Network proteomics of human dermal wound healing.

Objective: The healing of wounds is critical in protecting the human body against environmental factors. The mechanisms involving protein expression during this complex physiological process have not been fully elucidated.

Approach: Here, we use reverse-phase protein microarrays (RPPA) involving 94 phosphoproteins to study tissue samples from tubes implanted in healing dermal wounds in seven human subjects tracked over two weeks.

Network analysis of human post-mortem microarrays reveals novel genes, microRNAs, and mechanistic scenarios of potential importance in fighting huntington's disease.

Huntington's disease is a progressive neurodegenerative disorder characterized by motor disturbances, cognitive decline, and neuropsychiatric symptoms. In this study, we utilized network-based analysis in an attempt to explore and understand the underlying molecular mechanism and to identify critical molecular players of this disease condition. Using human post-mortem microarrays from three brain regions (cerebellum, frontal cortex and caudate nucleus) we selected in a four-step procedure a seed set of highly modulated genes.

Network Topology Analysis of Post-Mortem Brain Microarrays Identifies More Alzheimer's Related Genes and MicroRNAs and Points to Novel Routes for Fighting with the Disease.

Network-based approaches are powerful and beneficial tools to study complex systems in their entirety, elucidating the essential factors that turn the multitude of individual elements into a functional system. In this study we used critical network topology descriptors and guilt-by-association rule to explore and understand the significant molecular players, drug targets and underlying biological mechanisms of Alzheimer's disease. Analyzing two post-mortem brain gene microarrays (GSE4757 and GSE28146) with Pathway Studio software package we constructed and analyzed a set of protein-protein interaction, as well as miRNA-target networks.

Trends in information theory-based chemical structure codification.

This report offers a chronological review of the most relevant applications of information theory in the codification of chemical structure information, through the so-called information indices. Basically, these are derived from the analysis of the statistical patterns of molecular structure representations, which include primitive global chemical formulae, chemical graphs, or matrix representations. Finally, new approaches that attempt to go "back to the roots" of information theory, in order to integrate other information-theoretic measures in chemical structure coding are discussed.

A Network Approach to Wound Healing.

Objective: The wound healing process is well-understood on the cellular and tissue level; however, its complex molecular mechanisms are not yet uncovered in their entirety. Viewing wounds as perturbed molecular networks provides the tools for analyzing and optimizing the healing process. It helps to answer specific questions that lead to better understanding of the complexity of the process.

A network view on Parkinson's disease.

Network-based systems biology tools including Pathway Studio 9.0 were used to identify Parkinson's disease (PD) critical molecular players, drug targets, and underlying biological processes. Utilizing several microarray gene expression datasets, biomolecular networks such as direct interaction, shortest path, and microRNA regulatory networks were constructed and analyzed for the disease conditions.

Genome-wide essential gene identification in Streptococcus sanguinis.

A clear perception of gene essentiality in bacterial pathogens is pivotal for identifying drug targets to combat emergence of new pathogens and antibiotic-resistant bacteria, for synthetic biology, and for understanding the origins of life. We have constructed a comprehensive set of deletion mutants and systematically identified a clearly defined set of essential genes for Streptococcus sanguinis. Our results were confirmed by growing S.

A survey of current software for network analysis in molecular biology.

Software for network motifs and modules is briefly reviewed, along with programs for network comparison. The three major software packages for network analysis, CYTOSCAPE, INGENUITY and PATHWAY STUDIO, and their associated databases, are compared in detail. A comparative test evaluated how these software packages perform the search for key terms and the creation of network from those terms and from experimental expression data.

Cellular automata modeling of FASL-initiated apoptosis.

Two strategies for fighting cancer by modulating FASL-induced apoptosis were modeled by 2D-cellular automata. Our models predict that cancer cells can be killed by maximizing the apoptosis via joint suppression of FLIP and IAP inhibitors by siRNA and SMAC proteins, respectively. It was also predicted that the presumed feedback loop CASP3-->CASP9-->|IAP in the intrinsic pathway accelerates the apoptosis, but does not change significantly the concentration of DFF40, the protein that decomposes DNA.

Evolution of metabolic network organization.

Background: Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints.

Results: We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell.

Toward a classification of isodynamic feed-forward motifs.

A preceding study analysed how the topology of network motifs affects the overall rate of the underlying biochemical processes. Surprisingly, it was shown that topologically non-isomorphic motifs can still be isodynamic in the sense that they exhibit the exact same performance rate. Because of the high prevalence of feed-forward functional modules in biological networks, one may hypothesize that evolution tends to favour motifs with faster dynamics.

