Order-Based Representation in Random Networks of Cortical Neurons

The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.     

Varieties of Representation in Evolved and Embodied Neural Networks

In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and anorganism. My approach in the current paper isto explore this and other key themes inneurosemantics through the use of computermodels of neural networks embodied and evolvedin virtual organisms. The models allow for thelaying bare of the causal economies of entireyet simple artificial organisms so that therelations between the neural bases of, forinstance, representation in perception andmemory can be regarded in the context of anentire organism. On the basis of thesesimulations, I argue for an account ofneurosemantics adequate for the solution of theeconomy problem.     

Charles Girard: Relationships and Representation in Nineteenth Century Systematics

Early nineteenth century systematists sought to describe what they called the Natural System or the Natural Classification. In the nineteenth century, there was no agreement about the basis of observed patterns of similarity between organisms. What did these systematists think they were doing, when they named taxa, proposed relationships between taxa, and arranged taxa into representational schemes? In this paper I explicate Charles Frederic Girard’s (1822–1895) theory and method of systematics. A student of Louis Agassiz, and subsequently (1850–1858) a collaborator with Spencer Baird, Girard claimed that natural classificatory methods do not presuppose either a special creationist or an evolutionary theory of the natural world. The natural system, in Girard’s view, comprises three distinct ways in which organisms can be related to each other. Girard analyzed these relationships, and justified his classificatory methodology, by appeal to his embryological and physiological work. Girard offers an explicit theoretical answer to the question, what characters are evidence for natural classificatory hypotheses? I show that the challenge of simultaneously depicting the three distinct types of relationship led Girard to add a third dimension to his classificatory diagrams.     

Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks

One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks.We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly calculated and screened with neither network size nor the number of required interventions posing major challenges.     

Genetic Structure and the Search for Genotype-Phenotype Relationships: An Example from Disequilibrium in the Apo B Gene Region

We analyzed allelic associations (disequilibria) for four restriction fragment length polymorphisms (RFLPs) in the region of the 43-kb Apo B gene in a sample of 233 unrelated individuals from Montreal, Canada, sampled for health. This total sample (T) included 160 individuals of known French Canadian (FC) ancestry. We present a rigorous application of current methodology to these samples, including estimation of type II error probabilities and correlations between markers for estimates of disequilibria. We then consider the utility of these estimates of allelic disequilibria for the interpretation of genotype-phenotype relations. Significant deviations from Hardy-Weinberg equilibrium were not predicted by proximity to other markers in disequilibrium. We found significant quadri-allelic disequilibrium for two marker pairs despite absence of significant deviations from Hardy-Weinberg equilibrium for either marker or tri-allelic disequilibrium, respectively. Altogether these results underscore the complexity of the genotypic structure of the data. A combination of nonevolutionary factors, including sampling for health, small sample size and data exclusion due to methodological constraints of not successfully typing all members of the sample for every RFLP, is a likely explanation for this complexity. These types of factors are common to many RFLP studies. Patterns of composite di-allelic disequilibrium indicated that some RFLP allele pairs may have a longer shared evolutionary history than others and that disequilibrium is not predicted by distance between RFLPs. Type II error probabilities were generally much higher than those for type I errors. Correlations between marker pairs for disequilibria were generally not high. We show from a review of 14 published studies of association between the XbaI RFLP and variation in a total of 15 lipid traits that deviations from Hardy-Weinberg equilibrium can cause substantial differences in the estimation of variability associated with phenotypic differences among marker genotypes relative to Hardy-Weinberg conditions.     

Stress Impact on Resting State Brain Networks

Resting state brain networks (RSNs) are spatially distributed large-scale networks, evidenced by resting state functional magnetic resonance imaging (fMRI) studies. Importantly, RSNs are implicated in several relevant brain functions and present abnormal functional patterns in many neuropsychiatric disorders, for which stress exposure is an established risk factor. Yet, so far, little is known about the effect of stress in the architecture of RSNs, both in resting state conditions or during shift to task performance. Herein we assessed the architecture of the RSNs using functional magnetic resonance imaging (fMRI) in a cohort of participants exposed to prolonged stress (participants that had just finished their long period of preparation for the medical residence selection exam), and respective gender- and age-matched controls (medical students under normal academic activities). Analysis focused on the pattern of activity in resting state conditions and after deactivation. A volumetric estimation of the RSNs was also performed. Data shows that stressed participants displayed greater activation of the default mode (DMN), dorsal attention (DAN), ventral attention (VAN), sensorimotor (SMN), and primary visual (VN) networks than controls. Importantly, stressed participants also evidenced impairments in the deactivation of resting state-networks when compared to controls. These functional changes are paralleled by a constriction of the DMN that is in line with the pattern of brain atrophy observed after stress exposure. These results reveal that stress impacts on activation-deactivation pattern of RSNs, a finding that may underlie stress-induced changes in several dimensions of brain activity.     

Identifying All Moiety Conservation Laws in Genome-Scale Metabolic Networks

The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell''s metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.     

Predicting Positive and Negative Relationships in Large Social Networks

In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers'' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.     

