bluuebunny/crossref_metadata_2025_split huggingface dataset:
COGs
: an evolutionary classification of genes and proteins from sequenced genomes
Authors: Koonin Eugene V. 
Published: April 2005

Abstract
A comprehensive classification of genes from sequenced genomes, based on evolutionary principles, is a must for the success of comparative and functional genomics. One such classification system is the database of Clusters of Orthologous Groups of proteins (COGs), which was constructed by clustering the results of an all‐against‐all comparison of the protein sequences encoded in prokaryotic and eukaryotic genomes. Each COG includes genes or sets of genes from three or more genomes, which are orthologous to each other, that is, evolved from a single ancestral gene in the common ancestor of the analyzed organisms. Between 50 and 85% of the genes from the sequenced genomes belong to COGs, indicating notable evolutionary conservation. The COG system is a natural framework for comparative genomics and has the potential of facilitating both functional annotation of genomes and large‐scale evolutionary studies.
Functionally and structurally relevant residues in PROSITE motif descriptors
Authors: Sigrist Christian J. A. , De Castro Edouard , Langendijk‐Genevaux Petra S. , Saux Virginie Le , Bairoch Amos , Hulo Nicolas 
Published: April 2005

Abstract
PROSITE is an annotated collection of motif descriptors, which are either patterns (regular expressions) or generalized profiles (weight matrices). Whereas patterns perform well for the identification of short well‐conserved regions such as active sites, profiles usually cover the full length of the protein or domain and can accept a mismatch at a highly conserved position if the rest of the sequence shows a sufficiently high level of similarity. Hence, a profile can match a structural domain even if the active site is mutated and thus is no longer functional. To distinguish these cases and make functional predictions more accurate, biologically meaningful residues of a profile can be tagged and the corresponding residues in the sequence retrieved and checked for their biological properties.
Classification of proteins by clustering techniques
Authors: Kriventseva Evgenia V. 
Published: April 2005

AbstractIn the past few years, the technology of sequencing has developed to the stage at which the sequencing of a complete genome can be contemplated as a practical and routine possibility. To cope with the exponentially growing data, methodologies for computational classification of biological sequences are required. Recently, several approaches for automatic functional classification of proteins were developed. This review describes the most widely applied clustering algorithms in Bioinformatics and resources developed for protein classification.
New approaches to bridging the timescale gap in the computer simulation of biomolecular processes
Authors: Caves Leo S. D. , Verma Chandra S. 
Published: April 2005

Abstract
Current state‐of‐the‐art computer simulations of biomolecules probe timescales that are generally very short compared to those that characterize most interesting biochemical and biological processes. New developments in simulation methods extend the reach of simulation in a trend that reflects the increasing time resolution of experimental spectroscopic techniques. We propose that the tools of dynamical systems analysis offer a potentially powerful approach to reconciling the view of biomolecular dynamics emerging from both simulation and experiment.
Constraint‐based modeling of metabolomic systems
Authors: Beard Daniel A. , Qian Hong 
Published: April 2005

Abstract
Constraint‐based analysis of large‐scale metabolic systems is the computational exploration of metabolic fluxes and concentrations constrained by the physical chemical laws of mass conservation and thermodynamics. This chapter reviews the mathematical formulation of the constraints on reaction fluxes and reactant concentrations that arise as a consequence of the stoichiometry of a specified network of biochemical reactions. Linear algebraic constraints arising from steady state mass balance form the basis of the related computational technologies metabolic flux analysis (MFA) and flux‐balance analysis (FBA). Thermodynamic laws, while introducing inherent nonlinearities into the mathematical description of the feasible space, facilitate the introduction of reactant concentrations to the constraint‐based framework. Together, the mass‐balance and thermodynamic constraints form the basis of an approach to modeling and analysis of biochemical systems that is alternate and complementary to detailed chemical kinetics.
A complex systems approach to understand how cells control their shape and make cell fate decisions
Authors: Ingber Donald E. , Huang Sui 
Published: April 2005

