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PubMed2002

Specificity and stability in topology of protein networks.

Molecular networks guide the biochemistry of a living cell on multiple levels: Its metabolic and signaling pathways are shaped by the network of interacting proteins, whose production, in turn, is controlled by the genetic regulatory network. To address topological properties of these two networks, we quantified correlations between connectivities of interacting nodes and compared them to a null model of a network, in which all links were randomly rewired. We found that for both interaction and regulatory networks, links between highly connected proteins are systematically suppressed, whereas those between a highly connected and low-connected pairs of proteins are favored. This effect decreases the likelihood of cross talk between different functional modules of the cell and increases the overall robustness of a network by localizing effects of deleterious perturbations.

Cell Nucleus↗
PubMed2006

Strategies for dealing with incomplete information in the modeling of molecular interaction networks.

Modelers of molecular interaction networks encounter the paradoxical situation that while large amounts of data are available, these are often insufficient for the formulation and analysis of mathematical models describing the network dynamics. In particular, information on the reaction mechanisms and numerical values of kinetic parameters are usually not available for all but a few well-studied model systems. In this article we review two strategies that have been proposed for dealing with incomplete information in the study of molecular interaction networks: parameter sensitivity analysis and model simplification. These strategies are based on the biologically justified intuition that essential properties of the system dynamics are robust against moderate changes in the value of kinetic parameters or even in the rate laws describing the interactions. Although advanced measurement techniques can be expected to relieve the problem of incomplete information to some extent, the strategies discussed in this article will retain their interest as tools providing an initial characterization of essential properties of the network dynamics.

Animals↗
PubMed2006

Modeling cellular machinery through biological network comparison.

Molecular networks represent the backbone of molecular activity within the cell. Recent studies have taken a comparative approach toward interpreting these networks, contrasting networks of different species and molecular types, and under varying conditions. In this review, we survey the field of comparative biological network analysis and describe its applications to elucidate cellular machinery and to predict protein function and interaction. We highlight the open problems in the field as well as propose some initial mathematical formulations for addressing them. Many of the methodological and conceptual advances that were important for sequence comparison will likely also be important at the network level, including improved search algorithms, techniques for multiple alignment, evolutionary models for similarity scoring and better integration with public databases.

Algorithms↗
PubMed2005

Molecular chaperones as regulatory elements of cellular networks.

Molecular chaperones help hundreds of signaling molecules to keep their activation-competent state, and regulate various signaling processes ranging from signaling at the plasma membrane to transcription. Besides these specific regulatory roles, recent studies have revealed that chaperones act as genetic buffers stabilizing the phenotypes of various cells and organisms. This may be related to their low affinity for the proteins they interact with, which means that they represent weak links in protein networks. Chaperones may uncouple protein, signaling, membrane, organelle and transcriptional networks during stress, which gives the cell additional protection. The same networks are preferentially remodeled in various diseases and aging, which may help us to design novel therapeutic and anti-aging strategies.

Animals↗
PubMed2006

Structure and function of negative feedback loops at the interface of genetic and metabolic networks.

The molecular network in an organism consists of transcription/translation regulation, protein-protein interactions/modifications and a metabolic network, together forming a system that allows the cell to respond sensibly to the multiple signal molecules that exist in its environment. A key part of this overall system of molecular regulation is therefore the interface between the genetic and the metabolic network. A motif that occurs very often at this interface is a negative feedback loop used to regulate the level of the signal molecules. In this work we use mathematical models to investigate the steady state and dynamical behaviour of different negative feedback loops. We show, in particular, that feedback loops where the signal molecule does not cause the dissociation of the transcription factor from the DNA respond faster than loops where the molecule acts by sequestering transcription factors off the DNA. We use three examples, the bet, mer and lac systems in Escherichia coli, to illustrate the behaviour of such feedback loops.

Down-Regulation↗
PubMed2004

Back to the biology in systems biology: what can we learn from biomolecular networks?