Impact of Repeated Exposures on Information Spreading in Social Networks

Clustered structure of social networks provides the chances of repeated exposures to carriers with similar information. It is commonly believed that the impact of repeated exposures on the spreading of information is nontrivial. Does this effect increase the probability that an individual forwards a message in social networks? If so, to what extent does this effect influence people’s decisions on whether or not to spread information? Based on a large-scale microblogging data set, which logs the message spreading processes and users’ forwarding activities, we conduct a data-driven analysis to explore the answer to the above questions. The results show that an overwhelming majority of message samples are more probable to be forwarded under repeated exposures, compared to those under only a single exposure. For those message samples that cover various topics, we observe a relatively fixed, topic-independent multiplier of the willingness of spreading when repeated exposures occur, regardless of the differences in network structure. We believe that this finding reflects average people’s intrinsic psychological gain under repeated stimuli. Hence, it makes sense that the gain is associated with personal response behavior, rather than network structure. Moreover, we find that the gain is robust against the change of message popularity. This finding supports that there exists a relatively fixed gain brought by repeated exposures. Based on the above findings, we propose a parsimonious model to predict the saturated numbers of forwarding activities of messages. Our work could contribute to better understandings of behavioral psychology and social media analytics.     

Relationships of Hair Mineral Concentrations with Insulin Resistance in Metabolic Syndrome

Mineral deficiencies can cause impaired insulin release and insulin resistance. This study was conducted to investigate the relationship between hair mineral concentrations and insulin resistance in patients with metabolic syndrome (MS). A total of 456 subjects (161 patients with MS and 295 subjects without MS) were reviewed, and fasting plasma glucose, triglycerides, HDL-cholesterol, homeostasis assessment model-insulin resistance (HOMA-IR), and hair mineral concentrations were analyzed. While hair sodium and potassium concentrations were significantly higher, the hair calcium, magnesium, and zinc concentrations were lower in the MS group than in the control group. Regarding toxic element measurements, the hair arsenic (As) and lead (Pb) concentrations were higher in the MS group than in the control group. The results of multiple regression analysis, after adjusting for age, showed significant relationships between the Na/Mg and Ca/P ratios and HOMA-IR (R ²?=?0.109, p?<?0.05). The Ca, Na, K, and B concentrations were also associated with HOMA-IR (R ²?=?0.116, p?<?0.05). The hair Na concentration was significantly associated with MS, even after adjusting for age, visceral adipose tissue, and HOMA-IR (OR 1.020; 95 % CI 1.001–1.040; p?=?0.036). Our findings suggest that hair mineral concentrations, such as calcium, magnesium, zinc, sodium, and potassium concentrations, may play a role in the development of insulin resistance.     

Relationships in six groups of rhesus monkeys I. Networks

Monkey groups are characterized in terms of their networks of relationships. Six groups, each consisting of one adult male, several adult females and immatures, were studied over the same time period. This provided data on interaction patterns within groups and the ways in which individuals' kinship, backgrounds, idiosyncrasies, age and sex affected their interactions. Consistencies and changes in group networks in the course of time and in the face of events such as births, deaths, separations, and introductions are given special attention. Principles of networks are discussed with reference to problems of social structure such as cohesion, permeability, communication, stability and competition.     

Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.     

Impact of Alien Plant Invaders on Pollination Networks in Two Archipelagos

Mutualistic interactions between plants and animals promote integration of invasive species into native communities. In turn, the integrated invaders may alter existing patterns of mutualistic interactions. Here we simultaneously map in detail effects of invaders on parameters describing the topology of both plant-pollinator (bi-modal) and plant-plant (uni-modal) networks. We focus on the invader Opuntia spp., a cosmopolitan alien cactus. We compare two island systems: Tenerife (Canary Islands) and Menorca (Balearic Islands). Opuntia was found to modify the number of links between plants and pollinators, and was integrated into the new communities via the most generalist pollinators, but did not affect the general network pattern. The plant uni-modal networks showed disassortative linkage, i.e. species with many links tended to connect to species with few links. Thus, by linking to generalist natives, Opuntia remained peripheral to network topology, and this is probably why native network properties were not affected at least in one of the islands. We conclude that the network analytical approach is indeed a valuable tool to evaluate the effect of invaders on native communities.     

Dynamics of Random Boolean Networks under Fully Asynchronous Stochastic Update Based on Linear Representation

A novel algebraic approach is proposed to study dynamics of asynchronous random Boolean networks where a random number of nodes can be updated at each time step (ARBNs). In this article, the logical equations of ARBNs are converted into the discrete-time linear representation and dynamical behaviors of systems are investigated. We provide a general formula of network transition matrices of ARBNs as well as a necessary and sufficient algebraic criterion to determine whether a group of given states compose an attractor of length in ARBNs. Consequently, algorithms are achieved to find all of the attractors and basins in ARBNs. Examples are showed to demonstrate the feasibility of the proposed scheme.     

Stochastic Simulation of Biomolecular Networks in Dynamic Environments

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.     

Structure-Dynamics Relationships in Bursting Neuronal Networks Revealed Using a Prediction Framework

The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small () networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger () networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.