AbstractIn this chapter, we analyze the mechanisms by which mammalian cells maintain their physical shape and make cell fate decisions whether to grow, move, differentiate, or die in response to chemical and physical signals during tissue development. We address how these robust and mutually exclusive, system‐level behavioral programs emerge from the dynamics of molecular networks that establish both the hardware (structure) and the software (information processing) of the cell. Molecular filaments within the cytoskeleton self‐organize using tensegrity architectural principles in which prestress and force balances between different filaments play key stabilizing roles. In this manner, the filaments form a physical network structure that stabilizes the shape of the cell while providing it with the flexibility necessary for movement. As for cellular information processing, genome‐wide regulatory networks impose constraints on the collective dynamics of signaling molecules, so that the biochemical activity of genes and proteins exhibits large‐scale coherent patterns that correspond to stable, high‐dimensional attractor states. These attractors define distinct cell fates that are stable, yet can undergo regulated all‐or‐none transitions between distinct stable cell phenotypes. Because of the robustness of these attractors, physical cues devoid of molecular specificity, such as mechanical stress and cell shape distortion, can produce similar effects on cell behavior as highly specific growth factors. This coordination between structural networks and information networks is critical for the formation and homeostasis of complex tissue structures in multicellular organisms. Taken together, this work conveys the importance of complementing existing “bottom‐up” approaches in systems biology that emphasize comprehensive characterization of molecular processes, with “top‐down” approaches that place these molecular activities within the context of actual system‐level properties at the level of the whole living cell.
Systems biology of the heart
Authors: Noble Denis 
Published: April 2005

Abstract
Models of the heart have been developed since 1960, starting with the discovery and modeling of potassium channels. The first models of calcium balance were made in the 1980s and have now reached a high degree of physiological detail. During the 1990s, these cell models have been incorporated into anatomically detailed tissue and organ models. Systems biology of the heart is now well developed and is proving increasingly useful in unraveling complicated physiological functions and in understanding disease states.
Integrative modeling of the pancreatic β‐cell
Authors: Sherman Arthur , Bertram Richard 
Published: April 2005

AbstractPancreatic β‐cells secrete insulin, which regulates the concentration of plasma glucose. Dysfunction of this system is a necessary contributing factor to Type II diabetes, and may be sufficient. Insulin secretion is controlled by oscillations of membrane potential, calledbursting oscillations, which drive oscillations of cytosolic calcium. We describe the development of mathematical models for this mechanism, beginning with the Chay–Keizer model.
EGFRnetwork
Authors: Shvartsman Stanislav Y. 
Published: April 2005

AbstractThe epidermal growth factor receptor (EGFR) network is an evolutionarily conserved regulator of epithelial tissues. At this time, we have a relatively good understanding of the mechanisms by which the EGFR network can control the physiology of a single cell. The current challenge, however, is to understand how this network operates in multicellular systems and tissues. To accomplish this, it will be necessary to integrate information from genetic, biochemical, and cellular studies into predictive, quantitative models.
Contig mapping and analysis
Authors: Siddiqui Asim , Jones Steven 
Published: April 2005

Abstract
Physical mapping and the creation of contig maps remains a valuable tool for genomics research. This section describes the methods and techniques used to create contig maps from restriction fragment fingerprint information. It mentions some of the bioinformatics challenges associated with building a high‐quality map and points to ongoing research that is attempting to address this problem. This section also illustrates how tiling path clones are chosen from a redundant set of clones.
Comparative analysis for mapping and sequence assembly
Authors: Milosavljevic Aleksandar 
Published: April 2005

AbstractThe increasing number of assembled reference sequences produced by ongoing genome sequencing projects provides information that is useful for the mapping and assembly of related genomes. Comparative information has the potential to both decrease the cost and accelerate mapping and sequencing projects by reducing experimental effort. Anchoring of contigs from one species onto an assembled genome of the other related species provides hypothetical order, orientation, and distance information. Mapping cloned genomic fragments, such as Bacterial Artificial Chromosomes (BACs), of one species onto an already assembled genome of another may be either used to select clones for targeted comparative sequencing or to infer the hypothetical order and orientation of BACs. Assembled genomic sequence of one species can be used as a template to guide the assembly of another, related species.
Information theory as a model of genomic sequences
Authors: Bi Chengpeng , Rogan Peter K. 
Published: April 2005

Abstract
Shannon information theory can be used to quantify overall sequence conservation among sets of related sequences. Variation in nucleic acid sequences recognized by proteins can be comprehensively modeled with information weight matrices that permit each member sequence to be rank‐ordered according to its respective individual information contents. These rankings can be used to compute the affinities of recognition sites by proteins and to predict the effects of nucleotide substitutions in the sequences of these sites. The distribution of information across a set of protein‐binding sites in DNA is related to the pattern of intermolecular contacts that stabilize the protein‐nucleic acid complex (i.e., the corresponding helical structure of double‐stranded DNA).
Gene structure prediction by genomic sequence alignment
Authors: Morgenstern Burkhard 
Published: April 2005

Abstract

Traditional gene‐finding programs are based on stochastical approaches such as hidden Markov models (HMM) describing the statistical composition of coding and noncoding regions for a given organism. Comparative gene prediction, by contrast, is based on the
phylogenetic footprinting
principle. During evolution, functionally important sites in the genome tend to be more conserved than nonfunctional regions. Thus, local sequence conservation usually indicates biological functionality. Comparative gene‐finding approaches start with an
alignment
of evolutionary related genomic sequences and try to identify splice signals in areas of local sequence conservation. This way, putative exons are identified that are used to construct gene models. In this chapter, the comparative genefinder AGenDA is described. Its performance is compared to more traditional approaches, and possible future developments are discussed.
Reconstructing vertebrate phylogenetic trees
Authors: Kumar Sudhir , Filipski Alan 
Published: April 2005

