A novel approach for assessing factors affecting biodiversity based on networks analysis

We propose in this work a novel approach aiming at assessing cause and effect relationships between variables that can affect target biodiversity issues. These cause–effect relations are used to build a network whose nodes represent variables linked by directed arcs. The arcs have associated a value that represents trends of cause–effect relations. An important novelty of this approach is the use of product and addition operations between trends of cause–effect relations for assessing factors that can affect target variables. For the analysis of the network we use the concept of paths. Paths are defined as sequences of cause–effect relations from source variables to target variables. For example, the path from population increment that causes effects on the increment of transport routes, which in turn causes effects on the loss of vegetation cover. This approach was applied to the assessment of vegetation cover in the Morelos State, México during the period 2000–2010. The results show a promising practical alternative to assess the potential effects on biodiversity issues based on the analysis of the paths represented in the network.     

Community Structure and Role Analysis in Biological Networks

Abstract

Complex network analysis has received increasing interest in recent years, which provides a remarkable tool to describe complex systems of interacting entities, particular for biological systems. In this paper, we propose a methodology for identifying the significant nodes of the networks, including core nodes, bridge nodes and high-influential nodes, based on the idea of community and two new ranking measures, InterRank and IntraRank. The results show the significant nodes form a small number in biological networks, and uncover the relative small number of which has advantage for reducing the dimensions of the network and possibly help to define new biological targets.     

Overrepresentation of interactions between homologous proteins in interactomes

It is well proved that the probability that a protein interacts with itself is higher than that it interacts with another protein. It has been recently shown that the probability of interaction is also higher for proteins with significant sequence similarity. In this paper we show that proteins sharing identical PFAM domains interact more often than expected by chance in Saccharomyces cerevisiae and Escherichia coli. We also analyze the variety of domain interfaces used by homologous proteins to interact and show that the overrepresentation of interactions between homological proteins is not caused by small number of pairs of identical "sticky domains" shared between interacting proteins.     

蛋白质功能预测方法概述

蛋白质是生物体内最必需也是最通用的大分子,对它们功能的认识对于科学领域和农业领域的发展有着至关重要的作用。随着后基因组时代的发展,NCBI数据库中迅速涌现出大量不明结构与功能的蛋白质序列,这些蛋白质序列甚至一跃成了研究的热点。近几十年来蛋白质功能预测的方法不断被完善。由最初的仅基于蛋白质序列或3D结构信息的方法衍生出更多的基于序列相似性、基于结构基序、基于相互作用网络等新方法,这些新型方法采用新的算法、新的研究思路和技术手段,力求得到准确性与普遍性并存,能够被广泛应用的蛋白质功能预测方法。本文综述了近年来蛋白质功能预测的方法,并将这些研究方法分类归纳,各自阐明了每类方法的优缺点。     

Reduction, integration and emergence in biochemical networks

Most studies of molecular cell biology are based upon a process of decomposition of complex biological systems into their components, followed by the study of these components. The aim of the present paper is to discuss, on a physical basis, the internal logic of this process of reduction. The analysis is performed on simple biological systems, namely protein and metabolic networks. A multi-sited protein that binds two ligands x and y can be considered the simplest possible biochemical network. The organization of this network can be described through a comparison of three systems, i.e. XY, X and Y. X and Y are component sub-systems that collect states x(i) and y(j), respectively, i.e. protein states that have bound either i molecules of x (whether or not these states have also bound y), or j molecules of y (whether or not these states have bound x). XY is a system made up of the specific association of X and Y that collects states x(i)y(j). One can define mean self-informations per node of the network, , and . Reduction of the system XY into its components is possible if, and only if, ,is equal to the sum of and . If is smaller than the sum of and , the system is integrated, for it has less self-information than the set of its components X and Y. It can also occur that , be larger than the sum of and . Hence, the system XY displays negative integration and emergence of self-information relative to its components X and Y. Such a system is defined as complex. Positive or negative integration of the system implies it cannot be reduced to its components. The degree of integration can be measured by a function , called mutual information of integration. In the case of enzyme networks, emergence of self-information is associated with emergence of catalytic activity. Moreover, if the enzyme reaction is part of a metabolic sequence, its mutual information of integration can be increased by an effect of context of this sequence.     

Sensitivity analysis of stoichiometric networks: an extension of metabolic control analysis to non-steady state trajectories

A sensitivity analysis of general stoichiometric networks is considered. The results are presented as a generalization of Metabolic Control Analysis, which has been concerned primarily with system sensitivities at steady state. An expression for time-varying sensitivity coefficients is given and the Summation and Connectivity Theorems are generalized. The results are compared to previous treatments. The analysis is accompanied by a discussion of the computation of the sensitivity coefficients and an application to a model of phototransduction.     

Emergent heterogeneity in declining tuberculosis epidemics

Tuberculosis is a disease of global importance: over 2 million deaths are attributed to this infectious disease each year. Even in areas where tuberculosis is in decline, there are sporadic outbreaks which are often attributed either to increased host susceptibility or increased strain transmissibility and virulence. Using two mathematical models, we explore the role of the contact structure of the population, and find that in declining epidemics, localized outbreaks may occur as a result of contact heterogeneity even in the absence of host or strain variability. We discuss the implications of this finding for tuberculosis control in low incidence settings.