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.

Synthetic biology devices for in vitro and in vivo diagnostics

Abstract: There is a growing need to enhance our capabilities in medical and environmental diagnostics. Synthetic biologists have begun to focus their biomolecular engineering approaches toward this goal, offering promising results that could lead to the development of new classes of inexpensive, rapidly deployable diagnostics. Many conventional diagnostics rely on antibody-based platforms that, although exquisitely sensitive, are slow and costly to generate and cannot readily confront rapidly emerging pathogens or be applied to orphan diseases. Synthetic biology, with its rational and short design-to-production cycles, has the potential to overcome many of these limitations. Synthetic biology devices, such as engineered gene circuits, bring new capabilities to molecular diagnostics, expanding the molecular detection palette, creating dynamic sensors, and untethering reactions from laboratory equipment. The field is also beginning to move toward in vivo diagnostics, which could provide near real-time surveillance of multiple pathological conditions. Here, we describe current efforts in synthetic biology, focusing on the translation of promising technologies into pragmatic diagnostic tools and platforms.

Effects of Colored Noise on Stochastic Resonance in Sensory Neurons

Abstract: Noise can assist neurons in the detection of weak signals via a mechanism known as stochastic resonance (SR). We demonstrate experimentally that SR-type effects can be obtained in rat sensory neurons with white noise, 1/f noise, or 1/f{sup 2} noise. For low-frequency input noise, we show that the optimal noise intensity is the lowest and the output signal-to-noise ratio the highest for conventional white noise. We also show that under certain circumstances, 1/f noise can be better than white noise for enhancing the response of a neuron to a weak signal. We present a theory to account for these results and discuss the biological implications of 1/f noise. {copyright} {ital 1999} {ital The American Physical Society}

Genome-wide analyses reveal a role for peptide hormones in planarian germline development.

Abstract: Bioactive peptides (i.e., neuropeptides or peptide hormones) represent the largest class of cell-cell signaling molecules in metazoans and are potent regulators of neural and physiological function. In vertebrates, peptide hormones play an integral role in endocrine signaling between the brain and the gonads that controls reproductive development, yet few of these molecules have been shown to influence reproductive development in invertebrates. Here, we define a role for peptide hormones in controlling reproductive physiology of the model flatworm, the planarian Schmidtea mediterranea. Based on our observation that defective neuropeptide processing results in defects in reproductive system development, we employed peptidomic and functional genomic approaches to characterize the planarian peptide hormone complement, identifying 51 prohormone genes and validating 142 peptides biochemically. Comprehensive in situ hybridization analyses of prohormone gene expression revealed the unanticipated complexity of the flatworm nervous system and identified a prohormone specifically expressed in the nervous system of sexually reproducing planarians. We show that this member of the neuropeptide Y superfamily is required for the maintenance of mature reproductive organs and differentiated germ cells in the testes. Additionally, comparative analyses of our biochemically validated prohormones with the genomes of the parasitic flatworms Schistosoma mansoni and Schistosoma japonicum identified new schistosome prohormones and validated half of all predicted peptide-encoding genes in these parasites. These studies describe the peptide hormone complement of a flatworm on a genome-wide scale and reveal a previously uncharacterized role for peptide hormones in flatworm reproduction. Furthermore, they suggest new opportunities for using planarians as free-living models for understanding the reproductive biology of flatworm parasites.

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.