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Computational System Biology Reviews: 2003
(34 References)
Adams, P. D., R. W. Grosse-Kunstleve, et al. (2003). "Computational aspects of high-throughput crystallographic macromolecular structure determination." Methods Biochem Anal 44: 75-87.
Armitage, J. P., C. J. Dorman, et al. (2003). "Thinking and decision making, bacterial style: Bacterial Neural Networks, Obernai, France, 7th-12th June 2002." Mol Microbiol 47(2): 583-93.
Bacteria exhibit a bewildering range of behavioural responses and permutations of metabolic pathways for maximum exploitation of their environment. These are based on sensory perception of external and internal signals through batteries of surface and cytoplasmic receptors, evaluation of complex information flows and rapid decision making. Appreciation of the diversity of bacterial behaviour and adaptation capacities requires the study of a broad range of organisms and at this meeting we sampled more than 30 species with new findings which included the nature of gaseous receptors, advances in chemotaxis, subversion of host defences by pathogens, adaptation to high salt, community life and its obvious benefits, cell to cell communications and even the nature of bacterial circadian rhythms. With around 80 bacterial genomes now completed, and many more almost there, it was appropriate to complete the meeting with an introduction to Systems Biology and prospects for simulating the virtual cell. The versatility and seemingly 'intelligent' behaviour of bacteria will continue to fascinate, and this meeting on Bacterial Neural Networks fully reflected the excitement of this field.
Baker, N. A. and J. A. McCammon (2003). "Electrostatic interactions." Methods Biochem Anal 44: 427-40.
Bicciato, S., M. Pandin, et al. (2003). "Pattern identification and classification in gene expression data using an autoassociative neural network model." Biotechnol Bioeng 81(5): 594-606.
The application of DNA microarray technology for analysis of gene expression creates enormous opportunities to accelerate the pace in understanding living systems and identification of target genes and pathways for drug development and therapeutic intervention. Parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and the identification of molecular signatures supporting the histological classification schemes of neoplastic specimens. However, the increasing volume of data generated by microarray experiments poses the challenge of developing equally efficient methods and analysis procedures to extract, interpret, and upgrade the information content of these databases. Herein, a computational procedure for pattern identification, feature extraction, and classification of gene expression data through the analysis of an autoassociative neural network model is described. The identified patterns and features contain critical information about gene-phenotype relationships observed during changes in cell physiology. They represent a rational and dimensionally reduced base for understanding the basic biology of the onset of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of pathological states. The proposed method has been tested on two different microarray datasets-Golub's analysis of acute human leukemia [Golub et al. (1999) Science 286:531-537], and the human colon adenocarcinoma study presented by Alon et al. [1999; Proc Natl Acad Sci USA 97:10101-10106]. The analysis of the neural network internal structure allows the identification of specific phenotype markers and the extraction of peculiar associations among genes and physiological states. At the same time, the neural network outputs provide assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.
Bissantz, C., P. Bernard, et al. (2003). "Protein-based virtual screening of chemical databases. II. Are homology models of G-Protein Coupled Receptors suitable targets?" Proteins 50(1): 5-25.
The aim of the current study is to investigate whether homology models of G-Protein-Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 A-resolution X-ray structure of bovine rhodopsin, homology models of an "antagonist-bound" form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three-dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin-based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge- and pharmacophore-based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist-bound and agonist-bound states).
Bourne, P. E. (2003). "CASP and CAFASP experiments and their findings." Methods Biochem Anal 44: 501-7.
