列定义。这项研究的作者声称，最基本的可重复性形式是replicability;“其他人在做完全相同的事情时会得到完全相同的结果吗？”的观念。”（即方法可复制性）。但是，是的reproducibilityrequires not only the method but also the phenomenon itself to be reproducible. Finally, when reproducing a software result the ultimate aim it to build on it, so researchers need to also consider theextensibilityof their computational method.
The authors present case studies that provide insight on these three aspects. They argue that well-designed software needs to facilitate these aspects in order to ensure the value, quality and assurance of the presented computational work. Finally, specific checklists and recommendation lists are provided to guide researchers, developers and even the community towards improving and ensuring reproducibility in computational biology.
Integrating mutation and gene expression cross-sectional data to infer cancer progression
Reproducibility is certainly an issue when it comes to cancer research and that’s due to the heterogeneity of the disease. In biomarker discovery certain patterns of ‘omics’ data in one study may fail to be validated in another. In clinical terms, that can be attributed to a number of factors, ranging from the employed molecular methodology and the existing cancer data sets, all the way to the poorly modular patient diagnosis and the complex dynamics of cancer progression.
Fleck et al propose a novel systematic methodology to infer temporal sequence by integrating gene mutation with gene expression data. They are able to identify a set of mutation events that may eventually lead to changes in the gene expression. Furthermore, this model is specifically tested on both simulated and real breast cancer data from癌症基因组图集. Overall, by identifying the groups of genes that change during cancer progression this model may provide superior insight to the aforementioned heterogeneity of cancer, further address clinical questions or even improve the current therapeutic strategies.
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