AbstractWithin the last three decades, the methodology underlying vertebrate phylogenetic analysis, including the dating of speciation events, has grown from the use of morphological characters in living organisms and fossils to an increased emphasis on molecular phylogenetics. Here, we use a selected set of examples to give a brief survey of how these approaches have been used together to resolve some interesting and difficult questions regarding both lower‐ and higher‐level relationships of vertebrate species.
Mapping mutations on phylogenies
Authors: Nielsen Rasmus 
Published: April 2005

AbstractThis chapter provides a short review of recent methodologies developed for mapping mutations on phylogenies. Mapping of mutations, or character changes in general, using the maximum parsimony principle has been one of the most powerful tools in phylogenetics, and it has been used in a variety of different applications, for example, in the detection of correlated evolution and to identify selection acting on DNA sequences. However, many uses of parsimony mappings have been criticized because they focus on only one of many possible mappings and/or because they do not incorporate statistical uncertainty in the mapping. Recently developed probabilistic methods can incorporate statistical uncertainty in the character mappings. In these methods, focus is on a probability distribution of mutational mappings instead of a single estimate of the mutational mapping.
Score functions for structure prediction
Authors: Goldstein Richard A. 
Published: April 2005

Abstract
Protein structure prediction methods often make use of a score function, which describes how compatible a given fold is for the target sequence. There are a wide variety of methods for constructing these score functions, ranging from physics‐based potentials that try to calculate the free energy, to simplified potentials, to functions drawn from machine‐learning techniques. Often, these score functions have a number of parameters whose values must be determined. For physics‐based potentials, these may be available through ab initio calculations and experimental observations of small molecules. For all of the different functional forms, it is possible to determine the values of these parameters on the basis of an analysis of available protein structures. Three different methods to do this – based on thermodynamics, statistics, and machine learning – are described.
Protein domains
Authors: Heringa Jaap 
Published: April 2005

AbstractMany cellular processes involve proteins composed of multiple domains. The modular nature afforded by breaking up a protein's three‐dimensional structure into discrete domains has many evolutionary advantages. First, it allows proteins to evolve to complex cooperative functions that require elaborate, large, and stable structural scaffolds, which would be impossible to form otherwise within the structural constraints associated with the protein‐folding process. Second, it can bring active sites in proximity in a fixed stoichiometric ratio. Third, otherwise unstable intermediates can be accommodated within interdomain clefts. Fourth, the flexibility afforded by protein domain linker regions, for example, hinge regions, can greatly aid the activity of the cooperative catalytic process carried out by a multidomain enzyme. Fifth, the repeated and combinatorial use of domains in multidomain proteins has provided nature both an evolutionary economic and versatile system to maintain and enhance cellular activity.
Modeling by homology
Authors: Mizuguchi Kenji 
Published: April 2005

Abstract
The structures of proteins can be modeled reasonably accurately on the basis of their relationships with homologous proteins, the structures of which have been experimentally determined. Recent advances in techniques for detecting distant homologs have increased the potential of this approach considerably. Combined with the availability of increasing numbers of genome sequences and experimentally defined protein structures, new methods for modeling by homology now provide great opportunities in areas raging from research into molecular and cellular processes to structure‐based drug design.
Threading for protein‐fold recognition
Authors: Lin Kuang 
Published: April 2005

Abstract

Protein three‐dimensional structures, which provide vital biological information, can often be solved using X‐ray crystallography and NMR techniques (Thornton
et al.
,

; Orengo
et al.
,

). These structure‐solving techniques have been greatly improved in the last two decades. However, protein sequences from large‐scale gene‐sequencing projects are emerging at extraordinary rates. The gap between the number of known protein sequences and the known protein structures is continuously enlarging. Methods of predicting protein structure from its amino acid sequence are therefore of great importance to many biological researches.
The Gene Ontology project
Authors: Harris Midori A. , Lomax Jane , Ireland Amelia , Clark Jennifer I. 
Published: April 2005