This short introductory chapter is intended simply to introduce a sense of the progress, limitations, challenges, and likely future developments in the field of protein structure prediction through what seems to be a unique scientific process. CASP and CAFASP represent a direct challenge and careful assessment of a field of study that has captured the interest of many scientists. Three of the best scientists in the field and their colleagues provide a more detailed description of the field and how it is developing in Chapters 25, 26, and 27. As prediction methods have advanced the distinction between comparative modeling, fold recognition, and novel fold recognition have blurred somewhat. It is a testament to the community that as the knowledge of the algorithms evolved, World Wide Web servers providing access to these algorithms appeared. Thus, making it relatively straightforward for any investigator to apply a melting pot of methods to the prediction process. What all approaches need are more targets and a continued refinement to the evaluation process. The first need is being met in part by the PDB, which is, with depositors' approval, releasing sequences ahead of structure release (see http://www.rcsb.org/pdb/status.html). Further, the structural genomics projects are reporting their progress for all targets on a weekly basis (see http://targetdb.pdb.org/). While there is no indication that the sequences of the latter will lead to a structure, it is a rich source of targets (17,000 in October 2002). Not only do CASP and CAFASP measure progress, they help define where efforts should be directed to move the field forward. It is a testament to how far the field has come that investigators are now turning to the unknown. Although attempting to predict a structure that will appear experimentally helps improve the methods applied to structure prediction, it does not further our understanding of living systems directly. Attempts at defining the "The Most Wanted" (Abbott, 2001)--the structures most in need of prediction to help further our understanding of the biology, and the efforts to make those predictions, speak to a healthy future for the field of protein structure prediction. To the many individuals who help define the CASP and CAFASP processes, serve the community as assesors and compete in the experiments this is a tribute.
Bruschweiler, R. (2003). "Efficient RMSD measures for the comparison of two molecular ensembles. Root-mean-square deviation." Proteins 50(1): 26-34.
Quantitative measures are presented for comparing the conformations of two molecular ensembles. The measures are based on Kabsch's formula for the root-mean-square deviation (RMSD) and the covariance matrix of atomic positions of isotropically distributed ensembles (IDE). By using a Taylor series expansion, it is shown that the RMSD can be expressed solely in terms of the IDE matrices. A fast approximate method is introduced for the pairwise RMSD determination whose computational cost scales linearly with the number of structures. A similarity measure for two structural ensembles that is based on the trace metric of the differences of powers of the IDE matrices is presented. The measures are illustrated for conformational ensembles generated by a molecular dynamics computer simulation of a partially folded A-state analog of ubiquitin.
Davidov, E., J. Holland, et al. (2003). "Advancing drug discovery through systems biology." Drug Discov Today 8(4): 175-83.
Pharmaceutical companies are facing an urgent need to both increase their lead compound and clinical candidate portfolios and satisfy market demands for continued innovation and revenue growth. Here, we outline an emerging approach that attempts to facilitate and alleviate many of the current drug discovery issues and problems. This is, in part, achieved through the systematic integration of technologies, which results in a superior output of data and information, thereby enhancing our understanding of biological function, chemico-biological interactions and, ultimately, drug discovery. Systems biology is one new discipline that is positioned to significantly impact this process.
Holcombe, M., L. Holcombe, et al. (2003). "A hybrid machine model of rice blast fungus, Magnaporthe grisea." Biosystems 68(2-3): 223-8.
The fungus, Magnaporthe grisea (Rice blast fungus) is a major agricultural problem affecting rice and related food crops. The way that the fungus invades the host plant and propagates itself is a very important scientific problem and recent advances in research into the genetic basis of these processes can be used to build a simple partial model using hybrid computational modelling techniques. The possible potential benefits of doing this include the use of computer simulation and automated analysis through techniques such as model checking to understand the complex behaviour of such systems. The example is a metaphor for the process of trying to integrate and understand much of the vast amounts of genomic and other data that is being produced in current molecular biology research.
Hood, L. and D. Galas (2003). "The digital code of DNA." Nature 421(6921): 444-8.
The discovery of the structure of DNA transformed biology profoundly, catalysing the sequencing of the human genome and engendering a new view of biology as an information science. Two features of DNA structure account for much of its remarkable impact on science: its digital nature and its complementarity, whereby one strand of the helix binds perfectly with its partner. DNA has two types of digital information--the genes that encode proteins, which are the molecular machines of life, and the gene regulatory networks that specify the behaviour of the genes.
