11,273 research outputs found

    Comparing biological networks via graph compression

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Comparison of various kinds of biological data is one of the main problems in bioinformatics and systems biology. Data compression methods have been applied to comparison of large sequence data and protein structure data. Since it is still difficult to compare global structures of large biological networks, it is reasonable to try to apply data compression methods to comparison of biological networks. In existing compression methods, the uniqueness of compression results is not guaranteed because there is some ambiguity in selection of overlapping edges.</p> <p>Results</p> <p>This paper proposes novel efficient methods, CompressEdge and CompressVertices, for comparing large biological networks. In the proposed methods, an original network structure is compressed by iteratively contracting identical edges and sets of connected edges. Then, the similarity of two networks is measured by a compression ratio of the concatenated networks. The proposed methods are applied to comparison of metabolic networks of several organisms, <it>H. sapiens, M. musculus, A. thaliana, D. melanogaster, C. elegans, E. coli, S. cerevisiae,</it> and <it>B. subtilis,</it> and are compared with an existing method. These results suggest that our methods can efficiently measure the similarities between metabolic networks.</p> <p>Conclusions</p> <p>Our proposed algorithms, which compress node-labeled networks, are useful for measuring the similarity of large biological networks.</p

    Rigidity and flexibility of biological networks

    Full text link
    The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl

    Magnetism, FeS colloids, and Origins of Life

    Full text link
    A number of features of living systems: reversible interactions and weak bonds underlying motor-dynamics; gel-sol transitions; cellular connected fractal organization; asymmetry in interactions and organization; quantum coherent phenomena; to name some, can have a natural accounting via physicalphysical interactions, which we therefore seek to incorporate by expanding the horizons of `chemistry-only' approaches to the origins of life. It is suggested that the magnetic 'face' of the minerals from the inorganic world, recognized to have played a pivotal role in initiating Life, may throw light on some of these issues. A magnetic environment in the form of rocks in the Hadean Ocean could have enabled the accretion and therefore an ordered confinement of super-paramagnetic colloids within a structured phase. A moderate H-field can help magnetic nano-particles to not only overcome thermal fluctuations but also harness them. Such controlled dynamics brings in the possibility of accessing quantum effects, which together with frustrations in magnetic ordering and hysteresis (a natural mechanism for a primitive memory) could throw light on the birth of biological information which, as Abel argues, requires a combination of order and complexity. This scenario gains strength from observations of scale-free framboidal forms of the greigite mineral, with a magnetic basis of assembly. And greigite's metabolic potential plays a key role in the mound scenario of Russell and coworkers-an expansion of which is suggested for including magnetism.Comment: 42 pages, 5 figures, to be published in A.R. Memorial volume, Ed Krishnaswami Alladi, Springer 201

    Computational biology in the 21st century

    Get PDF
    Computational biologists answer biological and biomedical questions by using computation in support of—or in place of—laboratory procedures, hoping to obtain more accurate answers at a greatly reduced cost. The past two decades have seen unprecedented technological progress with regard to generating biological data; next-generation sequencing, mass spectrometry, microarrays, cryo-electron microscopy, and other highthroughput approaches have led to an explosion of data. However, this explosion is a mixed blessing. On the one hand, the scale and scope of data should allow new insights into genetic and infectious diseases, cancer, basic biology, and even human migration patterns. On the other hand, researchers are generating datasets so massive that it has become difficult to analyze them to discover patterns that give clues to the underlying biological processes.National Institutes of Health. (U.S.) ( grant GM108348)Hertz Foundatio

    Differential growth of wrinkled biofilms

    Get PDF
    Biofilms are antibiotic-resistant bacterial aggregates that grow on moist surfaces and can trigger hospital-acquired infections. They provide a classical example in biology where the dynamics of cellular communities may be observed and studied. Gene expression regulates cell division and differentiation, which affect the biofilm architecture. Mechanical and chemical processes shape the resulting structure. We gain insight into the interplay between cellular and mechanical processes during biofilm development on air-agar interfaces by means of a hybrid model. Cellular behavior is governed by stochastic rules informed by a cascade of concentration fields for nutrients, waste and autoinducers. Cellular differentiation and death alter the structure and the mechanical properties of the biofilm, which is deformed according to Foppl-Von Karman equations informed by cellular processes and the interaction with the substratum. Stiffness gradients due to growth and swelling produce wrinkle branching. We are able to reproduce wrinkled structures often formed by biofilms on air-agar interfaces, as well as spatial distributions of differentiated cells commonly observed with B. subtilis.Comment: 19 pages, 13 figure

