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.

Noise-Enhanced Human Sensorimotor Function Using Stochastic Resonance to Enhance Somatosensation and Improve Performance of the Human Balance Control System

Abstract: omatosensory feedback is an important component of the balance control system. Older adults, patients with diabetic neuropathy, and patients with stroke exhibit a marked deficit in the perception of cutaneous and proprioceptive stimuli (e.g., see [1] and references therein). Such changes to the somatosensory system, which have been associated with increases in mechanoreceptor detection thresholds , may predispose individuals to falls, which are the most common cause of morbidity and mortality among older persons. Accordingly, there is a pressing need to develop bioengi-neering techniques and devices that improve sensorimotor function in older adults and patients with sensory deficits. Recently, it has been shown that noise can enhance the detection and transmission of weak signals in certain nonlinear systems, via a mechanism known as stochastic resonance (SR). The phenomenon of SR, which is counterintuitive given that noise has traditionally been viewed as a detriment to signal detection and transmission, is based on the concept that the flow of information through a system can be maximized by the presence of a particular, nonzero level of noise. SR was originally proposed in the context of global climate modeling as a possible explanation for the periodic recurrences of the Earth's ice ages [2], [3]. Since then, SR-type dynamics have been demonstrated in a variety of physical and biological systems [4]-[10], including rat cutaneous afferents [11] and human muscle spindles [12]. In this article, we review our work on using input noise (mechanical and electrical, respectively) to enhance somatosensation in humans [1], [13]-[16] and improve the performance of the human balance control system [17], [18]. We also discuss bioengineering applications and future directions for SR-based techniques and devices. As a first effort in this area, we designed a study to examine the effects of input noise on tactile sensation in humans. Specifically , we were interested in studying noise-mediated changes in the perception of subthreshold tactile stimuli. We hypothesized that the ability of an individual to detect a subthreshold tactile stimulus can be significantly enhanced by the presence of a particular, nonzero level of noise [13], [14]. To test this hypothesis, we conducted psychophysical experiments on ten healthy young subjects (age 18-31 years, mean 25 years). Local indentations were applied to the tip of each subject's right middle digit using a cylindrical probe (Figure 1). The protocol consisted of the presentation of: a) a subthreshold mechanical stimulus plus mechanical noise, or b) no mechanical stimulus …

Compositions comprising riboregulators and methods of use thereof

Abstract: The invention provides novel and versatile classes of riboregulators, including inter alia activating and repressing riboregulators, switches, and trigger and sink RNA, and methods of their use for detecting RNAs in a sample such as a well and in modulating protein synthesis and expression.

Issues of methods and interpretation in the National Cancer Institute formaldehyde cohort study.

Abstract: In 2004, the International Agency for Research on Cancer (IARC) reclassified formaldehyde (FA) from a probable (Group 2A) to a known human carcinogen (Group 1) citing results for nasopharyngeal cancer (NPC) mortality from the follow-up through 1994 of the National Cancer Institute formaldehyde cohort study. To the contrary, in 2012, the Committee for Risk Assessment of the European Chemicals Agency disagreed with the proposal to classify FA as a known human carcinogen (Carc. 1A), proposing a lower but still protective category, namely as a substance which is presumed to have carcinogenic potential for humans (Carc. 1B). Thus, U.S. and European regulatory agencies currently disagree about the potential human carcinogenicity of FA. In 2013, the National Cancer Institute reported results from their follow-up through 2004 of the formaldehyde cohort and concluded that the results continue to suggest a link between FA exposure and NPC. We discuss in this commentary why we believe that this interpretation is neither consistent with the available data from the most recent update of the National Cancer Institute cohort study nor with other research findings from that cohort, other large cohort studies and the series of publications by some of the current authors, including an independent study of one of the National Cancer Institute’s study plants. Another serious concern relates to the incorrectness of the data from the follow-up through 1994 of the National Cancer Institute study stemming from incomplete mortality ascertainment. While these data were corrected by the National Cancer Institute in subsequent supplemental publications, incorrect data from the original publications have been cited extensively in recent causal evaluations of FA, including IARC. We conclude that the NCI publications that contain incorrect data from the incomplete 1994 mortality follow-up should be retracted entirely or corrected via published errata in the corresponding journals, and efforts should be made to re-analyze data from the 2004 follow-up of the NCI cohort study.

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.