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

Learning Capitalist Culture: Deep in the Heart of Tejas:Learning Capitalist Culture: Deep in the Heart of Tejas.

T.P.2 10 The effects of prednisone on exercised-induced muscle damage in mdx mice

Enzymes are highly specific catalysts delivering improved drugs and greener industrial processes. Naturally occurring enzymes must typically be optimized which is often accomplished through directed evolution; however, this is still a labor- and capital-intensive process, due in part to multiple molecular biology steps including DNA extraction, in vitro library generation, transformation, and limited screening throughput. We present an effective and broadly applicable continuous evolution platform that enables controlled exploration of fitness landscape to evolve enzymes at ultrahigh throughput based on direct measurement of enzymatic activity. This drop-based microfluidics platform cycles cells between growth and mutagenesis followed by screening with minimal human intervention, relying on the nCas9 chimera with mutagenesis polymerase to produce in vivo gene diversification using sgRNAs tiled along the gene. We evolve alditol oxidase to change its substrate specificity towards glycerol, turning a waste product into a valuable feedstock. We identify a variant with a 10.5-fold catalytic efficiency.

Controlled continuous evolution of enzymatic activity screened at ultrahigh throughput using drop-based microfluidics.

Schistosomes are deadly pathogens responsible for the neglected tropical disease schistosomiasis. The parasite's virulence is aided by a skin-like tissue called the tegument. The study of the tegument is hampered by a lack of tools suitable for visualizing the tissue. Here we describe a novel methodology employing fluorophore-conjugated dextrans that allows specific fluorescent labeling of the tegument and that is compatible with downstream fluorescence-labeling techniques including phalloidin labeling, RNA FISH, and immunofluorescence.

James J. Collins

Papers

Learning Capitalist Culture: Deep in the Heart of Tejas:Learning Capitalist Culture: Deep in the Heart of Tejas.

T.P.2 10 The effects of prednisone on exercised-induced muscle damage in mdx mice

Controlled continuous evolution of enzymatic activity screened at ultrahigh throughput using drop-based microfluidics.

Labeling of the Schistosoma mansoni Tegument.

Engineering Models to Scale