    Field-control, phase-transitions, and life's emergence

    Get PDF
    Instances of critical-like characteristics in living systems at each organizational level as well as the spontaneous emergence of computation (Langton), indicate the relevance of self-organized criticality (SOC). But extrapolating complex bio-systems to life's origins, brings up a paradox: how could simple organics--lacking the 'soft matter' response properties of today's bio-molecules--have dissipated energy from primordial reactions in a controlled manner for their 'ordering'? Nevertheless, a causal link of life's macroscopic irreversible dynamics to the microscopic reversible laws of statistical mechanics is indicated via the 'functional-takeover' of a soft magnetic scaffold by organics (c.f. Cairns-Smith's 'crystal-scaffold'). A field-controlled structure offers a mechanism for bootstrapping--bottom-up assembly with top-down control: its super-paramagnetic components obey reversible dynamics, but its dissipation of H-field energy for aggregation breaks time-reversal symmetry. The responsive adjustments of the controlled (host) mineral system to environmental changes would bring about mutual coupling between random organic sets supported by it; here the generation of long-range correlations within organic (guest) networks could include SOC-like mechanisms. And, such cooperative adjustments enable the selection of the functional configuration by altering the inorganic network's capacity to assist a spontaneous process. A non-equilibrium dynamics could now drive the kinetically-oriented system towards a series of phase-transitions with appropriate organic replacements 'taking-over' its functions.Comment: 54 pages, pdf fil

    The micromechanics of the superficial zone of articular cartilage.

    Get PDF
    Journal ArticleOBJECTIVE: To investigate the relationships between the unique mechanical and structural properties of the superficial zone of articular cartilage on the microscopic scale. DESIGN: Fresh unstained equine metacarpophalangeal cartilage samples were mounted on tensile and compressive loading rigs on the stage of a multiphoton microscope. Sequential image stacks were acquired under incremental loads together with simultaneous measurements of the applied stress and strain. Second harmonic generation was used to visualise the collagen fibre network, while two photon fluorescence was used to visualise elastin fibres and cells. The changes visualised by each modality were tracked between successive loads. RESULTS: The deformation of the cartilage matrix was heterogeneous on the microscopic length scale. This was evident from local strain maps, which showed shearing between different regions of collagen under tensile strain, corrugations in the articular surface at higher tensile strains and a non-uniform distribution of compressive strain in the axial direction. Chondrocytes elongated and rotated under tensile strain and were compressed in the axial direction under compressive load. The magnitude of deformation varied between cells, indicating differences in either load transmission through the matrix or the mechanical properties of individual cells. Under tensile loading the reorganisation of the elastin network differed from a homogeneous elastic response, indicating that it forms a functional structure. CONCLUSIONS: This study highlights the complexity of superficial zone mechanics and demonstrates that the response of the collagen matrix, elastin fibres and chondrocytes are all heterogeneous on the microscopic scale.Arthritis Research U

    A novel neural network approach to cDNA microarray image segmentation

    Get PDF
    This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

    Bayesian Networks to Assess the Newborn Stool Microbiome

    Get PDF
    In human stool, a large population of bacterial genes and transcripts from hundreds of genera coexist with host genes and transcripts. Assessments of the metagenome and transcriptome are particularly challenging, since there is a great deal of sequence overlap among related species and related genes. We sequenced the total RNA content from stool samples in a neonate using previously-described methods. We then performed stepwise alignment of different populations of RNA sequence reads to different indices, including ribosomal databases, the human genome, and all sequenced bacterial genomes. Each pool of RNA at each alignment step was subjected to compression to assess sequence complexity in bits per symbol. In order to account for the high degree of overlap among species, a Bayesian network tool (RNABayes) was constructed using a node based on 16S sequencing, and a large number of nodes based on alignment scores to bacterial genes. The following algorithm was then employed: (1) fit 16S census from a sample onto a Dirichlet distribution using maximum likelihood estimation to get the conjugate prior, (2) estimate probabilities of each bacterial genus for each bacterial mRNA alignment using BLAST alignment scores, (3) fit each of these probabilities to a Dirichlet distribution using maximum likelihood estimation, (4) perform inference iteratively to update the conjugate prior, with the result being the posterior probability distribution of metabolically active stool bacteria. This algorithm was then applied to three datasets: (1) a simulated data set with normally distributed mRNAs, (2) a simulated data set with skewed mRNAs for a single bacterial population, and (3) the RNASeq dataset from our newborn stool sample. Results indicate that a Bayesian network built in this fashion reliably adjusts the prior bacterial population distribution to more accurately reflect the transcriptionally active bacterial population. Application of this method to real world samples appears to show even more marked skew, indicating transcripts are not uniformly distributed by population
    corecore