Genome-scale molecular networks, including protein interaction and gene regulatory networks, have taken centre stage in the investigation of the burgeoning disciplines of systems biology and biocomplexity. What do networks tell us? Some see in networks simply the comprehensive, detailed description of all cellular pathways, others seek in networks simple, higher-order qualities that emerge from the collective action of the individual pathways. This paper discusses networks from an encompassing category of thinking that will hopefully help readers to bridge the gap between these polarised viewpoints. Systems biology so far has emphasised the characterisation of large pathway maps. Now one has to ask: where is the actual biology in 'systems biology'? As structures midway between genome and phenome, and by serving as an 'extended genotype' or an 'elementary phenotype', molecular networks open a new window to the study of evolution and gene function in complex living systems. For the study of evolution, features in network topology offer a novel starting point for addressing the old debate on the relative contributions of natural selection versus intrinsic constraints to a particular trait. To study the function of genes, it is necessary not only to see them in the context of gene networks, but also to reach beyond describing network topology and to embrace the global dynamics of networks that will reveal higher-order, collective behaviour of the interacting genes. This will pave the way to understanding how the complexity of genome-wide molecular networks collapses to produce a robust whole-cell behaviour that manifests as tightly-regulated switching between distinct cell fates - the basis for multicellular life.

Animals↗
PubMed2004

Molecular interaction maps--a diagrammatic graphical language for bioregulatory networks.

Molecular interaction maps (MIMs) use a clear, accurate, and versatile graphical language to depict complex biological processes. Here, we discuss the main features of the MIM language and its potential uses. MIMs can be used as database resources and simulation guides, and can serve to generate new hypotheses regarding the roles of specific molecules in the bioregulatory networks that control progression through the cell cycle, differentiation, and cell death.

Animals↗
PubMedDate not supplied

Novel alignment method of small molecules using the Hopfield Neural Network.

Molecular alignment is an important step in three-dimensional quantitative structure-activity relationship (3D-QSAR) such as comparative molecular field analysis (CoMFA). A reasonable molecular alignment is necessary for building a 3D-QSAR model. In this paper, a novel method for molecular alignment using the Hopfield Neural Network (HNN) is introduced. Four kinds of chemical properties are assigned to each atom of a molecule. Then, those properties between two molecules correspond to each other using HNN. To validate our method, HNN was applied to 12 pairs of enzyme inhibitors cited from the Protein Data Bank (PDB). As a result, our method could successfully reproduce the real molecular alignments obtained from X-ray crystallography.

Crystallography, X-Ray↗
PubMed2005

Molecular origin of shear thickening in transient polymer networks: a molecular dynamics study.

This paper proposes a simple model of transient networks of telechelic associating polymers for molecular simulations and reports the main results obtained by molecular dynamics on the rheological properties of the transient networks. The steady shear viscosity obtained by the non-equilibrium molecular dynamics simulation exhibits shear thickening at moderate shear rates and shear thinning at larger shear rates. The behavior is similar to that observed in experiments of telechelic associating polymers. By analyzing the distribution function of the end-to-end vector of bridge chains as a function of the shear rate, we find that shear thickening is mainly caused by the stress from the bridge chains highly stretched by shear flow. We also find that fracture of the transient network occurs in the shear-thinning regime at high shear rates.

Journal Article↗
PubMed2006

A method for the generation of standardized qualitative dynamical systems of regulatory networks.

BACKGROUND: Modeling of molecular networks is necessary to understand their dynamical properties. While a wealth of information on molecular connectivity is available, there are still relatively few data regarding the precise stoichiometry and kinetics of the biochemical reactions underlying most molecular networks. This imbalance has limited the development of dynamical models of biological networks to a small number of well-characterized systems. To overcome this problem, we wanted to develop a methodology that would systematically create dynamical models of regulatory networks where the flow of information is known but the biochemical reactions are not. There are already diverse methodologies for modeling regulatory networks, but we aimed to create a method that could be completely standardized, i.e. independent of the network under study, so as to use it systematically. RESULTS: We developed a set of equations that can be used to translate the graph of any regulatory network into a continuous dynamical system. Furthermore, it is also possible to locate its stable steady states. The method is based on the construction of two dynamical systems for a given network, one discrete and one continuous. The stable steady states of the discrete system can be found analytically, so they are used to locate the stable steady states of the continuous system numerically. To provide an example of the applicability of the method, we used it to model the regulatory network controlling T helper cell differentiation. CONCLUSION: The proposed equations have a form that permit any regulatory network to be translated into a continuous dynamical system, and also find its steady stable states. We showed that by applying the method to the T helper regulatory network it is possible to find its known states of activation, which correspond the molecular profiles observed in the precursor and effector cell types.

