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PubMed1988

Collation of student results in practical class experiments in physiology, using a BBC ECONET computer network.

Funding was provided to Queen's University by the Department of Education and by the Industrial Development Board for Northern Ireland to provide microcomputers for undergraduate use. An allocation from the grant to the Department of Physiology enabled the purchase of 20 BBC Master 128 microcomputers with monitors used as student work-stations connected together by an ECONET network with a file server using a dual floppy disc drive, two printer servers and two demonstrator stations. A BASIC program was written to analyse the students' practical class measurements which they entered manually at their work-station keyboards. Class results were presented to the students in the form of frequency distribution histograms or X/Y graphs. Program modifications to suit different practicals can be made relatively easily. The time taken to analyse data has been shortened. It is easy for the students to get immediate comparison of their own results with those of the rest of the class--particularly advantageous if the student's own experiment did not work. The class can be divided into groups to study different variations of the experiment and provide the data from each group to the whole class. The students' opinions on whether the equipment had (1) improved the teaching of physiology and (2) provided helpful preparation for the use of computers in medical practice were assessed by a questionnaire which showed that a clear majority felt these aims had been fulfilled.

Computer-Assisted Instruction↗
PubMed2002

The KEGG database.

KEGG (http://www.genome.ad.jp/kegg/) is a suite of databases and associated software for understanding and simulating higher-order functional behaviours of the cell or the organism from its genome information. First, KEGG computerizes data and knowledge on protein interaction networks (PATHWAY database) and chemical reactions (LIGAND database) that are responsible for various cellular processes. Second, KEGG attempts to reconstruct protein interaction networks for all organisms whose genomes are completely sequenced (GENES and SSDB databases). Third, KEGG can be utilized as reference knowledge for functional genomics (EXPRESSION database) and proteomics (BRITE database) experiments. I will review the current status of KEGG and report on new developments in graph representation and graph computations.

Amino Acid Sequence↗
PubMed1995

Viewing genome data as objects for application development.

Genomics is becoming a data-intensive science, and an increasing number of laboratories are generating data which swamps storage in traditional paper-and-ink notebooks. Capturing the data flow requires large systems with multiple applications manipulating the same or similar data. Large systems often have conflicting requirements for data representation. Consistency across applications is a prime consideration, and appropriate data representation is an important issue in developing practical systems for molecular biologists. Graphs are a natural representation for describing genome data, while objects are good for modeling the behavior necessary for laboratory applications. We present a method for translating graph descriptions of genome data into objects using objects as views on graphs. Graph representations describe genome concepts while objects capture individual views for application development insuring consistency across genome applications.

Chromosome Mapping↗
PubMed1995

[Glossary of terms used by radiologists in image processing].

We give the definition of 166 words used in image processing. Adaptivity, aliazing, analog-digital converter, analysis, approximation, arc, artifact, artificial intelligence, attribute, autocorrelation, bandwidth, boundary, brightness, calibration, class, classification, classify, centre, cluster, coding, color, compression, contrast, connectivity, convolution, correlation, data base, decision, decomposition, deconvolution, deduction, descriptor, detection, digitization, dilation, discontinuity, discretization, discrimination, disparity, display, distance, distorsion, distribution dynamic, edge, energy, enhancement, entropy, erosion, estimation, event, extrapolation, feature, file, filter, filter floaters, fitting, Fourier transform, frequency, fusion, fuzzy, Gaussian, gradient, graph, gray level, group, growing, histogram, Hough transform, Houndsfield, image, impulse response, inertia, intensity, interpolation, interpretation, invariance, isotropy, iterative, JPEG, knowledge base, label, laplacian, learning, least squares, likelihood, matching, Markov field, mask, matching, mathematical morphology, merge (to), MIP, median, minimization, model, moiré, moment, MPEG, neural network, neuron, node, noise, norm, normal, operator, optical system, optimization, orthogonal, parametric, pattern recognition, periodicity, photometry, pixel, polygon, polynomial, prediction, pulsation, pyramidal, quantization, raster, reconstruction, recursive, region, rendering, representation space, resolution, restoration, robustness, ROC, thinning, transform, sampling, saturation, scene analysis, segmentation, separable function, sequential, smoothing, spline, split (to), shape, threshold, tree, signal, speckle, spectrum, spline, stationarity, statistical, stochastic, structuring element, support, syntaxic, synthesis, texture, truncation, variance, vision, voxel, windowing.

