Systems biology of the structural proteome

代谢（GEM）的基因组规模模型代表可用于多种理论和实践计算研究中的生化，遗传和基因组（BIGG）知识库。这些范围可能从发现身份不明的代谢反应到宿主/病原体相互作用的探索。最近，这些模型已通过纳入其他生物学信息（例如蛋白质结构信息）来扩展，从而孕育了具有蛋白质结构（GEM-PRO）的基因组规模模型。

This article generates and applies the GEM-PRO methodology on two distinct organisms:Escherichia coliandThermotoga Maritima。这是一次多尺度尝试，它在基因（及其产物），生化反应和表型函数之间建立了多个联系，通过有关单个蛋白质的分子级信息进一步增强了生化反应和表型功能。本质上，Gem-Pro在于系统生物学和结构生物学的交集，并深入了解生物体基因型的物理体现。

To aid in the understanding and further application of this methodology specific tutorials showing how protein-related information can be linked to genome-scale models and can be accessed in a public GitHub repository (https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/）。

Planarian干细胞系统中的反馈控制

Planarian flatworms have astonishing regenerative mechanisms in response to their environmental conditions and for that property they have been systematically studied for the past 100+ years. However, currently it is possible to trace their stem cell activity from the gene-level all the way to the organism-level, providing a unique opportunity for a ‘systems biology’ approach.

本文提出了一种非线性动力学模型of the flatworm’s stem cell system incorporating feedback control. It draws conclusions on the dynamics of the size, signaling systems and rates of mortality for the aforementioned animal and builds the foundations for a full conceptual framework for planarian cellular dynamics. Based on this model, the scientific community may begin to understand the mechanisms of cell migration during injury, the characterization of homeostatic levels of differentiated cells, as well as the associated stochastic effects.

Comparing Alzheimer’s and Parkinson’s diseases networks using graph communities structure

The rapid advancement of high throughput technologies, along with the vast amounts of available ‘omics’ data, cultivate the ideal environment for a system-level investigation of pathologies. In this article, Calderone et al. showcase a state-of-the-art algorithm, termed ‘InfoMap’, which is able to exploit network community structures and is built upon network theory fundamentals.

It is applied to two age-related neurodegenerative diseases, Alzheimer’s Disease and Parkinson’s Disease, in order to identify similarities and differences in their respective network proteins and pathways. Significant insights are gained for both known and unknown processes, but particularly with regards to information about mitochondrial dysfunction and metabolism. The remarkable fact about this methodological approach is that it can be applied to the comparison of any pair of biological networks.

计算生物学的可重复性议程的下一步？

The term ‘reproducibility’ is a gray aspect for the computational biology community. Unfortunately, it is often (wrongly) open to interpretation and can have a spectrum of definitions. The authors of this study claim that the most basic form of reproducibility is可复制性; the notion that “do other people get exactly the same results when doing exactly the same thing?” (i.e. method replicability). However, true可重复性不仅需要该方法，而且需要现象本身才能重现。最后，当复制软件会导致其构建的最终目标，因此研究人员还需要考虑可扩展性他们的计算方法。

作者介绍了案例研究，以洞悉这三个方面。他们认为，精心设计的软件需要促进这些方面，以确保提出的计算工作的价值，质量和保证。最后，为指导研究人员，开发人员甚至社区提供了具体的清单和建议清单，以改善和确保计算生物学的可重复性。

整合突变和基因表达横截面数据以推断癌症进展

在癌症研究方面，可重复性无疑是一个问题，这是由于疾病的异质性。在生物标志物发现中，一项研究中某些“ OMIC”数据的某些模式可能无法在另一个研究中得到验证。从临床上讲，这可以归因于许多因素，包括所采用的分子方法和现有的癌症数据集，一直到模块化的患者诊断不良以及癌症进展的复杂动态。

Fleck等人提出了一种新型的系统方法，以通过将基因突变与基因表达数据整合来推断时间序列。他们能够识别一组突变事件，最终可能导致基因表达变化。此外，该模型在模拟和实际乳腺癌数据上进行了专门测试The Cancer Genome Atlas。总体而言，通过鉴定癌症进展过程中改变的基因组，该模型可以为上述癌症的异质性提供更好的见解，进一步解决临床问题，甚至改善当前的治疗策略。