The topology of the proteome map revealed by recent large-scale hybridization methods has shown that the distribution of protein-protein interactions is highly heterogeneous, with many proteins having few links while a few of them are heavily connected. This particular topology is shared by other cellular networks, such as metabolic pathways, and it has been suggested to be responsible for the high mutational homeostasis displayed by the genome of some organisms. In this paper we explore a recent model of proteome evolution that has been shown to reproduce many of the features displayed by its real counterparts. The model is based on gene duplication plus re-wiring of the newly created genes. The statistical features displayed by the proteome of well-known organisms are reproduced, suggesting that the overall topology of the protein maps naturally emerges from the two leading mechanisms considered by the model. I.

Complex networks are characterized by highly heterogeneous distributions of links, often pervading the presence of key properties such as robustness under node removal. Several correlation measures have been defined in order to characterize the structure of these nets. Here we show that mutual information, noise and joint entropies can be properly defined on a static graph. These measures are computed for a number of real networks and analytically estimated for some simple standard models. It is shown that real networks are clustered in a well-defined domain of the entropy/noise space. By using simulated annealing optimization, it is shown that optimally heterogeneous nets actually cluster around the same narrow domain, suggesting that strong constraints actually operate on the possible universe of complex networks. The evolutionary implications are discussed.

The response of biological cells to environmental change is coordinated by protein based signaling networks. These networks are to be found in both prokaryotes and eukaryotes. In eukaryotes the signaling networks can be highly complex, some networks comprising of sixty or more proteins. The fundamental motif that has been found in all signaling networks is the protein phosphorylation/dephosphorylation cycle- the cascade cycle. At this time, the computational function of many of the signaling networks is poorly understood. However, it is clear that it is possible to construct a huge variety of control and computational circuits, both analog and digital from combinations of the cascade cycle. In this review we will summarize the great versatility of the simple cascade cycle as a computational unit and towards the end give two examples, one prokaryotic chemotaxis circuit and the other, the eukaryotic MAPK

Abstract—We exploit recent advances in analysis of graph topology to better understand software evolution, and to construct predictors that facilitate software development and maintenance. Managing an evolving, collaborative software system is a complex and expensive process, which still cannot ensure software reliability. Emerging techniques in graph mining have revolutionized the modeling of many complex systems and processes. We show how we can use a graph-based characterization of a software system to capture its evolution and facilitate development, by helping us estimate bug severity, prioritize refactoring efforts, and predict defect-prone releases. Our work consists of three main thrusts. First, we construct graphs that capture software structure at two different levels: (a) the product, i.e., source code and module level, and (b) the process, i.e., developer collaboration level. We identify a set of graph metrics that capture interesting properties of these graphs. Second, we study the evolution of eleven open source programs, including Firefox, Eclipse, MySQL, over the lifespan of the programs, typically a decade or more. Third, we show how our graph metrics can be used to construct predictors for bug severity, high-maintenance software parts, and failureprone releases. Our work strongly suggests that using graph topology analysis concepts can open many actionable avenues in software engineering research and practice. Keywords-Graph science; software evolution; software quality; defect prediction; productivity metrics; empirical studies I.

Abstract—Software evolution and update play a vital role in software engineering. It has many advantages, such as improving the efficiency of programming, reducing the cost of maintenance and promoting the development of software systems. This paper further analyzes the evolution and update processes of three typical kinds of real-world object-oriented software systems by using the tools of complex networks. It discovers some underlying dynamical evolution characteristics and rules of the object- oriented software systems. These results are very useful for the design and development of the object-oriented software systems. I.

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Abstract — Ad-hoc sensor networks comprising large numbers of randomly deployed wireless sensors have recently been an active focus of investigation. These networks require selforganized configuration after deployment, and ad-hoc heuristic methods for such configuration have been proposed with regard to many aspects of the networks ’ performance. However, systematic approaches for such configuration remain elusive. In this paper, we present a preliminary attempt towards such a systematic approach using evolutionary algorithms and reverse engineering. In particular, we focus on the problem of obtaining heterogeneous networks that optimize global functional properties through local adaptive rules. Almost all work on adhoc sensor network has so far involved homogeneous networks where all nodes transmit with the same power level, creating a symmetric connectivity. It is possible to construct heterogeneous networks by allowing nodes to transmit at different power levels, and such networks are known to provide improvements in network lifetime, power efficiency, routing, etc. However, such networks are difficult to build mainly because the optimal power level for each node depends on the node location and spatial context, which are not known before deployment. A few heuristic schemes focused on improving power consumption have been proposed in the literature, but the issue has not been investigated sufficiently at a general level. In this paper, we present a new and improved heuristic developed using a reverse engineered approach. A genetic algorithm is used to generate a set of heterogeneous sensor networks that are characterized by low short paths and minimal congestion. Analysis of this optimal network set yields rules that form the basis for a local heuristic. We show that networks adapted using this heuristic produce significant improvement over the homogeneous case. More importantly, the results validate the utility of the proposed approach that can be used in other self-organizing systems. I.

The response of biological cells to environmental change is coordinated by protein-based signaling networks. These networks are to be found in both prokaryotes and eukaryotes. In eukaryotes, the signaling networks can be highly complex, some networks comprising of 60 or more proteins. The fundamental motif that has been found in all signaling networks is the protein phosphorylation/dephosphorylation cycle—the cascade cycle. At this time, the computational function of many of the signaling networks is poorly understood. However, it is clear that it is possible to construct a huge variety of control and computational circuits, both analog and digital from combinations of the cascade cycle. In this review, we will summarize the great versatility of the simple cascade cycle as a computational unit and towards the end give two examples, one prokaryotic chemotaxis circuit and the other, the eukaryotic MAPK cascade.

The network perspective will help, but is comorbidity the question? Citation for published version: Johnson, W & Penke, L 2010, 'The network perspective will help, but is comorbidity the question?'

According to the traditional nomological-deductive methodology of physics and chemistry [Hempel and Oppenheim, 1948], explaining a phenomenon means sub-suming it under a law. Logic becomes then the glue of explanation and laws the primary explainers. Thus, the scientific study of a system would consist in the