Diagnostic Imaging↗
PubMed2005

Insights into the quaternary association of proteins through structure graphs: a case study of lectins.

The unique three-dimensional structure of both monomeric and oligomeric proteins is encoded in their sequence. The biological functions of proteins are dependent on their tertiary and quaternary structures, and hence it is important to understand the determinants of quaternary association in proteins. Although a large number of investigations have been carried out in this direction, the underlying principles of protein oligomerization are yet to be completely understood. Recently, new insights into this problem have been gained from the analysis of structure graphs of proteins belonging to the legume lectin family. The legume lectins are an interesting family of proteins with very similar tertiary structures but varied quaternary structures. Hence they have become a very good model with which to analyse the role of primary structures in determining the modes of quaternary association. The present review summarizes the results of a legume lectin study as well as those obtained from a similar analysis carried out here on the animal lectins, namely galectins, pentraxins, calnexin, calreticulin and rhesus rotavirus Vp4 sialic-acid-binding domain. The lectin structure graphs have been used to obtain clusters of non-covalently interacting amino acid residues at the intersubunit interfaces. The present study, performed along with traditional sequence alignment methods, has provided the signature sequence motifs for different kinds of quaternary association seen in lectins. Furthermore, the network representation of the lectin oligomers has enabled us to detect the residues which make extensive interactions ('hubs') across the oligomeric interfaces that can be targetted for interface-destabilizing mutations. The present review also provides an overview of the methodology involved in representing oligomeric protein structures as connected networks of amino acid residues. Further, it illustrates the potential of such a representation in elucidating the structural determinants of protein-protein association in general and will be of significance to protein chemists and structural biologists.

Amino Acid Sequence↗
PubMed2004

Counter-propagation artificial neural network as a tool for the independent variable selection: structure-mutagenicity study on aromatic amines.

The counter-propagation artificial neural network (CP ANN) technique was applied for the independent variable selection and for structure-mutagenic potency modeling on a set of 95 aromatic and heteroaromatic amines with biological activity investigated experimentally by an in vitro assay. The molecular structures were represented by 275 independent variables classified as topostructural, topochemical, geometrical and quantum-chemical descriptors. As a result of the neural network modeling, the following descriptors were found to be the most important for structure-activity relationship: 5 chi -path connectivity index of order h = 5, 3chibC-bond cluster connectivity index of order h = 3, J(B)-Balaban's J index based on bond types, SHSNH2-electrotopological state index values for atoms, phia-flexibility index (kappa p1 x kappa p2/nvx), IC0-mean information content or complexity of a graph based on the 0 order neighborhood of vertices in a hydrogen-filled graph and ELUMO. The leave one out (LOO) method was used in order to test and select the models for mutagenicity prediction. The statistical parameters for the 7-descriptors model are R(Model) = 0.96 and Rcv = 0.85, respectively. In the next step, the number of variables was reduced and the 4-descriptors model was found (R(Model) = 0.95 and Rcv = 0.85) and classified as the best one.

Algorithms↗
PubMed2005

POPSCOMP: an automated interaction analysis of biomolecular complexes.

Large-scale analysis of biomolecular complexes reveals the functional network within the cell. Computational methods are required to extract the essential information from the available data. The POPSCOMP server is designed to calculate the interaction surface between all components of a given complex structure consisting of proteins, DNA or RNA molecules. The server returns matrices and graphs of surface area burial that can be used to automatically annotate components and residues that are involved in complex formation, to pinpoint conformational changes and to estimate molecular interaction energies. The analysis can be performed on a per-atom level or alternatively on a per-residue level for low-resolution structures. Here, we present an analysis of ribosomal structures in complex with various antibiotics to exemplify the potential and limitations of automated complex analysis. The POPSCOMP server is accessible at http://ibivu.cs.vu.nl/programs/popscompwww/.

