James J. Collins
Other affiliations: Baylor College of Medicine, University at Albany, SUNY, State University of New York System ...read more
Bio: James J. Collins is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topic(s): Synthetic biology & Population. The author has an hindex of 151, co-authored 669 publication(s) receiving 89476 citation(s). Previous affiliations of James J. Collins include Baylor College of Medicine & University at Albany, SUNY.
Papers published on a yearly basis
TL;DR: The construction of a genetic toggle switch is presented—a synthetic, bistable gene-regulatory network—in Escherichia coli and a simple theory is provided that predicts the conditions necessary for bistability.
Abstract: It has been proposed' that gene-regulatory circuits with virtually any desired property can be constructed from networks of simple regulatory elements. These properties, which include multistability and oscillations, have been found in specialized gene circuits such as the bacteriophage lambda switch and the Cyanobacteria circadian oscillator. However, these behaviours have not been demonstrated in networks of non-specialized regulatory components. Here we present the construction of a genetic toggle switch-a synthetic, bistable gene-regulatory network-in Escherichia coli and provide a simple theory that predicts the conditions necessary for bistability. The toggle is constructed from any two repressible promoters arranged in a mutually inhibitory network. It is flipped between stable states using transient chemical or thermal induction and exhibits a nearly ideal switching threshold. As a practical device, the toggle switch forms a synthetic, addressable cellular memory unit and has implications for biotechnology, biocomputing and gene therapy.
TL;DR: A new method for calculating the largest Lyapunov exponent from an experimental time series is presented that is fast, easy to implement, and robust to changes in the following quantities: embedding dimension, size of data set, reconstruction delay, and noise level.
Abstract: Detecting the presence of chaos in a dynamical system is an important problem that is solved by measuring the largest Lyapunov exponent. Lyapunov exponents quantify the exponential divergence of initially close state-space trajectories and estimate the amount of chaos in a system. We present a new method for calculating the largest Lyapunov exponent from an experimental time series. The method follows directly from the definition of the largest Lyapunov exponent and is accurate because it takes advantage of all the available data. We show that the algorithm is fast, easy to implement, and robust to changes in the following quantities: embedding dimension, size of data set, reconstruction delay, and noise level. Furthermore, one may use the algorithm to calculate simultaneously the correlation dimension. Thus, one sequence of computations will yield an estimate of both the level of chaos and the system complexity.
05 Nov 2010-Cell Stem Cell
TL;DR: It is shown that this approach can reprogram multiple human cell types to pluripotency with efficiencies that greatly surpass established protocols and represents a safe, efficient strategy for somatic cell reprogramming and directing cell fate that has broad applicability for basic research, disease modeling, and regenerative medicine.
Abstract: SUMMARY Clinical application of induced pluripotent stem cells (iPSCs) is limited by the low efficiency of iPSC derivation and the fact that most protocols modify the genome to effect cellular reprogramming. Moreover, safe and effective means of directing the fate of patient-specific iPSCs toward clinically useful cell types are lacking. Here we describe a simple, nonintegrating strategy for reprogramming cell fate based on administration of synthetic mRNA modified to overcome innate antiviral responses. We show that this approach can reprogram multiple human cell types to pluripotency with efficiencies that greatly surpass established protocols. We further show that the same technology can be used to efficiently direct the differentiation of RNA-induced pluripotent stem cells (RiPSCs) into terminally differentiated myogenic cells. This technology represents a safe, efficient strategy for somatic cell reprogramming and directing cell fate that has broad applicability for basic research, disease modeling, and regenerative medicine.
01 Jun 2005-Nature Reviews Genetics
TL;DR: Stochasticity in gene expression can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
Abstract: Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
TL;DR: The results suggest that all three major classes of bactericidal drugs can be potentiated by targeting bacterial systems that remediate hydroxyl radical damage, including proteins involved in triggering the DNA damage response, e.g., RecA.
Abstract: Antibiotic mode-of-action classification is based upon drug-target interaction and whether the resultant inhibition of cellular function is lethal to bacteria. Here we show that the three major classes of bactericidal antibiotics, regardless of drug-target interaction, stimulate the production of highly deleterious hydroxyl radicals in Gram-negative and Gram-positive bacteria, which ultimately contribute to cell death. We also show, in contrast, that bacteriostatic drugs do not produce hydroxyl radicals. We demonstrate that the mechanism of hydroxyl radical formation induced by bactericidal antibiotics is the end product of an oxidative damage cellular death pathway involving the tricarboxylic acid cycle, a transient depletion of NADH, destabilization of iron-sulfur clusters, and stimulation of the Fenton reaction. Our results suggest that all three major classes of bactericidal drugs can be potentiated by targeting bacterial systems that remediate hydroxyl radical damage, including proteins involved in triggering the DNA damage response, e.g., RecA.
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.
28 Jul 2005
01 Jan 2001-Reviews of Modern Physics
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.
01 Sep 1995-History of European Ideas
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.
Abstract: (1995). Imagined communities: Reflections on the origin and spread of nationalism. History of European Ideas: Vol. 21, No. 5, pp. 721-722.
Stephen S Lim1, Theo Vos, Abraham D. Flaxman1, Goodarz Danaei2 +207 more•Institutions (92)
15 Dec 2012-The Lancet
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.
Abstract: Methods We 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 eff ects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. W e estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specifi c deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. Findings In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2–7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5–7·0]), and alcohol use (5·5% [5·0–5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8–9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6–8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4–6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2–10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily aff ect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient defi ciencies, fell in rank between 1990 and 2010, with unimproved water