Horn, F., E. Bettler, et al. (2003). "GPCRDB information system for G protein-coupled receptors." Nucleic Acids Res 31(1): 294-7.
The GPCRDB is a molecular class-specific information system that collects, combines, validates and disseminates heterogeneous data on G protein-coupled receptors (GPCRs). The database stores data on sequences, ligand binding constants and mutations. The system also provides computationally derived data such as sequence alignments, homology models, and a series of query and visualization tools. The GPCRDB is updated automatically once every 4-5 months and is freely accessible at http://www.gpcr.org/7tm/.
Hucka, M., A. Finney, et al. (2003). "The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models." Bioinformatics 19(4): 524-31.
MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/
Kijak, G. H., A. E. Rubio, et al. (2003). "Discrepant results in the interpretation of HIV-1 drug-resistance genotypic data among widely used algorithms." HIV Med 4(1): 72-8.
OBJECTIVES: The aim of this study was to assess the concordance on the interpretation of HIV-1 drug-resistance genotypic data by three widely used algorithms: Stanford University Database (SU), TruGene (Visible Genetics, Canada) (VG) and VirtualPhenotype (Virco, Belgium) (VP). METHODS: Genotypic data from 293 HIV-1-infected individuals with treatment failure was interpreted for 14 antiretroviral drugs by the three algorithms. RESULTS: Complete concordant results among the three systems for all the drugs studied were found in 40/293 (13.7%) samples. Low concordance in the interpretation was observed for most nucleoside reverse transcriptase inhibitors (NRTIs), while results agreed highly for all nonnucleoside reverse transcriptase inhibitors (NNRTIs) and most protease inhibitors (PIs). In pair-wise comparisons, discordant interpretations between SU and VP were found in over 50% of the samples for didanosine, zalcitabine, stavudine and abacavir, and the level of disagreement between VG and VP exceeded 40% for the same drugs. Major discrepancies (high-level resistance interpretation by one algorithm with sensitive interpretation by another) were observed between VG and VP in over 10% of the cases for didanosine, zalcitabine, stavudine and abacavir. On the other hand, the three algorithms had concordant results for lamivudine in over 90% of the cases. CONCLUSIONS: This work demonstrates the great level of discordance in the interpretation of genotyping results among algorithms, clearly showing the necessity for clinical validation. Moreover, these results suggest that a joint effort from the scientific community as well as national and international HIV societies is needed to achieve a consensus for the interpretation of genotypic data.
Kim, J. T., T. Martinetz, et al. (2003). "Bioinformatic principles underlying the information content of transcription factor binding sites." J Theor Biol 220(4): 529-44.
Empirically, it has been observed in several cases that the information content of transcription factor binding site sequences (R(sequence)) approximately equals the information content of binding site positions (R(frequency)). A general framework for formal models of transcription factors and binding sites is developed to address this issue. Measures for information content in transcription factor binding sites are revisited and theoretic analyses are compared on this basis. These analyses do not lead to consistent results. A comparative review reveals that these inconsistent approaches do not include a transcription factor state space. Therefore, a state space for mathematically representing transcription factors with respect to their binding site recognition properties is introduced into the modelling framework. Analysis of the resulting comprehensive model shows that the structure of genome state space favours equality of R(sequence) and R(frequency) indeed, but the relation between the two information quantities also depends on the structure of the transcription factor state space. This might lead to significant deviations between R(sequence) and R(frequency). However, further investigation and biological arguments show that the effects of the structure of the transcription factor state space on the relation of R(sequence) and R(frequency) are strongly limited for systems which are autonomous in the sense that all DNA-binding proteins operating on the genome are encoded in the genome itself. This provides a theoretical explanation for the empirically observed equality.
Liu, Z., L. Jiang, et al. (2003). "Beyond the rotamer library: genetic algorithm combined with the disturbing mutation process for upbuilding protein side-chains." Proteins 50(1): 49-62.
