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.

Wavelets of excitability in sensory neurons.

Abstract: We have investigated variations in the excitability of mammalian cutaneous mechanoreceptor neurons. We focused on the phase dynamics of an action potential relative to a periodic stimulus, showing that the excitability of these sensory neurons has interesting nonstationary oscillations. Using a wavelet analysis, these oscillations were characterized through the depiction of their period as a function of time. It was determined that the induced oscillations are weakly dependent on the stimulus frequency, and that lower temperatures significantly reduce the frequency of the phase response. Our results reveal novel excitability properties in sensory neurons, and, more generally, could prove significant in the deduction of mechanistic attributes underlying the nonstationary excitability in neuronal systems. Since peripheral neurons feed information to the CNS, variable responses observed in higher regions may be generated in part at the site of sensory detection.

Theorizing Cultural Memory: Totalizing Recall?

Abstract: The study of popular culture has undergone a number of profound changes over the past decade, fundamentally altering how the "object" of popular culture is to be framed in relation to issues of textuality, ideology, and reception. With the advent of the cultural studies movement, popular culture ceased to be "other," as the very notion of "culture" was redefined in descriptive, all-encompassing terms, rather than in the evaluative, restrictive categorizations of Matthew Arnold and his descendants. At the same time, postmoder artists and theoreticians were also constructing a notion of culture that rejected, just as fundamentally, any kind of hard and fast distinctions between high art and popular culture, including elements of both in eclectic designs that envision "culture" as the intersection of heterogeneous discourses. While there are a number of points of contact between the reformulations by the Birmingham school and postmodernists, a significant common denominator is the desire to connect the popular to specific cultural traditions that cannot be reduced to mere nostalgia, because they play a vital role in the formation of cultural identity. George Lipsitz, in his preface to Time Passages, states his goal succinctly: "I wish to explore the ways in which collective memory and popular culture are peculiarly linked-how the infinitely renewable present of electronic mass media creates a crisis for collective memory, and how collective memory decisively frames the production and reception of commercial culture" (vii). Time Passages is in many ways an unprecedented project. Lipsitz's study is most compelling in his careful delineation of how the different forms of cultural expression produced by various subcultural groups (African-American, Mexican-American, etc.) have had a profound impact on mainstream popular culture. Rather than simply bemoaning the disappearance of folk-cultural traditions, Lipsitz argues convincingly that such traditions persist, and though they have been subject to adaptation and commercialization, they still retain their ability to express the conflicts and contradictions within American culTime Passages: Collective Memory and American Popular Culture By George Lipsitz University of Minnesota Press, 1989

Design and Construction of Genetic Applets

Abstract: : In this Project, we took a forward engineering approach to understanding and controlling gene expression. Specifically, we developed a theory of gene networks that will execute a prescribed series of cellular functions. By designing the gene networks ourselves, we can allow only simple interactions and thus avoid the enormous complexity that biology has developed over years of evolution. This project involved both theoretical and experimental work.

Journal club. A bioengineer gets schooled by Escherichia coli.

Abstract: The ability to learn from situations and to predict certain outcomes sets us apart from many living things. It prevents many of us from chasing balls into busy streets or placing bottles of ethanol near Bunsen burners. Still, it didn’t stop thousands of US researchers submitting applications for the National Institutes of Health’s Challenge Grants — funded by President Barack Obama’s economic stimulus package — despite an expected success rate little better than one or two per cent. To enjoy the benefits of learning and predictive behaviour, we usually assume that you need a nervous system or at least a neuron. So it was surprising to read that Saeed Tavazoie at Princeton University, New Jersey, and his colleagues have demonstrated that bacteria can learn and exhibit anticipatory behaviour (I. Tagkopoulos et al. Science 320, 1313–1317; 2008). They show computationally and experimentally that Escherichia coli can learn temporal correlations between environmental stimuli — for example, that an increase in temperature is followed by a decrease in oxygen levels — allowing the bacteria to predict and prepare for future environmental changes. The researchers show that this associative learning is accomplished by rewiring of biochemical networks. Strikingly, they also show that, like many of us, E. coli quickly ‘unlearn’ (in fewer than 100 generations) what they had learned in a new situation. Now we know that bacteria can be taught such tricks, it will be interesting to see if we can use novel combinations of environmental stimuli to train microbes to efficiently convert biomass into energy sources, such as hydrogen or butanol. By providing E. coli with such an educational stimulus package, we may be able to boost the global economy. GEOLOGY

Enhancing nutritional niche and host defenses by modifying the gut microbiome

Abstract: The gut microbiome is essential for processing complex food compounds and synthesizing nutrients that the host cannot digest or produce, respectively. New model systems are needed to study how the metabolic capacity provided by the gut microbiome impacts the nutritional status of the host, and to explore possibilities for altering host metabolic capacity via the microbiome. Here, we colonized the nematode Caenorhabditis elegans gut with cellulolytic bacteria that enabled C. elegans to utilize cellulose, an otherwise indigestible substrate, as a carbon source. Cellulolytic bacteria as a community component in the worm gut can also support additional bacterial species with specialized roles, which we demonstrate by using Lactobacillus plantarum to protect C. elegans against Salmonella enterica infection. This work shows that engineered microbiome communities can be used to endow host organisms with novel functions, such as the ability to utilize alternate nutrient sources or to better fight pathogenic bacteria.

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.