Hypergraph models of metabolism

In this paper, we employ a directed hypergraph model to investigate the extent to which environmental variability influences the set of available biochemical reactions within a living cell. Such an approach avoids the limitations of the usual complex network formalism by allowing for the multilateral relationships (i.e. connections involving more than two nodes) that naturally occur within many biological processes. More specifically, we extend the concept of network reciprocity to complex hyper-networks, thus enabling us to characterise a network in terms of the existence of mutual hyper-connections, which may be considered a proxy for metabolic network complexity. To demonstrate these ideas, we study 115 metabolic hyper-networks of bacteria, each of which can be classified into one of 6 increasingly varied habitats. In particular, we found that reciprocity increases significantly with increased environmental variability, supporting the view that organism adaptability leads to increased complexities in the resultant biochemical networks

Network motif frequency vectors reveal evolving metabolic network organisation

At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this under- lying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic network

Can linear collocation ever beat quadratic?

Computational approaches are becoming increasingly important in neuroscience, where complex, nonlinear systems modelling neural activity across multiple spatial and temporal scales are the norm. This paper considers collocation techniques for solving neural field models, which typically take the form of a partial integro-dfferential equation. In particular, we investigate and compare the convergence properties of linear and quadratic collocation on both regular grids and more general meshes not fixed to the regular Cartesian grid points. For regular grids we perform a comparative analysis against more standard techniques, in which the convolution integral is computed either by using Fourier based methods or via the trapezoidal rule. Perhaps surprisingly, we find that on regular, periodic meshes, linear collocation displays better convergence properties than quadratic collocation, and is in fact comparable with the spectral convergence displayed by both the Fourier based and trapezoidal techniques. However, for more general meshes we obtain superior convergence of the convolution integral using higher order methods, as expected

Complexity and robustness in hypernetwork models of metabolism

Metabolic reaction data is commonly modelled using a complex network approach, whereby nodes represent the chemical species present within the organism of interest, and connections are formed between those nodes participating in the same chemical reaction. Unfortunately, such an approach provides an inadequate description of the metabolic process in general, as a typical chemical reaction will involve more than two nodes, thus risking over-simplification of the the system of interest in a potentially significant way. In this paper, we employ a complex hypernetwork formalism to investigate the robustness of bacterial metabolic hypernetworks by extending the concept of a percolation process to hypernetworks. Importantly, this provides a novel method for determining the robustness of these systems and thus for quantifying their resilience to random attacks/errors. Moreover, we performed a site percolation analysis on a large cohort of bacterial metabolic networks and found that hypernetworks that evolved in more variable enviro nments displayed increased levels of robustness and topological complexity

A numerical simulation of neural fields on curved geometries

Despite the highly convoluted nature of the human brain, neural field models typically treat the cortex as a planar two-dimensional sheet of neurons. Here, we present an approach for solving neural field equations on surfaces more akin to the cortical geometries typically obtained from neuroimaging data. Our approach involves solving the integral form of the partial integro-differential equation directly using collocation techniques alongside efficient numerical procedures for determining geodesic distances between neural units. To illustrate our methods, we study localised activity patterns in a two-dimensional neural field equation posed on a periodic square domain, the curved surface of a torus, and the cortical surface of a rat brain, the latter of which is constructed using neuroimaging data. Our results are twofold: Firstly, we find that collocation techniques are able to replicate solutions obtained using more standard Fourier based methods on a flat, periodic domain, independent of the underlying mesh. This result is particularly significant given the highly irregular nature of the type of meshes derived from modern neuroimaging data. And secondly, by deploying efficient numerical schemes to compute geodesics, our approach is not only capable of modelling macroscopic pattern formation on realistic cortical geometries, but can also be extended to include cortical architectures of more physiological relevance. Importantly, such an approach provides a means by which to investigate the influence of cortical geometry upon the nucleation and propagation of spatially localised neural activity and beyond. It thus promises to provide model-based insights into disorders like epilepsy, or spreading depression, as well as healthy cognitive processes like working memory or attention

Behavior Change through Innovation Adoption: A Case Study of Alternative Mobility Solutions

