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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.


Papers
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Journal ArticleDOI
TL;DR: A dynamic control technique was used to suppress a cardiac arrhythmia called an alternans rhythm in a piece of dissected rabbit heart that adapted to drifting system parameters, making it well suited for the control of physiological rhythms.
Abstract: A dynamic control technique was used to suppress a cardiac arrhythmia called an alternans rhythm ina piece of dissected rabbit heart. Our control algorithm adapted to drifting system parameters, makingit well suited for the control of physiological rhythms. Control of cardiac alternans rhythms may haveimportant clinical implications since they often precede serious cardiac arrhythmias and are a harbingerof sudden cardiac death. [S0031-9007(97)03337-1]

177 citations

Journal ArticleDOI
TL;DR: The work on using input noise and electrical noise to enhance somatosensation in humans and improve the performance of the human balance control system is reviewed.
Abstract: We review our work on using input noise (mechanical and electrical, respectively) to enhance somatosensation in humans and improve the performance of the human balance control system. We also discuss bioengineering applications and future directions for stochastic resonance (SR) based techniques and devices. Age- and disease-related sensory loss may be reversible by exploiting SR-type effects.

177 citations

Journal ArticleDOI
TL;DR: A cell-free in vitro transcription system that uses RNA Output Sensors Activated by Ligand Induction (ROSALIND) to detect contaminants in water, and it is shown that adding RNA circuitry can invert responses, reduce crosstalk and improve sensitivity without protein engineering.
Abstract: Lack of access to safe drinking water is a global problem, and methods to reliably and easily detect contaminants could be transformative We report the development of a cell-free in vitro transcription system that uses RNA Output Sensors Activated by Ligand Induction (ROSALIND) to detect contaminants in water A combination of highly processive RNA polymerases, allosteric protein transcription factors and synthetic DNA transcription templates regulates the synthesis of a fluorescence-activating RNA aptamer The presence of a target contaminant induces the transcription of the aptamer, and a fluorescent signal is produced We apply ROSALIND to detect a range of water contaminants, including antibiotics, small molecules and metals We also show that adding RNA circuitry can invert responses, reduce crosstalk and improve sensitivity without protein engineering The ROSALIND system can be freeze-dried for easy storage and distribution, and we apply it in the field to test municipal water supplies, demonstrating its potential use for monitoring water quality

176 citations

Journal ArticleDOI
TL;DR: The transcriptomes of developing HSCs purified from >2,500 murine embryos and adult mice were acquired and it was found that embryonic hematopoietic elements clustered into three distinct transcriptional states characteristic of the definitive yolk sac, H SCs undergoing specification, and definitive HSCS.

175 citations

Journal ArticleDOI
TL;DR: This work uses a systems-level approach to determine the genomic and physiological responses of E. coli to HU treatment and suggests a model by which H U treatment rapidly induces a set of protective responses to manage genomic instability.

175 citations


Cited by
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Journal ArticleDOI
04 Jun 1998-Nature
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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.

39,297 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组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�ly.
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。

18,940 citations

Journal ArticleDOI
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
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.

18,415 citations

Journal ArticleDOI
TL;DR: In this paper, Imagined communities: Reflections on the origin and spread of nationalism are discussed. And the history of European ideas: Vol. 21, No. 5, pp. 721-722.

13,842 citations

Journal ArticleDOI
Stephen S Lim1, Theo Vos, Abraham D. Flaxman1, Goodarz Danaei2  +207 moreInstitutions (92)
TL;DR: In this paper, the authors estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010.

9,324 citations