Anti-Bacterial Agents↗
PubMed2005

Making sense of high-throughput protein-protein interaction data.

Accurate systems biology modeling requires a complete catalog of protein complexes and their constituent proteins. We discuss a graph-theoretic/statistical algorithm for local dynamic modeling of protein complexes using data from affinity purification-mass spectrometry experiments. The algorithm readily accommodates multicomplex membership by individual proteins and dynamic complex composition, two biological realities not accounted for in existing topological descriptions of the overall protein network. A likelihood-based objective function guides the protein complex modeling algorithm. With an accurate complex membership catalog in place, systems biology can proceed with greater precision.

Journal Article↗
PubMed2001

A proliferation control network model: the simulation of two-dimensional epithelial homeostasis.

Despite the recent progress in the description of the molecular mechanisms of proliferation and differentiation controls in vitro, the regulation of the homeostasis of normal stratified epithelia remains unclear in vivo. Computer simulation represents a powerful tool to investigate the complex field of cell proliferation regulation networks. It provides huge computation capabilities to test, in a dynamic in silico context, hypotheses about the many pathways and feedback loops involved in cell growth and proliferation controls. Our approach combines a model of cell proliferation and a spatial representation of cells in 2D using the Voronoi graph. The cell proliferation model includes intracellular (cyclins, Cyclin Dependent Kinases - CDKs. Retinoblastoma protein - Rb, CDK inhibitors) and extracellular controls (growth and differentiation factors, integrins). The Voronoi graph associates a polygon with every cell and the set of these polygons defines the tissue architecture. Thus, the model provides a quantitative model of extracellular signals and cell motility as a function of the neighborhood during time dependent simulations. The 2D simulations illustrate the influence of the microenvironment on cell proliferation in basal layers of stratified epithelia and of differential adherence in keratinocytes differentiation and related upward migration. Our results particularly show the role of CDK inhibitors (mainly the protein p27) in the Rb dependent control pathway of the transition from the G1 to S phase of the cell cycle.

Cell Cycle↗
PubMed2005

The effect of proton disorder on the structure of ice-Ih: a theoretical study.

A precise and accurate measurement of the crystal structure of ice-Ih is hindered by its disordered H-bond network. In this work, we carried out first-principle calculations to study the effects of H-bond topology on the structure of ice-Ih with emphasis on the molecular geometry of water and the distortion in oxygen lattice. An analytic algorithm based on group and graph theory is employed to enumerate all possible configurations in a given unit cell and to select a set of structures for detailed examinations. In total we have studied more than 60 ice-Ih structures in a hexagonal unit cell of 48 water molecules by quantum-chemical methods and found a significant amount of static distortion in the oxygen positions from their crystallographic positions which is in good agreements with highly significant higher-order terms obtained from both x-ray and neutron-diffraction data. Much debated structural information such as H-O-H angle and O-H bond length is found to be 106.34+/-0.36 degrees and 0.9997+/-0.0008 A, compared to experimental value of 106.6+/-1.5 degrees and 0.986+/-0.005 A. Detailed benchmarking calculations were carried out to gauge the influence of using different exchange and correlation functionals, pseudopotentials, and unit-cell sizes. Our results have proven that first-principle methods are useful complementary tools to experiments, especially for cases in which experimental accuracy is limited by intrinsic orientational disorder.

Journal Article↗
PubMed2005

Topological approach to neural complexity.

Considerable effort in modern statistical physics is devoted to the study of networked systems. One of the most important example of them is the brain, which creates and continuously develops complex networks of correlated dynamics. An important quantity which captures fundamental aspects of brain network organization is the neural complexity C(X) introduced by Tononi et al. [Proc. Natl. Acad. Sci. USA 91, 5033 (1994)]. This work addresses the dependence of this measure on the topological features of a network in the case of a Gaussian stationary process. Both analytical and numerical results show that the degree of complexity has a clear and simple meaning from a topological point of view. Moreover, the analytical result offers a straightforward and faster algorithm to compute the complexity of a graph than the standard one.

Animals↗
PubMed2006

Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle.

