Systems modelling predicts chronic inflammation and genomic instability prevent effective mitochondrial regulation during biological ageing

The regulation of mitochondrial turnover under conditions of stress occurs partly through the AMPK-NAD+-PGC1α-SIRT1 signalling pathway. This pathway can be affected by both genomic instability and chronic inflammation since these will result in an increased rate of NAD+ degradation through PARP1 and CD38 respectively. In this work we develop a computational model of this signalling pathway, calibrating and validating it against experimental data. The computational model is used to study mitochondrial turnover under conditions of stress and how it is affected by genomic instability, chronic inflammation and biological ageing in general. We report that the AMPK-NAD+-PGC1α-SIRT1 signalling pathway becomes less responsive with age and that this can prime for the accumulation of dysfunctional mitochondria

Versatile Graphene-Based Platform for Robust Nanobiohybrid Interfaces

Technologically useful and robust graphene-based interfaces for devices require the introduction of highly selective, stable, and covalently bonded functionalities on the graphene surface, whilst essentially retaining the electronic properties of the pristine layer. This work demonstrates that highly controlled, ultrahigh vacuum covalent chemical functionalization of graphene sheets with a thiol-terminated molecule provides a robust and tunable platform for the development of hybrid nanostructures in different environments. We employ this facile strategy to covalently couple two representative systems of broad interest: metal nanoparticles, via S-metal bonds, and thiol-modified DNA aptamers, via disulfide bridges. Both systems, which have been characterized by a multi-technique approach, remain firmly anchored to the graphene surface even after several washing cycles. Atomic force microscopy images demonstrate that the conjugated aptamer retains the functionality required to recognize a target protein. This methodology opens a new route to the integration of high-quality graphene layers into diverse technological platforms, including plasmonics, optoelectronics, or biosensing. With respect to the latter, the viability of a thiol-functionalized chemical vapor deposition graphene-based solution-gated field-effect transistor array was assessed

Deciphering the connectivity structure of biological networks using MixNet

<p>Abstract</p> <p>Background</p> <p>As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles.</p> <p>Results</p> <p>We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the <it>E. coli </it>transcriptional regulatory network, the macaque cortex network, a foodweb network and the <it>Buchnera aphidicola </it>metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering.</p> <p>Conclusion</p> <p>We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.</p

Analysis of Combinatorial Regulation: Scaling of Partnerships between Regulators with the Number of Governed Targets

Through combinatorial regulation, regulators partner with each other to control common targets and this allows a small number of regulators to govern many targets. One interesting question is that given this combinatorial regulation, how does the number of regulators scale with the number of targets? Here, we address this question by building and analyzing co-regulation (co-transcription and co-phosphorylation) networks that describe partnerships between regulators controlling common genes. We carry out analyses across five diverse species: Escherichia coli to human. These reveal many properties of partnership networks, such as the absence of a classical power-law degree distribution despite the existence of nodes with many partners. We also find that the number of co-regulatory partnerships follows an exponential saturation curve in relation to the number of targets. (For E. coli and Bacillus subtilis, only the beginning linear part of this curve is evident due to arrangement of genes into operons.) To gain intuition into the saturation process, we relate the biological regulation to more commonplace social contexts where a small number of individuals can form an intricate web of connections on the internet. Indeed, we find that the size of partnership networks saturates even as the complexity of their output increases. We also present a variety of models to account for the saturation phenomenon. In particular, we develop a simple analytical model to show how new partnerships are acquired with an increasing number of target genes; with certain assumptions, it reproduces the observed saturation. Then, we build a more general simulation of network growth and find agreement with a wide range of real networks. Finally, we perform various down-sampling calculations on the observed data to illustrate the robustness of our conclusions

Statistical regularities in the rank-citation profile of scientists

Recent science of science research shows that scientific impact measures for journals and individual articles have quantifiable regularities across both time and discipline. However, little is known about the scientific impact distribution at the scale of an individual scientist. We analyze the aggregate production and impact using the rank-citation profile ci(r) of 200 distinguished professors and 100 assistant professors. For the entire range of paper rank r, we fit each ci(r) to a common distribution function. Since two scientists with equivalent Hirsch h-index can have significantly different ci(r) profiles, our results demonstrate the utility of the βi scaling parameter in conjunction with hi for quantifying individual publication impact. We show that the total number of citations Ci tallied from a scientist's Ni papers scales as . Such statistical regularities in the input-output patterns of scientists can be used as benchmarks for theoretical models of career progress

A New Measure of Centrality for Brain Networks

Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network

Systems modelling ageing: from single senescent cells to simple multi-cellular models

Systems modelling has been successfully used to investigate several key molecular mechanisms of ageing. Modelling frameworks to allow integration of models and methods to enhance confidence in models are now well established. In this article, we discuss these issues and work through the process of building an integrated model for cellular senescence as a single cell and in a simple tissue context

Neural interfaces based on flexible graphene transistors: A new tool for electrophysiology

The use of graphene transistors for transducing neural activity has demonstrated the potential to extend the spatiotemporal resolution of electrophysiological methods to lower frequencies, providing a new tool to understand the role of the infra-slow activity