The disturbing genetic algorithm, incorporating the disturbing mutation process into the genetic algorithm flow, has been developed to extend the searching space of side-chain conformations and to improve the quality of the rotamer library. Moreover, the growing generation amount idea, simulating the real situation of the natural evolution, is introduced to improve the searching speed. In the calculations using the pseudo energy scoring function of the root mean squared deviation, the disturbing genetic algorithm method has been shown to be highly efficient. With the real energy function based on AMBER force field, the program has been applied to rebuilding side-chain conformations of 25 high-quality crystallographic structures of single-protein and protein-protein complexes. The averaged root mean standard deviation of atom coordinates in side-chains and veracities of the torsion angles of chi(1) and chi(1) + chi(2) are 1.165 A, 88.2 and 72.9% for the buried residues, respectively, and 1.493 A, 79.2 and 64.7% for all residues, showing that the method has equal precision to the program SCWRL, whereas it performs better in the prediction of buried residues and protein-protein interfaces. This method has been successfully used in redesigning the interface of the Basnase-Barstar complex, indicating that it will have extensive application in protein design, protein sequence and structure relationship studies, and research on protein-protein interaction.
Markley, J. L., E. L. Ulrich, et al. (2003). "Macromolecular structure determination by NMR spectroscopy." Methods Biochem Anal 44: 89-113.
Miller, E. S., E. Kutter, et al. (2003). "Bacteriophage T4 genome." Microbiol Mol Biol Rev 67(1): 86-156, table of contents.
Phage T4 has provided countless contributions to the paradigms of genetics and biochemistry. Its complete genome sequence of 168,903 bp encodes about 300 gene products. T4 biology and its genomic sequence provide the best-understood model for modern functional genomics and proteomics. Variations on gene expression, including overlapping genes, internal translation initiation, spliced genes, translational bypassing, and RNA processing, alert us to the caveats of purely computational methods. The T4 transcriptional pattern reflects its dependence on the host RNA polymerase and the use of phage-encoded proteins that sequentially modify RNA polymerase; transcriptional activator proteins, a phage sigma factor, anti-sigma, and sigma decoy proteins also act to specify early, middle, and late promoter recognition. Posttranscriptional controls by T4 provide excellent systems for the study of RNA-dependent processes, particularly at the structural level. The redundancy of DNA replication and recombination systems of T4 reveals how phage and other genomes are stably replicated and repaired in different environments, providing insight into genome evolution and adaptations to new hosts and growth environments. Moreover, genomic sequence analysis has provided new insights into tail fiber variation, lysis, gene duplications, and membrane localization of proteins, while high-resolution structural determination of the "cell-puncturing device," combined with the three-dimensional image reconstruction of the baseplate, has revealed the mechanism of penetration during infection. Despite these advances, nearly 130 potential T4 genes remain uncharacterized. Current phage-sequencing initiatives are now revealing the similarities and differences among members of the T4 family, including those that infect bacteria other than Escherichia coli. T4 functional genomics will aid in the interpretation of these newly sequenced T4-related genomes and in broadening our understanding of the complex evolution and ecology of phages-the most abundant and among the most ancient biological entities on Earth.
Nordman, N., J. Valjakka, et al. (2003). "Analysis of the binding energies of testosterone, 5alpha-dihydrotestosterone, androstenedione and dehydroepiandrosterone sulfate with an antitestosterone antibody." Proteins 50(1): 135-43.