MOTIVATION: To understand the behaviour of complex biological regulatory networks, a proper integration of molecular data into a full-fledge formal dynamical model is ultimately required. As most available data on regulatory interactions are qualitative, logical modelling offers an interesting framework to delineate the main dynamical properties of the underlying networks. RESULTS: Transposing a generic model of the core network controlling the mammalian cell cycle into the logical framework, we compare different strategies to explore its dynamical properties. In particular, we assess the respective advantages and limits of synchronous versus asynchronous updating assumptions to delineate the asymptotical behaviour of regulatory networks. Furthermore, we propose several intermediate strategies to optimize the computation of asymptotical properties depending on available knowledge. AVAILABILITY: The mammalian cell cycle model is available in a dedicated XML format (GINML) on our website, along with our logical simulation software GINsim (http://gin.univ-mrs.fr/GINsim). Higher resolution state transitions graphs are also found on this web site (Model Repository page).

Animals↗
PubMed2002

Dynamical random graphs with memory.

We study the large-time dynamics of a Markov process whose states are finite but unbounded graphs. The number of vertices is described by a supercritical branching process, and the edges follow a certain mean-field dynamics determined by the rates of appending and deleting: the older an edge is, the lesser is the probability that it is still in the graph. The lifetime of any edge is distributed exponentially. We call its mean value (common for all edges) a parameter of memory, since it shows for how long the system keeps a particular connection between the vertices in the graph. We show that our model provides a bridge between two well-known models: when the parameter of memory goes to infinity this is a generalized model of random growth, and when this parameter is zero, i.e., no memory, our model behaves as a random graph. Thus by introducing a general class of dynamical graphs we have a unified overview on rather different models and the relations between them. We find all the critical values of the parameters at which our model exhibits phase transitions and describe the properties of the phase diagram. Finally, we compare and discuss the efficiency of the corresponding networks.

Journal Article↗
PubMed1996

Hydrogen-bond patterns in 1,4-dihydro-2,3-quinoxalinediones: ligands for the glycine modulatory site on the NMDA receptor.

The crystal structures of five 1, 4-dihydro-2, 3-quinoxalinediones, antagonists of the NMDA modulatory glycine binding site on the excitary amino acid (EAA) receptor complex, have been determined: (I) 6, 7-dinitro-1, 4-dihydro-2, 3-quinoxalinedione (DNQX); (II) 5, 7-dinitro-1,4-dihydro-2, 3-quinoxalinedione (MNQX); (III) 6-nitro-1,4-dihydro-2, 3-quinoxalinedione hydrate; (IV) 6, 7-dichloro-1, 4-dihydro-2, 3-quinoxalinedione; (V) 5, 7-dichloro-I, 4-dihydro-2, 3-quinoxalinedione dimethylformamide. The crystal structure of the most active compound (II) contains a unique intramolecular N--H...O(NO2) hydrogen bond, which may be important for activity, as semiempirical calculations show that this bond is stable over a wide range of dihedral angles between the planes of the molecule and of the nitro group. In the other compounds the intermolecular hydrogen bonds connect molecules into three-dimensional networks. In compounds (I), (III) and (IV) head-to-tail pi-stacking is found between molecules connected by a center of symmetry. The geometries of the hydrogen-bonded -NH-C = O fragments show evidence of pi-cooperativity or resonance-assisted hydrogen bonding. Graph-set analysis of the hydrogen-bond patterns of quinoxalinedione derivatives shows a tendency to form two types of hydrogen-bonding motifs: a centrosymmetric dimeric ring and an infinite chain. Even though this pattern may be modified by the presence of additional hydrogen-bond acceptors and/or donors, as well as by solvent molecules, general similarities have been found. Comparison of all quinoxalinedione structures suggests that the hydrogen-bonding pattern necessary for the biological activity at the glycine binding site contains one donor and two acceptors.

Anticonvulsants↗
PubMed2025

Seed2LP: seed inference in metabolic networks for reverse ecology applications.