An in silico approach to the analysis of acute wound healing.

The complex interactions that characterize acute wound healing have stymied the development of effective therapeutic modalities. The use of computational models holds the promise to improve our basic approach to understanding the process. By modifying an existing ordinary differential equation model of systemic inflammation to simulate local wound healing, we expect to improve the understanding of the underlying complexities of wound healing and thus allow for the development of novel, targeted therapeutic strategies.

Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity.

Background: Identification of genes that modulate longevity is a major focus of aging-related research and an area of intense public interest. In addition to facilitating an improved understanding of the basic mechanisms of aging, such genes represent potential targets for therapeutic intervention in multiple age-associated diseases, including cancer, heart disease, diabetes, and neurodegenerative disorders. To date, however, targeted efforts at identifying longevity-associated genes have been limited by a lack of predictive power, and useful algorithms for candidate gene-identification have also been lacking.

Phylogenetic distances are encoded in networks of interacting pathways.

Motivation: Although metabolic reactions are unquestionably shaped by evolutionary processes, the degree to which the overall structure and complexity of their interconnections are linked to the phylogeny of species has not been evaluated in depth. Here, we apply an original metabolome representation, termed Network of Interacting Pathways or NIP, with a combination of graph theoretical and machine learning strategies, to address this question. NIPs compress the information of the metabolic network exhibited by a species into much smaller networks of overlapping metabolic pathways, where nodes are pathways and links are the metabolites they exchange.

Cellular automata simulation of topological effects on the dynamics of feed-forward motifs.

Background: Feed-forward motifs are important functional modules in biological and other complex networks. The functionality of feed-forward motifs and other network motifs is largely dictated by the connectivity of the individual network components. While studies on the dynamics of motifs and networks are usually devoted to the temporal or spatial description of processes, this study focuses on the relationship between the specific architecture and the overall rate of the processes of the feed-forward family of motifs, including double and triple feed-forward loops.

Predicting aging/longevity-related genes in the nematode Caenorhabditis elegans.

We present a novel mathematical/computational strategy for predicting genes/proteins associated with aging/longevity. The novelty of our method arises from the topological analysis of an organismal longevity gene/protein network (LGPN), which extends the existing cellular networks. The LGPN nodes represent both genes and corresponding proteins.

From molecular to biological structure and back.

A comparative analysis of the topological structure of molecules and molecular biology networks revealed both similarity and differences in the methods used, as well as in the essential features of the two types of systems. Molecular graphs are static and, due to the limitations in atomic valence, show neither power distribution of vertex degrees nor "small-world" properties, which are typical for dynamic evolutionary networks. Areas of mutual benefits from an exchange of methods and ideas are outlined for the two fields.

Impaired wound healing.

Nonhealing wounds represent a significant cause of morbidity and mortality for a large portion of the population. One of the underlying mechanisms responsible for the failure of chronic wounds to heal is an out-of-control inflammatory response that is self-sustaining. Underappreciation of the inherent complexity of the healing wound has led to the failure of monotherapies, with no significant reduction in wound healing times.

Modeling biochemical networks: a cellular-automata approach.

The potential of the cellular-automata (CA) method for modeling biological networks is demonstrated for the mitogen-activated protein kinase (MAPK) signaling cascade. The models derived reproduced the high signal amplification through the cascade and the deviation of the cascade enzymes from the Michaelis-Menten kinetics, evidencing cooperativity effects. The patterns of pathway change upon varying substrate concentrations and enzyme efficiencies were identified and used to show the ways for controlling pathway processes.

Complexity analysis of yeast proteome network.

Topological and compositional complexity of protein-protein networks is assessed in a variety of ways making use of graph theory and information theory. The methodology used is borrowed from mathematical chemistry and includes complexity descriptors such as substructure count, overall connectivity, walk count, and information on various vertex distributions. The approach is applied to the (incomplete) proteome of Saccharomyces cerevisiae containing 232 protein complexes of a total of 1,440 proteins.

Novel ZE-isomerism descriptors derived from molecular topology and their application to QSAR analysis.

We introduce several series of novel ZE-isomerism descriptors derived directly from two-dimensional molecular topology. These descriptors make use of a quantity named ZE-isomerism correction, which is added to the vertex degrees of atoms connected by double bonds in Z and E configurations. This approach is similar to the one described previously for topological chirality descriptors (Golbraikh, A.