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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: This work investigates Deep Neural Networks to predict toehold switch function as a canonical riboswitch model in synthetic biology and shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology.
Abstract: Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R2 = 0.43–0.70) previous state-of-the-art thermodynamic and kinetic models (R2 = 0.04–0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology. RNA can be used as a programmable tool for detection of biological analytes. Here the authors use deep neural networks to predict toehold switch functionality in synthetic biology applications.

81 citations

Journal ArticleDOI
TL;DR: It is found that Escherichia coli survival under antibiotic pressure is severely compromised without adenine methylation at GATC sites, and this work indicates that the GATOR methylome provides structural support for bacterial survival during antibiotic stress and suggests targeting bacterial DNA methylation as a viable approach to enhancing antibiotic activity.
Abstract: Antibiotic resistance is an increasingly serious public health threat. Understanding pathways allowing bacteria to survive antibiotic stress may unveil new therapeutic targets. We explore the role of the bacterial epigenome in antibiotic stress survival using classical genetic tools and single-molecule real-time sequencing to characterize genomic methylation kinetics. We find that Escherichia coli survival under antibiotic pressure is severely compromised without adenine methylation at GATC sites. Although the adenine methylome remains stable during drug stress, without GATC methylation, methyl-dependent mismatch repair (MMR) is deleterious and, fueled by the drug-induced error-prone polymerase Pol IV, overwhelms cells with toxic DNA breaks. In multiple E. coli strains, including pathogenic and drug-resistant clinical isolates, DNA adenine methyltransferase deficiency potentiates antibiotics from the β-lactam and quinolone classes. This work indicates that the GATC methylome provides structural support for bacterial survival during antibiotic stress and suggests targeting bacterial DNA methylation as a viable approach to enhancing antibiotic activity.

81 citations

Book ChapterDOI
TL;DR: Mutations in each of the three collagen VI genes (col6a1, COL6a2 and COL6A3) cause Ullrich congenital muscular dystrophy, a severe phenotype, and a mild to moderate phenotype Bethlem myopathy as mentioned in this paper.
Abstract: Mutations in each of the three collagen VI genes COL6A1, COL6A2 and COL6A3 cause two main types of muscle disorders: Ullrich congenital muscular dystrophy, a severe phenotype, and a mild to moderate phenotype Bethlem myopathy. Recently, two additional phenotypes, including a limb-girdle muscular dystrophy phenotype and an autosomal recessive myosclerosis reported in one family with mutations in COL6A2 have been reported. Collagen VI is an important component of the extracellular matrix which forms a microfibrillar network that is found in close association with the cell and surrounding basement membrane. Collagen VI is also found in the interstitial space of many tissues including muscle, tendon, skin, cartilage, and intervertebral discs. Thus, collagen VI mutations result in disorders with combined muscle and connective tissue involvement, including weakness, joint laxity and contractures, and abnormal skin findings.

81 citations

Journal ArticleDOI
TL;DR: Data do not provide consistent support for a relationship between formaldehyde exposure and leukemia risk and are evaluated to determine if publication or reporting biases may be affecting the estimates.

80 citations

Journal ArticleDOI
TL;DR: The presence and persistence of 4 specific alterations in sagittal plane joint kinetics at both comfortable and fast walking speeds imply specific intrinsic pattern differences and allow for new insights into the mechanics of gait in elderly people who fall.

80 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