MOTIVATION: A challenging problem in microbiology is to determine nutritional requirements of microorganisms and culture them, especially for the microbial dark matter detected solely with culture-independent methods. The latter foster an increasing amount of genomic sequences that can be explored with reverse ecology approaches to raise hypotheses on the corresponding populations. Building upon genome-scale metabolic networks (GSMNs) obtained from genome annotations, metabolic models predict contextualized phenotypes using nutrient information. RESULTS: We developed the tool Seed2LP, addressing the inverse problem of predicting source nutrients, or seeds, from a GSMN and a metabolic objective. The originality of Seed2LP is its hybrid model, combining a scalable and discrete Boolean approximation of metabolic activity, with the numerically accurate flux balance analysis (FBA). Seed inference is highly customizable, with multiple search and solving modes, exploring the search space of external and internal metabolites combinations. Application to a benchmark of 107 curated GSMNs highlights the usefulness of a logic modelling method over a graph-based approach to predict seeds, and the relevance of hybrid solving to satisfy FBA constraints. Focusing on the dependency between metabolism and environment, Seed2LP is a computational support contributing to address the multifactorial challenge of culturing possibly uncultured microorganisms. AVAILABILITY AND IMPLEMENTATION: Seed2LP is available on https://github.com/bioasp/seed2lp.

Metabolic Networks and Pathways↗
PubMed2025

Design Rules for Expanding PAM Compatibility in CRISPR-Cas9 from the VQR, VRER and EQR variants.

Expanding the range of Protospacer Adjacent Motifs (PAMs) recognized by CRISPR-Cas9 is essential for broadening genome-editing applications. Here, we combine molecular dynamics simulations with graph-theory and centrality analyses to dissect the principles of PAM recognition in three Cas9 variants - VQR, VRER, and EQR - that target non-canonical PAMs. We show that efficient recognition is not dictated solely by direct contacts between PAM-interacting residues and DNA, but also by a distal network that stabilizes the PAM-binding domain and preserves long-range communication with REC3, a hub that relays signals to the HNH nuclease. A key role emerges for the D1135V/E substitution, which enables stable DNA binding by K1107 and preserves key DNA phosphate locking interactions via S1109, securing stable PAM engagement. In contrast, variants carrying only R-to-Q substitutions at PAM-contacting residues, though predicted to enhance adenine recognition, destabilize the PAM-binding cleft, perturb REC3 dynamics, and disrupt allosteric coupling to HNH. Together, these findings establish that PAM recognition requires local stabilization, distal coupling, and entropic tuning, rather than a simple consequence of base-specific contacts. This framework provides guiding principles for engineering Cas9 variants with expanded PAM compatibility and improved editing efficiency.

Journal Article↗
PubMed2005

Complementary identification of multiple flux distributions and multiple metabolic pathways.

Cell robustness and complexity have been recognized as unique features of biological systems. Such robustness and complexity of metabolic-reaction systems can be explored by discovering, or identifying, the multiple flux distributions (MFD) and redundant pathways that lead to a given external state; however, this is exceedingly cumbersome to accomplish. It is, therefore, highly desirable to establish an effective computational method for their identification, which, in turn, gives rise to a novel insight into the cellular function. An effective approach is proposed for complementarily identifying MFD in metabolic flux analysis and multiple metabolic pathways (MMP) in structural pathway analysis. This approach judiciously integrates flux balance analysis (FBA) based on linear programming and the graph-theoretic method for determining reaction pathways. A single metabolic pathway, with the concomitant flux distribution and the overall reaction manifesting itself as the desired phenotype under some environmental conditions, is determined by FBA from the initial candidate sequence of metabolic reactions. Subsequently, the graph-theoretic method recovers all feasible MMP and the corresponding MFD. The approach's efficacy is demonstrated by applying it to the in silico Escherichia coli model under various culture conditions. The resultant MMP and MFD attaining a unique external state reveal the surprising adaptability and robustness of the intricate cellular network as a key to cell survival against environmental or genetic changes. These results indicate that the proposed approach would be useful in facilitating drug discovery.

Algorithms↗
PubMed2004

Cognitive navigation based on nonuniform Gabor space sampling, unsupervised growing networks, and reinforcement learning.

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.

Cognition↗