Molecular dynamics simulations and molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) free energy calculations were used to study the binding of testosterone (TES), 5alpha-dihydrotestosterone (5ADHT), androstenedione (AND), and dehydroepiandrosterone sulfate (DHEAS) to the monoclonal antitestosterone antibody 3-C(4)F(5). The relative binding free energy of TES and AND was also calculated with free energy perturbation (FEP) simulations. The antibody 3-C(4)F(5) has a relatively high affinity (3 x 10(8) M(-1)) and on overall good binding profile for testosterone but its cross-reactivity with DHEAS has been the main reason for the failure to use this antibody in clinical immunoassays. The relative binding free energies obtained with the MM-PBSA method were 1.5 kcal/mol for 5ADHT, 3.8 kcal/mol for AND, and 4.3 kcal/mol for DHEAS, as compared to TES. When a water molecule of the ligand binding site, observed in the antibody-TES crystal structure, was explicitly included in MM-PBSA calculations, the relative binding energies were 3.4, 4.9, and 5.4 kcal/mol for 5ADHT, AND, and DHEAS, respectively. The calculated numbers are in correct order but larger than the corresponding experimental energies of 1.3, 1.5, and 2.6 kcal/mol, respectively. The fact that the MM-PBSA method reproduced the relative binding free energies of DHEAS, a steroid having a negatively charged sulfate group, and the neutrally charged TES, 5ADHT, and AND in satisfactory agreement with experiment shows the robustness of the method in predicting relative binding affinities. The 800-ps FEP simulations predicted that the antibody 3-C(4)F(5) binds TES 1.3 kcal/mol tighter than AND. Computational mutagenesis of selected amino acid residues of the ligand binding site revealed that the lower affinities of AND and DHEAS as compared to TES are due to a combined effect of several residues, each contributing a small fraction to the tighter binding of TES. An exception to this is Tyr99H, whose mutation to Ala lowered the binding of DHEAS 0.7 kcal/mol more than the binding of TES. This is probably due to the hydrogen bonding interaction formed between the OH group of Tyr99H and the sulfate group of DHEAS. Computational mutagensis data also showed that the affinity of the steroids to the antitestosterone antibody 3-C(4)F(5) would be enhanced if Trp47H were repositioned so that it would make more extensive contacts with the bound ligands. In addition, the binding of steroids to antitestosterone, antiprogesterone, and antiestradiol antibodies is discussed.
Overbeek, R., N. Larsen, et al. (2003). "The ERGO genome analysis and discovery system." Nucleic Acids Res 31(1): 164-71.
The ERGO (http://ergo.integratedgenomics.com/ERGO/) genome analysis and discovery suite is an integration of biological data from genomics, biochemistry, high-throughput expression profiling, genetics and peer-reviewed journals to achieve a comprehensive analysis of genes and genomes. Far beyond any conventional systems that facilitate functional assignments, ERGO combines pattern-based analysis with comparative genomics by visualizing genes within the context of regulation, expression profiling, phylogenetic clusters, fusion events, networked cellular pathways and chromosomal neighborhoods of other functionally related genes. The result of this multifaceted approach is to provide an extensively curated database of the largest available integration of genomes, with a vast collection of reconstructed cellular pathways spanning all domains of life. Although access to ERGO is provided only under subscription, it is already widely used by the academic community. The current version of the system integrates 500 genomes from all domains of life in various levels of completion, 403 of which are available for subscription.
Patterson, S. D. and R. H. Aebersold (2003). "Proteomics: the first decade and beyond." Nat Genet 33 Suppl: 311-23.
Proteomics is the systematic study of the many and diverse properties of proteins in a parallel manner with the aim of providing detailed descriptions of the structure, function and control of biological systems in health and disease. Advances in methods and technologies have catalyzed an expansion of the scope of biological studies from the reductionist biochemical analysis of single proteins to proteome-wide measurements. Proteomics and other complementary analysis methods are essential components of the emerging 'systems biology' approach that seeks to comprehensively describe biological systems through integration of diverse types of data and, in the future, to ultimately allow computational simulations of complex biological systems.
Pey, A. L., L. R. Desviat, et al. (2003). "Phenylketonuria: genotype-phenotype correlations based on expression analysis of structural and functional mutations in PAH." Hum Mutat 21(4): 370-8.
