The geography of spatial synchrony

Spatial synchrony, defined as correlated temporal fluctuations among populations, is a fundamental feature of population dynamics, but many aspects of synchrony remain poorly understood. Few studies have examined detailed geographical patterns of synchrony; instead most focus on how synchrony declines with increasing linear distance between locations, making the simplifying assumption that distance decay is isotropic. By synthesising and extending prior work, we show how geography of synchrony, a term which we use to refer to detailed spatial variation in patterns of synchrony, can be leveraged to understand ecological processes including identification of drivers of synchrony, a long‐standing challenge. We focus on three main objectives: (1) showing conceptually and theoretically four mechanisms that can generate geographies of synchrony; (2) documenting complex and pronounced geographies of synchrony in two important study systems; and (3) demonstrating a variety of methods capable of revealing the geography of synchrony and, through it, underlying organism ecology. For example, we introduce a new type of network, the synchrony network, the structure of which provides ecological insight. By documenting the importance of geographies of synchrony, advancing conceptual frameworks, and demonstrating powerful methods, we aim to help elevate the geography of synchrony into a mainstream area of study and application.     

Neuroendocrine and immune network re-modeling in chronic fatigue syndrome: an exploratory analysis

This work investigates the significance of changes in association patterns linking indicators of neuroendocrine and immune activity in patients with chronic fatigue syndrome (CFS). Gene sets preferentially expressed in specific immune cell isolates were integrated with neuroendocrine data from a large population-based study. Co-expression patterns linking immune cell activity with hypothalamic–pituitary–adrenal (HPA), thyroidal (HPT) and gonadal (HPG) axis status were computed using mutual information criteria. Networks in control and CFS subjects were compared globally in terms of a weighted graph edit distance. Local re-modeling of node connectivity was quantified by node degree and eigenvector centrality measures. Results indicate statistically significant differences between CFS and control networks determined mainly by re-modeling around pituitary and thyroid nodes as well as an emergent immune sub-network. Findings align with known mechanisms of chronic inflammation and support possible immune-mediated loss of thyroid function in CFS exacerbated by blunted HPA axis responsiveness.     

Graph decompositions for demographic loop analysis

A new approach to loop analysis is presented in which decompositions of the total elasticity of a population projection matrix over a set of life history pathways are obtained as solutions of a constrained system of linear equations. In loop analysis, life history pathways are represented by loops in the life cycle graph, and the elasticity of the loop is interpreted as a measure of the contribution of the life history pathway to the population growth rate. Associated with the life cycle graph is a vector space -- the cycle space of the graph -- which is spanned by the loops. The elasticities of the transitions in the life cycle graph can be represented by a vector in the cycle space, and a loop decomposition of the life cycle graph is then defined to be any nonnegative linear combination of the loops which sum to the vector of elasticities. In contrast to previously published algorithms for carrying out loop analysis, we show that a given life cycle graph admits of either a unique loop decomposition or an infinite set of loop decompositions which can be characterized as a bounded convex set of nonnegative vectors. Using this approach, loop decompositions which minimize or maximize a linear objective function can be obtained as solutions of a linear programming problem, allowing us to place lower and upper bounds on the contributions of life history pathways to the population growth rate. Another consequence of our approach to loop analysis is that it allows us to identify the exact tradeoffs in contributions to the population growth rate that must exist between life history pathways.     

Structural similarity enhances interaction propensity of proteins

We study statistical properties of interacting protein-like surfaces and predict two strong, related effects: (i) statistically enhanced self-attraction of proteins; (ii) statistically enhanced attraction of proteins with similar structures. The effects originate in the fact that the probability to find a pattern self-match between two identical, even randomly organized interacting protein surfaces is always higher compared with the probability for a pattern match between two different, promiscuous protein surfaces. This theoretical finding explains statistical prevalence of homodimers in protein-protein interaction networks reported earlier. Further, our findings are confirmed by the analysis of curated database of protein complexes that showed highly statistically significant overrepresentation of dimers formed by structurally similar proteins with highly divergent sequences ("superfamily heterodimers"). We suggest that promiscuous homodimeric interactions pose strong competitive interactions for heterodimers evolved from homodimers. Such evolutionary bottleneck is overcome using the negative design evolutionary pressure applied against promiscuous homodimer formation. This is achieved through the formation of highly specific contacts formed by charged residues as demonstrated both in model and real superfamily heterodimers.     

The community structure of human cellular signaling network

Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.     

Analyzing urban green space pattern and eco-network in Hanoi, Vietnam

In Hanoi, the capital city of Vietnam, there has recently been a growing awareness about the roles and benefits of greening in urbanized areas. As a result, planners and decision-makers propose a combination of water bodies and green areas, using cultural as well as historic values, in a strategic concept for city planning in Hanoi. This study aims at quantifying the landscape patterns and ecological processes or clearly linking pattern to process to identify green space changes and their driving forces, based on gradient analysis combined with landscape metrics, GIS support, and FRAGSTATS 3.3, from 1996 to 2003. The results of gradient analysis taken four directions show that green spaces have been become more fragmented in this period, especially in the south and west directions. These changes could be caused by land use change, economic growth, population increase, urbanization, and weakness in planning and managing the urban development. From this context, graph theory was also applied to find any eco-networking, by mitigating the fragmentation and enhancing the green space connectivity, as a biodiversity conservation strategy for the city. Analyzing the green network based on graph theory indicates that among six different network scenarios which were produced from several models (Traveling Salesman, Paul Revere, Least Cost to User), network F with 37 links, and gamma (0.07), beta (0.62), cost ratio (0.606), circuitry (0.098) and connectivity (0.398) is the best option for ecological restoration in the Hanoi city. This will be a basis for the 2020 Green Space Planning in Hanoi.     

Graph-theoretic methods for the analysis of chemical and biochemical networks. I. Multistability and oscillations in ordinary differential equation models

A chemical mechanism is a model of a chemical reaction network consisting of a set of elementary reactions that express how molecules react with each other. In classical mass-action kinetics, a mechanism implies a set of ordinary differential equations (ODEs) which govern the time evolution of the concentrations. In this article, ODE models of chemical kinetics that have the potential for multiple positive equilibria or oscillations are studied. We begin by considering some methods of stability analysis based on the digraph of the Jacobian matrix. We then prove two theorems originally given by A. N. Ivanova which correlate the bifurcation structure of a mass-action model to the properties of a bipartite graph with nodes representing chemical species and reactions. We provide several examples of the application of these theorems.     

A general algorithm for peak-tracking in multi-dimensional NMR experiments

We present an algorithmic method allowing automatic tracking of NMR peaks in a series of spectra. It consists in a two phase analysis. The first phase is a local modeling of the peak displacement between two consecutive experiments using distance matrices. Then, from the coefficients of these matrices, a value graph containing the a priori set of possible paths used by these peaks is generated. On this set, the minimization under constraint of the target function by a heuristic approach provides a solution to the peak-tracking problem. This approach has been named GAPT, standing for General Algorithm for NMR Peak Tracking. It has been validated in numerous simulations resembling those encountered in NMR spectroscopy. We show the robustness and limits of the method for situations with many peak-picking errors, and presenting a high local density of peaks. It is then applied to the case of a temperature study of the NMR spectrum of the Lipid Transfer Protein (LTP).