Cell Differentiation↗
PubMed2006

Molecular epidemiology of Neisseria gonorrhoeae in Amsterdam, The Netherlands, shows distinct heterosexual and homosexual networks.

Molecular typing, added to epidemiological data, can better identify transmission patterns of gonorrhea in Western countries, where the incidence has recently been rising. From September 2002 to September 2003, patients with a laboratory-confirmed diagnosis of gonorrhea at the Clinic for Sexually Transmitted Infections in Amsterdam, The Netherlands, were subjected to a questionnaire pertaining to sexual risk behavior and sexual partners in the 6 months prior to the diagnosis. The Neisseria gonorrhoeae isolates were all genotyped using PCR-restriction fragment length polymorphism of the porin and opacity genes. All patients with a completed questionnaire and genotyped isolates were included in the study. We obtained 885 N. gonorrhoeae isolates from 696 patients that revealed 88 clusters and 46 unique genotypes. Patients infected at multiple anatomical sites with one or more strains and patients infected several times during the study period were shown to pursue high-risk sexual behavior and were considered core groups. There were 11 clusters of > or =20 patients; in seven clusters, 81% to 100% of patients were men who have sex with men (MSM), three clusters contained 87 to 100% heterosexual men and women, and one cluster was formed by equal proportions of MSM and heterosexual male and female patients. However, the various clusters differed in characteristics such as types of coinfections, numbers of sexual partners, Internet use to seek sexual partners, and locations of sexual encounters. Molecular epidemiology of gonococcal isolates in Amsterdam revealed core groups and clusters of MSM and heterosexual patients that probably indicate distinct transmission networks.

Adult↗
PubMed1993

Neural networks for molecular sequence classification.

A neural network classification method has been developed as an alternative approach to the search/organization problem of large molecular databases. Two artificial neural systems have been implemented on a Cray supercomputer for rapid protein/nucleic acid sequence classifications. The neural networks used are three-layered, feed-forward networks that employ back-propagation learning algorithm. The molecular sequences are encoded into neural input vectors by applying an n-gram hashing method or a SVD (singular value decomposition) method. Once trained with known sequences in the molecular databases, the neural system becomes an associative memory capable of classifying unknown sequences based on the class information embedded in its neural interconnections. The protein system, which classifies proteins into PIR (Protein Identification Resource) superfamilies, showed a 82% to a close to 100% sensitivity at a speed that is about an order of magnitude faster than other search methods. The pilot nucleic acid system, which classifies ribosomal RNA sequences according to phylogenetic groups, has achieved a 100% classification accuracy. The system could be used to reduce the database search time and help organize the molecular sequence databases. The tool is generally applicable to any databases that are organized according to family relationships.

Base Sequence↗
PubMed1997

Artificial neural networks for molecular sequence analysis.

Artificial neural networks provide a unique computing architecture whose potential has attracted interest from researchers across different disciplines. As a technique for computational analysis, neural network technology is very well suited for the analysis of molecular sequence data. It has been applied successfully to a variety of problems, ranging from gene identification, to protein structure prediction and sequence classification. This article provides an overview of major neural network paradigms, discusses design issues, and reviews current applications in DNA/RNA and protein sequence analysis.

Algorithms↗
PubMed2003

Report from the 4th international meeting of the attention deficit hyperactivity disorder molecular genetics network.