AbstractThe main goal of the Gene Ontology (GO) project is to support the construction and use of structured, controlled vocabularies to address the growing need for meaningful annotation of genes and their products in different organisms. There are three key aspects of the GO project: the development of dynamic, controlled vocabularies that can be applied to all organisms even as knowledge of the roles of gene products is accumulating and changing; the application of GO terms in annotating genes or gene products; and the development and maintenance of databases and software for querying, displaying, and manipulating ontologies and associated annotation sets. The GO vocabularies are four nonoverlapping, structured networks of terms that describe key aspects of biology.Molecular functiondescribes the activities or tasks performed by individual gene products at the molecular level;biological processdescribes broad biological goals that are accomplished by ordered assemblies of molecular functions;cellular componentencompasses subcellular structures, locations, and macromolecular complexes;sequence ontologyincludes genome feature terms. The GO project's resources are available to the public athttp://www.geneontology.org.
Ontologies for natural language processing
Authors: Lussier Yves A. 
Published: April 2005

AbstractRapid technological improvements of biomedical computational semantics and natural language processing (NLP) are leading to a profound transformation in the reuse of knowledge found in scientific journal articles or semistructured biomedical databases. Indeed, both contain textual entries, which can be transformed and annotated with formal language representation, description logic and ontologies (such as Gene Ontology), and can be stored in ontology‐anchored databases. Recent advances in the standardization of electronic document interchange formats (e.g., XML, RDF, OWL) also contribute to the reuse of information and knowledge. While scientific biomedical literature remains the pinnacle of knowledge in terms of breadth and depth, as compared to derivative databases (e.g., Pubmed), its role is challenged by alternate receptacles of original knowledge, such as biomedical databases containing semistructured textual entries and highly computable data (e.g., Genbank). As a result of the development of digital libraries and semistructured textual databases, automated tools are increasingly researched to create data structures indeclarative knowledge, a highly computable data structure using logic. Though current generation ofliterary knowledgetrumps declarative knowledge in quantity, its noticeable value is compromised at aretrieval cost, owing to literary knowledge being buried in an overwhelming growth of scientific articles. Similarly, data entry of biomedical databases derived from scientific journals, generally accomplished via manual annotation, is a rate‐limiting and costly process. In contrast, automated data structures derived from NLP portend instantaneous and comprehensivelinguistic knowledgeacross boundless scientific articles and research communities. Increasing efforts have been invested to translate linguistic data structures generated by NLP into ontology‐anchored declarative data sets to obtain otherwise unattainable large‐scale or cross‐disciplinary inferences. Additionally, NLP based on Harris' Sublanguage Theory challenges the very nature by which we conceive and maintain biomedical ontologies. This article focuses on the challenges brought on by the convergence of biomedical ontologies, originally developed to structure databases, and linguistic data structures produced by NLP operating on unstructured or semistructured corpora. Theories upon which the convergence of NLP, biomedical informatics, and ontologies are being conducted will first be addressed, followed by a description of the properties of ontologies that make them suitable for NLP, and a succinct analysis of their readiness for use by NLP systems.
Bioinformatics pathway representations, databases, and algorithms
Authors: Karp Peter D. 
Published: April 2005

Abstract
Management of pathway information is of great importance in bioinformatics because pathways place gene products in a causal, mechanistic context showing how those gene products contribute to a larger biological process, and allow us to reason about perturbations to a living system. In the postgenomic era, pathways are also an important organizational and information‐reduction device that decreases the cognitive complexity of a biological system by allowing us to reason about a system at the level of pathways instead of at the level of proteins.
Management of pathway data is a challenging area of bioinformatics because it involves a host of representational and algorithmic problems. This article explores pathway representations and reviews existing pathway databases. It also presents a number of algorithms for computing with pathways, including algorithms for pathway prediction, algorithms for predicting which genes code for missing enzymes in metabolic pathways, algorithms for flux‐balance analysis, and algorithms for qualitatively propagating metabolites through a metabolic network.
Frame‐based systems: Protégé
Authors: Yeh Iwei 
Published: April 2005

AbstractAn ontology is the formal specification of concepts and their attributes in a particular domain of interest. Frames provide an intuitive representation structure for ontologies and knowledge bases (KB) (ontologies with specific instances of concepts and attributes). Protégé is a frame‐based Knowledge Base Management System (KBMS) that provides users with tools for knowledge modeling, knowledge acquisition, consistency checking, and concurrency control. In addition, Protégé provides tools for analysis of knowledge bases and a Java API that allows users to develop and share their own tools.
Bioconductor: software and development strategies for statistical genomics
Authors: Gentleman Robert , Carey Vincent 
Published: April 2005

Abstract
Bioconductor is an open source initiative for the creation and dissemination of methods in statistical genomics and computational biology based on R. This article describes the requirements, language features, and methodology of design and development guiding the evolution of this project. Commitments to software interoperability, computable task‐oriented documentation, and full transparency of algorithm development and use are found to be valuable in reducing barriers to access faced by statistical, computational, or biological researchers attempting interdisciplinary work. These commitments are expected to foster the propagation of standards of transparency and explicit reproducibility from wet‐lab science, where they are well accepted, to in silico biology, where explicit reproduction of important published results is often very difficult.