GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs

We present a prototype of a software tool for exploration of multiple combinatorial optimisation problems in large real-world and synthetic complex networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial Explorer), provides a unified framework for scalable computation and presentation of high-quality suboptimal solutions and bounds for a number of widely studied combinatorial optimisation problems. Efficient representation and applicability to large-scale graphs and complex networks are particularly considered in its design. The problems currently supported include maximum clique, graph colouring, maximum independent set, minimum vertex clique covering, minimum dominating set, as well as the longest simple cycle problem. Suboptimal solutions and intervals for optimal objective values are estimated using scalable heuristics. The tool is designed with extensibility in mind, with the view of further problems and both new fast and high-performance heuristics to be added in the future. GraphCombEx has already been successfully used as a support tool in a number of recent research studies using combinatorial optimisation to analyse complex networks, indicating its promise as a research software tool

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Fingerprint for Network Topologies

A network's topology information can be given as an adjacency matrix. The bitmap of sorted adjacency matrix(BOSAM) is a network visualisation tool which can emphasise different network structures by just looking at reordered adjacent matrixes. A BOSAM picture resembles the shape of a flower and is characterised by a series of 'leaves'. Here we show and mathematically prove that for most networks, there is a self-similar relation between the envelope of the BOSAM leaves. This self-similar property allows us to use a single envelope to predict all other envelopes and therefore reconstruct the outline of a network's BOSAM picture. We analogise the BOSAM envelope to human's fingerprint as they share a number of common features, e.g. both are simple, easy to obtain, and strongly characteristic encoding essential information for identification.Comment: 12papes, 3 figures, in pres

Modelling fungal colonies and communities:challenges and opportunities

This contribution, based on a Special Interest Group session held during IMC9, focuses on physiological based models of filamentous fungal colony growth and interactions. Fungi are known to be an important component of ecosystems, in terms of colony dynamics and interactions within and between trophic levels. We outline some of the essential components necessary to develop a fungal ecology: a mechanistic model of fungal colony growth and interactions, where observed behaviour can be linked to underlying function; a model of how fungi can cooperate at larger scales; and novel techniques for both exploring quantitatively the scales at which fungi operate; and addressing the computational challenges arising from this highly detailed quantification. We also propose a novel application area for fungi which may provide alternate routes for supporting scientific study of colony behaviour. This synthesis offers new potential to explore fungal community dynamics and the impact on ecosystem functioning

Mega-City-Regions: on Awareness and Value Chain Approach

Mega-City-Regions (MCR) as a new large-scale urban phenomenon have been gaining attention recently: In research, empirical studies address their functional consistency, and spatial planning policies underline the strategic role of MCRs for territorial competition of a country. But increasingly a tension between the functional logic of knowledge-intensive business activities and the territorial and normative approaches of public bodies begin to emerge. Typical conflicts of spatial development in MCRs occur for example when globally motivated investment decisions hit upon the local needs. This paper proposes an integrated view that can help to bridge the gap between the growing factual knowledge about MCRs and the still weak ability to use this knowledge for local and regional development and spatial planning purposes. The proposed integration draws on the one hand from the corporate-based value chain approach: The interaction of analysis of spatio-economic development, its adequate visualization and focussed communication towards stakeholders is apt to bring about the initiating momentum for beneficial spatial development. In the context of a diffuse perception of MCRs – whose mere size surpasses our common notions of space – analysis, visualization and communication as methodological components in the spatial planning process add value to sustainable spatial development. The process starts with creating awareness for the often invisible and complex functions, qualities and identities of the MCR spatial scale. New strategies of visualization and communication are needed to improve insight and motivation of the actors involved. On the other hand this value chain approach has to be adapted to the varying vertical levels of public bodies with their numerous policies. Thus, “multi-level-governance†is to be conceived as a concept to close the gap between the territorial and the functional logic of spatial development. The paper will study this dual approach with the case of the announced expansion of the international airport in Munich. This complex multi-level-governance process experiments with a consensus-oriented dialogue platform – the so called “neighbourhood conference†(NC) – bringing together actors with divers responsibilities and objectives. The NC sits at the interface of global and local objectives that are transformed on the spatial scale of the MCR of Munich. The paper concludes with recommendations for using the above described spatial value chain approach for more efficient multi-level-governance.

Planar growth generates scale free networks

In this paper we introduce a model of spatial network growth in which nodes are placed at randomly selected locations on a unit square in $\mathbb{R}^2$, forming new connections to old nodes subject to the constraint that edges do not cross. The resulting network has a power law degree distribution, high clustering and the small world property. We argue that these characteristics are a consequence of the two defining features of the network formation procedure; growth and planarity conservation. We demonstrate that the model can be understood as a variant of random Apollonian growth and further propose a one parameter family of models with the Random Apollonian Network and the Deterministic Apollonian Network as extreme cases and our model as a midpoint between them. We then relax the planarity constraint by allowing edge crossings with some probability and find a smooth crossover from power law to exponential degree distributions when this probability is increased.Comment: 27 pages, 9 figure