论生物体的信息代谢

生物体的信息代谢是指依托生物体物质和能量代谢、建立在生物体各组织结构层次上的由单源信息的出现到互信息的构建到组合信息的形成到信息网络的整合以及由信息网络到组合信息到互信息到单源信息的逐渐崩解的同期性动态链接过程。信息代谢是生命信息进化的高级表现,也是生命的本质;它蕴育着各种生命活动。     

植物分类学实验教学模式改革

以使学生通过实验学习顺利掌握“鉴别植物种类所需的基本技能”的训练为出发点,来对植物分类学实验教学体系进行重新构建,并根据新体系对实验教学方法和考核方法进行改革和调整,从而提高了教学效果,促进了学生综合素质的提高。     

A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

As one large class of non-coding RNAs(nc RNAs), long nc RNAs(lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lnc RNAs have been implicated in human disorders. Many lnc RNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here,we introduce a computational method named lnc RNA–protein bipartite network inference(LPBNI). LPBNI aims to identify potential lnc RNA–interacting proteins, by making full use of the known lnc RNA–protein interactions. Leave-one-out cross validation(LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk(RWR)and protein-based collaborative filtering(Pro CF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lnc RNA–interacting proteins.     

基于BP神经网络模型的全球森林土壤异养硝化过程N2O排放通量估算

N₂O作为重要的温室气体之一,对地球和人类都有很大的影响。为了深入探究对有机氮异养硝化作用及其产生N₂O过程的影响机制,完善全球N₂O通量估算模型,本研究采用Pearson相关性分析与广义可加模型(GAM)对全球135个样点有机氮异养硝化速率及其产生N₂O速率的影响因子进行分析,然后将主要影响因子作为BP神经网络的输入层来模拟全球森林土壤有机氮异养硝化速率及其产生N₂O速率的空间分布。结果显示,土壤pH和土壤C/N是影响有机氮异养硝化速率的主要因素,土壤C/N、土壤孔隙含水量(WFPS)以及土壤温度是影响有机氮异养硝化产生N₂O速率的主要因素。全球森林土壤异养硝化速率平均为0.4241(0.0014～0.689)μg N·g^-1·d^-1,异养硝化产生N₂O速率平均为0.2936(0.21～1.103)μg N₂O·kg^-1·d^-1     

生物网络研究进展述评

生物网络是一类典型的复杂适应性系统,包含了许多个体的多层次的各种相互作用和关系,在过去的十年里,利用复杂网络理论对生物网络进行研究引起了人们的注意并获得了快速发展.本文首先从从度分布、聚类系数及鲁棒性等角度对现阶段生物网络性质的研究进行了简要介绍,后进一步对生物网络的聚类算法及主要建模理论做出了概括.今后的研究趋势在于如何建立合理的生物网络模型,以深入研究生物网络的各种性质.     

复杂疾病靶基因识别与网络重建的计算系统生物研究

复杂疾病相关靶基因的识别、构建疾病驱使相关基因网络及进行疾病机制研究,是功能基因组学研究中非常重要的科学问题。文章以计算系统生物学的观点和三维的角度,综述了基于生物谱（SNP遗传谱、芯片表达谱和2D-PAGE蛋白质谱等）的复杂疾病靶基因识别、多水平（SNPs虚拟网络、基因调控网络、蛋白质互作网络等）遗传网络逆向重构方法,及不同水平的网络之间在生物学和拓扑学上的纵向映射关系,并给出复杂疾病靶基因识别与网络关系的计算系统生物方法研究的未来展望。     

土壤理化性质驱动烤烟根际细菌群落的组配及其共现性网络互作

【目的】解析土壤微生物在植物根际的组配机制对于认识和维护农田生态系统的稳定性至关重要。【方法】通过Illumina高通量测序和生物信息学分析方法明确了我国主要种植烟草生态区烤烟根际土壤细菌群落与土壤理化性质的互作关系。【结果】烤烟根际细菌类群主要为放线菌纲(Actinobacteria)、α-变形菌纲(Alphaproteobacteria)、γ-变形菌纲(Gammaproteobacteria)和嗜热油菌纲(Thermoleophilia)。细菌群落组成按生态区聚类,且样本空间距离和细菌群落相似度显著负相关。共现性网络分析表明,烤烟根际细菌群落间协同作用大于拮抗作用,武陵秦巴生态区、黄淮平原生态区、南岭丘陵生态区和沂蒙丘陵生态区细菌群落高度模块化,小单胞菌属(Micromonospora)为南岭丘陵生态区和黄淮生态区细菌共现性网络的网络中心,Bryobacter和气单胞菌属(Arenimonas)为南岭丘陵生态区细菌网络的模块核心,其菌群特性而非相对丰度决定了其在稳定细菌网络中的重要作用。冗余分析结果证实pH、有效铁、交换性镁和有效锰能显著影响烤烟根际细菌群落结构。【结论】烤烟根际细...     

What is a healthy ecosystem?

Rapid deterioration of the world's major ecosystems has intensified the need for effective environmental monitoring and the development of operational indicators of ecosystem health. Ecosystem health represents a desired endpoint of environmental management, but it requires adaptive, ongoing definition and assessment. We propose that a healthy ecosystem is one that is sustainable – that is, it has the ability to maintain its structure (organization) and function (vigor) over time in the face of external stress (resilience). Various methods to quantify these three ecosystem attributes (vigor, organization, and resilience) are discussed. These attributes are then folded into a comprehensive assessment of ecosystem health. A network analysis based ecosystem health assessment is developed and tested using trophic exchange networks representing several different aquatic ecosystems. Results indicate the potential of such an ecosystem health assessment for evaluating the relative health of similar ecosystems, and quantifying the effects of natural or anthropogenic stress on the health of a particular ecosystem over time.     

A review of EEG and MEG for brainnetome research

The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain’s multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new –ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG’s applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.     

LncRNA and mRNA integration network reconstruction reveals novel key regulators in esophageal squamous-cell carcinoma

Many experimental and computational studies have identified key protein coding genes in initiation and progression of esophageal squamous cell carcinoma (ESCC). However, the number of researches that tried to reveal the role of long non-coding RNAs (lncRNAs) in ESCC has been limited. LncRNAs are one of the important regulators of cancers which are transcribed dominantly in the genome and in various conditions. The main goal of this study was to use a systems biology approach to predict novel lncRNAs as well as protein coding genes associated with ESCC and assess their prognostic values. By using microarray expression data for mRNAs and lncRNAs from a large number of ESCC patients, we utilized “Weighted Gene Co-expression Network Analysis” (WGCNA) method to make a big coding-non-coding gene co-expression network, and discovered important functional modules. Gene set enrichment and pathway analysis revealed major biological processes and pathways involved in these modules. After selecting some protein coding genes involved in biological processes and pathways related to cancer, we used “LncTar”, a computational tool to predict potential interactions between these genes and lncRNAs. By combining interaction results with Pearson correlations, we introduced some novel lncRNAs with putative key regulatory roles in the network. Survival analysis with Kaplan-Meier estimator and Log-rank test statistic confirmed that most of the introduced genes are associated with poor prognosis in ESCC. Overall, our study reveals novel protein coding genes and lncRNAs associated with ESCC, along with their predicted interactions. Based on the promising results of survival analysis, these genes can be used as good estimators of patients' survival, or even can be analyzed further as new potential signatures or targets for the therapy of ESCC disease.