James J. Collins

Bio: James J. Collins is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & Population. The author has an hindex of 151, co-authored 669 publications receiving 89476 citations. Previous affiliations of James J. Collins include Baylor College of Medicine & University at Albany, SUNY.

Functionalization of endogenous bacteria

Abstract: Various aspects and embodiments of the present disclosure are directed to methods and compositions for functionalizing endogenous bacteria in vivo. The methods include delivering to endogenous bacterial cells a recombinant bacteriophage or phagemid that is engineered to contain at least one genetic circuit.

CRISPR Guide RNA Cloning for Mammalian Systems

Abstract: The outlined protocol describes streamlined methods for the efficient and cost-effective generation of Cas9-associated guide RNAs. Two alternative strategies for guide RNA (gRNA) cloning are outlined based on the usage of the Type IIS restriction enzyme BsmBI in combination with a set of compatible vectors. Outside of the access to Sanger sequencing services to validate the generated vectors, no special equipment or reagents are required aside from those that are standard to modern molecular biology laboratories. The outlined method is primarily intended for cloning one single gRNA or one paired gRNA-expressing vector at a time. This procedure does not scale well for the generation of libraries containing thousands of gRNAs. For those purposes, alternative sources of oligonucleotide synthesis such as oligo-chip synthesis are recommended. Finally, while this protocol focuses on a set of mammalian vectors, the general strategy is plastic and is applicable to any organism if the appropriate gRNA vector is available.

Methods of treating gram-negative microbial infections

Abstract: Provided herein are methods and compositions for treatment of a microbial infection or a microbial biofilm in a subject or on a surface by administering to the subject or surface determined to have or suspected of having a microbial infection/film an antimicrobial agent in combination with a silver-containing compound (e.g., a silver salt). In some embodiments, a silver-containing compound can increase activity of the antimicrobial agent. In other embodiments, addition of a silver-containing compound to an antimicrobial agent can expand the antimicrobial spectrum of the antimicrobial agent such that the antimicrobial agent originally indicated for treatment of one microbial strain (e.g., Gram-positive microbes) becomes effective for treating additional microbial strains (e.g., Gram-negative microbes). Other aspects relating to methods and compositions for delivering an agent to a microbe by increasing the membrane permeability of the microbe are also provided herein.

Collective dynamics of small-world networks

Abstract: 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.

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

Abstract: 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。

Statistical mechanics of complex networks

Abstract: 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.