Investigators from around the world have convened annually since 1999 to share their work on the molecular genetic basis of attention deficit hyperactivity disorder (ADHD). The 4th meeting of the ADHD Molecular Genetics Network was held in May 2002. This conference began with a discussion of ongoing and proposed collaborative projects. Ongoing collaborations include the International Multi-Center ADHD Genetics (IMAGE) project, a study of additive and interactive effects of DAT1 and DRD4 on ADHD and a meta-analysis of association studies of ADHD and the DRD5 gene. A new collaborative project regarding DNA pooling was also proposed. Over 20 investigators presented recent findings from their individual research sites. Participants also broke up into working groups to discuss additional projects and topics of mutual interest, including population genetic studies using dimensional phenotypic data and a QTL approach, endophenotypes for ADHD and the genetics of ADHD in adults. Finally, participant feedback was obtained about the utility of the ADHD Molecular Genetics Network conferences and the e-mail network. Although technological and statistical advances in the field of molecular genetics have allowed researchers to begin to identify specific genes that are associated with ADHD, continued collaborative efforts are needed to fully elucidate the genetic underpinnings of this complex phenotype. Data collected at the current conference indicate that the ADHD Molecular Genetics Network has facilitated new collaborative as well as single-site projects among ADHD researchers.

Attention Deficit Disorder with Hyperactivity↗
PubMed2005

Finite size effects in tightly meshed polymer networks.

Molecular dynamics computer simulations on regular, tightly meshed model networks exhibit variations of the network density with system size. We show that these variations are due mainly to network elasticity. A theoretical expression derived on the basis of the self-consistent-field approach yields finite size scaling behavior in good accord with the simulation for a wide range of thermodynamic conditions.

Journal Article↗
PubMed2005

Chaperone-related immune dysfunction: an emergent property of distorted chaperone networks.

Molecular chaperones (heat shock proteins) are important components of cellular networks, such as protein-protein and gene regulatory networks. Chaperones participate in the folding of immunologically important proteins, presentation of antigens and activation of the immune system. Here, we propose that chaperone-related immune dysfunction might be more general than was previously thought. Mutations and polymorphism of chaperones and the regulators of their synthesis, heat shock factor-1, chaperone diseases, sick chaperones and chaperone overload might all affect (mostly impairing) immune responses.

Animals↗
PubMed2005

A graphic tool for curating molecular interaction networks from the literature.

We propose a graphic tool for curating molecular interaction networks constructed from the literature by information extraction (IE). In order to turn preliminary results from IE into useful biomedical resources, we propose to use a controlled environment in which visualization and IE work synergistically. The usability of the proposed graphic tool is shown with respect to the identification of incorrectly extracted results that are due to the much troubling coordination phenomena in natural language texts. Through the experiment on molecular interactions in Saccaharomyces cerevisiae, we have seen a meaningful increase (from 91.5% to 97.5%) in the number of correctly extracted interaction information.

Computational Biology↗
PubMed2004

Genetic analysis of macrophage characteristics as a tool to identify tumor susceptibility genes: mapping of three macrophage-associated risk inflammatory factors, marif1, marif2, and marif3.

Genetic predisposition to cancer is influenced by allelic variation in tumor susceptibility genes (TSGs) as present in the germline. We previously demonstrated in the mouse that TSGs frequently participate in genetic interactions, indicating that they represent molecular networks. Inflammation may constitute one of the molecular networks underlying susceptibility to cancer by influencing the tumor microenvironment. Because macrophages play a key role in inflammation and are often associated with tumors, we argue that a subset of TSGs can be identified by examining the genetics of macrophage characteristics. A panel of inflammation-related assays was established to phenotype mouse bone marrow-derived macrophages, which included stimulation with lipopolysaccharides followed by measurement of secretion of tumor necrosis factor alpha and the p40 chain of interleukin-12 and of expression of inducible nitric oxide synthase and cyclooxygenase-2. This panel of assays was used for linkage analysis and applied to bone marrow-derived macrophages derived from individual mice of segregating crosses between inbred strain O20 and the highly related strains NTX-10 and NTX-20, which differed from O20 in only 10% of their genome, to reduce genetic complexity. Three macrophage-associated risk inflammatory factors were mapped-Marif1, Marif2, and Marif3-that each affected several inflammation-related assays, confirming that they function within molecular networks. Moreover, Marif1 and Marif2 were localized in regions with established linkage for both quantitative and qualitative aspects of lung cancer susceptibility. These studies provide a novel approach to investigate the genetics of microenvironmental influence on predisposition to tumorigenesis, thereby contributing to development of new strategies that aim to prevent or treat cancer.

Animals↗