Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

/pdf/collective-dynamics-of-small-world-networks-205zhem3gm.pdf

Collective dynamics of small-world networks

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. 
Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. 
The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. 
The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

/pdf/statistical-mechanics-of-complex-networks-1zfjpvxipu.pdf

Statistical mechanics of complex networks

Imagined communities: Reflections on the origin and spread of nationalism

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(12)61766-8.pdf

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010

proteins and are often perturbed in cancer. These networks consist of proteins that modulate the activity of downstream targets through mechanisms such as phosphorylation. Commonly, signaling cascades are conceptualized as linear chains of events. In reality, these pathways are branched with proteins influencing multiple downstream targets and are extremely complex due to a high degree of overlap and crosstalk among pathways. This complexity is critical in defining cancer phenotypes but extremely hard to dissect and analyze. To address this challenge, Chang and colleagues devised a method to dissect signal transduction pathways into individual signaling modules, based on gene expression profiles. The authors first identified a set of genes likely impacted by alterations to a given signaling pathway. These genes were selected using a protein-protein interaction network or gene expression data from experiments in which the pathway of interest was active. Factor analysis was then used in conjunction with a compendium of cancer data (NCI-60) to decompose expression profiles of these genes into a number of underlying signatures, which were taken to represent individual signaling modules in the pathway. Each signature was defined by a set of weighted genes, and the expression levels of these genes were weighted and averaged to create a signature score. These scores were hypothesized to represent the activity of the individual signaling modules. Factor analysis is a statistical technique used to uncover hidden variables within complex data sets, and it has been previously used to study biomolecular

Systems biology promises to personalize medicine via network-based biomarkers that predict therapeutic effectiveness. Toward this goal, Chang et al. (2009) recently introduced a systems-based approach to break down oncogenic signaling networks into modules that predict the effectiveness of pathway-specific therapeutics.

Complex networks of genes, proteins, and small molecules interact to determine cellular function. The reverse engineering or inference of gene regulatory networks using DNA sequence data, protein–DNA binding data, and observed molecular abundance data has become a major interest of the biological community. We discuss recent successes and remaining challenges in the construction of gene network models and their use for gaining biological insight. We draw lessons from the detailed discussion of several recent gene network inference studies, focusing on the novel advances and limitations of each approach. These approaches differ in the strategies used to incorporate biological data, the level of physical detail and type of model used, and the purpose of the modeling in terms of biological discovery. The approaches we discuss aim to gain insights into the logic of combinatorial regulation occurring at a gene promoter, the activity of regulatory factors distinct from their abundance, the identification of key regulators in a gene network, and the prediction of drug compound mode of action. We suggest possibilities for future directions in network inference studies.


Keywords:

network;
inference;
model;
reverse engineering;
system identification;
systems biology;
computational biology;
gene regulation;
interaction

Reverse engineering gene regulatory networks

Provided herein are pharmaceutical compositions and kits useful for sensitizing a microorganism or a population of microorganisms to a quinolone antibiotic. In a particular aspect, a carbon source and an electron acceptor can sensitize an antibiotic persistent microorganism to treatment with a fluoroquinolone antibiotic. Methods for sensitizing a microorganism to a quinolone antibiotic, reducing the density-dependent persistence (DDP) of an antibiotic resistant microorganism, and reducing the number of persistent cells in a population are also provided. Theses compositions and methods are useful in treating infections resulting from high-density bacterial cultures, such as pneumonia, genitourinary infections, biofilms, prosthetic graft infections, sepsis, and endovascular infections.

Sensitization of bacterial cells to quinolone antibiotics

Literacy and Literacies: Preface

La presente invention concerne l'utilisation d'inhibiteurs RecA comme agents antibiotiques, et permet d'obtenir des inhibiteurs RecA utiles pour traiter des infections. Elle concerne aussi plusieurs compositions et methodes associees avec une inhibition RecA.

James J. Collins

Papers

Reverse engineering gene regulatory networks

Sensitization of bacterial cells to quinolone antibiotics

Literacy and Literacies: Preface

Inhibiteurs reca à activité antibiotique, compositions et méthodes d'utilisation