The sustainability of urban transportation is becoming one of the biggest concerns in the field of mobility. Transportation behavior of urban citizens is highly car dependent. Alternative mobility solutions (AMS) like car-sharing services are seen as a potential innovative way to improve the environmental situation in cities. Despite AMS are present in the market, there are challenges for people to adopt this type of services and change their behavior towards more sustainable urban living. To address this challenge, it is important to identify What are the factors influencing an individual decision to adopt AMS innovation and change user behavior. There is no coherent framework to study innovation adoption in transportation related to AMS. This study aims at providing a fresh perspective on innovation adoption and behavior change in modern urban transportation with the focus on AMS. This includes synthesis of three theories to introduce a framework that presents factors which can be taken into account when developing an intervention in the transportation sector. The factors of the proposed framework are tested and validated in this research through multiple case studies in the form of interviews with transportation experts (N=8) in Finland. Findings demonstrate an importance of understanding influential elements in an ecosystem that affect innovation adoption. These elements include involvement of every stakeholder in addressing individual behavior change, particularly, peers that individuals trust, and government representation. As a part of this study, the innovation characteristics are specifically emphasized as factors with an important role in AMS innovation adoption decision. For instance, minimum complexity, opportunity to try an innovation before making the adoption decision, competitive pricing, and compatibility to prior experiences were listed as the most influential characteristics. The results of the study serve as a basis for the research on the development of interventions in the transportation sector with a particular focus on alternative mobility solutions (AMS)

In Silico identification of pathogenic strains of Cronobacter from Biochemical data reveals association of inositol fermentation with pathogenicity

<p>Abstract</p> <p>Background</p> <p><it>Cronobacter</it>, formerly known as <it>Enterobacter sakazakii</it>, is a food-borne pathogen known to cause neonatal meningitis, septicaemia and death. Current diagnostic tests for identification of <it>Cronobacter </it>do not differentiate between species, necessitating time consuming 16S rDNA gene sequencing or multilocus sequence typing (MLST). The organism is ubiquitous, being found in the environment and in a wide range of foods, although there is variation in pathogenicity between <it>Cronobacter </it>isolates and between species. Therefore to be able to differentiate between the pathogenic and non-pathogenic strains is of interest to the food industry and regulators.</p> <p>Results</p> <p>Here we report the use of Expectation Maximization clustering to categorise 98 strains of <it>Cronobacter </it>as pathogenic or non-pathogenic based on biochemical test results from standard diagnostic test kits. Pathogenicity of a strain was postulated on the basis of either pathogenic symptoms associated with strain source or corresponding MLST sequence types, allowing the clusters to be labelled as containing either pathogenic or non-pathogenic strains. The resulting clusters gave good differentiation of strains into pathogenic and non-pathogenic groups, corresponding well to isolate source and MLST sequence type. The results also revealed a potential association between pathogenicity and inositol fermentation. An investigation of the genomes of <it>Cronobacter sakazakii </it>and <it>C. turicensis </it>revealed the gene for inositol monophosphatase is associated with putative virulence factors in pathogenic strains of <it>Cronobacter</it>.</p> <p>Conclusions</p> <p>We demonstrated a computational approach allowing existing diagnostic kits to be used to identify pathogenic strains of <it>Cronobacter</it>. The resulting clusters correlated well with MLST sequence types and revealed new information about the pathogenicity of <it>Cronobacter </it>species.</p

Towards In Silico identification of genes contributing to similarity of patients’ multi-omics profiles: a case study of acute myeloid leukemia

We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients' similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of three similarity networks, based on mRNA expression of immune genes, miRNA expression and DNA methylation data, using SNF_v2.1 software. For each cluster, we rank multi-omics features, ensuring the best separation between clusters, and select the top-ranked features that preserve clustering. To find genes targeted by DNA methylation and miRNAs found in the top-ranked features, we use chromosome-conformation capture data and miRNet 2.0 software, respectively. To identify informative genes, these combined sets of target genes are analyzed in terms of their enrichment in somatic/germline mutations, GO biological processes/pathways terms and known sets of genes considered to be important in relation to a given disease, as recorded in the Molecular Signature Database from GSEA. The protein-protein interaction (PPI) networks were analyzed to identify genes that are hubs of PPI networks. We used data recorded in The Cancer Genome Atlas for patients with acute myeloid leukemia to demonstrate our approach, and discuss our findings in the context of results in the literature