When analyzed in the context of the phenylalanine hydroxylase (PAH) three-dimensional structure, only a minority of the PKU mutations described world-wide affect catalytic residues. Consistent with these observations, recent data point to defective folding and subsequent aggregation/degradation as a predominant disease mechanism for several mutations. In this work, we use a combined approach of expression in eukaryotic cells at different temperatures and a prokaryotic system with co-expression of chaperonins to elucidate and confirm structural consequences for 18 PKU mutations. Three mutations are located in the amino terminal regulatory domain and 15 in the catalytic domain. Four mutations were found to abolish the specific activity in all conditions. Two are catalytic mutations (Y277D and E280K) and two are severe structural defects (IVS10-11G>A and L311P). All the remaining mutations (D59Y, I65T, E76G, P122Q, R158Q, G218V, R243Q, P244L, R252W, R261Q, A309V, R408Q, R408W, and Y414C) are folding defects causing reduced stability and accelerated degradation, although some of them probably affect residues involved in regulation. In these cases, we have demonstrated that the amount of mutant PAH protein and residual activity could be modulated by in vitro experimental conditions, and therefore the observed in vivo metabolic variation may be explained by interindividual variation in the quality control systems. The results derived provide an experimental framework to define the mutation severity relating genotype to phenotype. They also explain the observed inconsistencies for some mutations in patients with similar genotype and different phenotypes.
Ressom, H., R. Reynolds, et al. (2003). "Increasing the efficiency of fuzzy logic-based gene expression data analysis." Physiol Genomics 13(2): 107-17.
DNA microarray technology can accommodate a multifaceted analysis of the expression of genes in an organism. The wealth of spatiotemporal data generated by this technology allows researchers to potentially reverse engineer a particular genetic network. "Fuzzy logic" has been proposed as a method to analyze the relationships between genes and help decipher a genetic network. This method can identify interacting genes that fit a known "fuzzy" model of gene interaction by testing all combinations of gene expression profiles. This paper introduces improvements made over previous fuzzy gene regulatory models in terms of computation time and robustness to noise. Improvement in computation time is achieved by using a cluster analysis as a preprocessing method to reduce the total number of gene combinations analyzed. This approach speeds up the algorithm by a factor of 50% with minimal effect on the results. The model's sensitivity to noise is reduced by implementing appropriate methods of "fuzzy rule aggregation" and "conjunction" that produce reliable results in the face of minor changes in model input.
Saito, R., H. Suzuki, et al. (2003). "Global insights into protein complexes through integrated analysis of the reliable interactome and knockout lethality." Biochem Biophys Res Commun 301(3): 633-40.
We performed an integrated computational analysis of data derived from a comprehensive set of protein-protein interactions (interactome) and a phenotype dataset on lethality in Saccharomyces cerevisiae. For the analysis, we selected reliable interactome data using our previous 'interaction generality,' a computational approach to assess reliability of interactions. Those efforts gave clear evidence that proteins with lethal phenotypes in knockout studies (lethal proteins) may interact with each other to form functional protein complexes to perform their cellular roles. However, our analysis indicates that interactions between lethal proteins are rather restricted to the same cellular pathway or function, and it is quite unlikely that they interact with other lethal proteins functioning in different cellular roles. Furthermore, our results allowed us predictions on the functions of thus far uncharacterized lethal proteins with an estimated 93% accuracy. Thus, the analysis described in here can provide global insights into the biological features of the protein complexes.
Steven Wiley, H., S. Y. Shvartsman, et al. (2003). "Computational modeling of the EGF-receptor system: a paradigm for systems biology." Trends Cell Biol 13(1): 43-50.
Computational models have rarely been used as tools by biologists but, when models provide experimentally testable predictions, they can be extremely useful. The epidermal growth factor receptor (EGFR) is probably the best-understood receptor system, and computational models have played a significant part in its elucidation. For many years, models have been used to analyze EGFR dynamics and to interpret mutational studies, and are now being used to understand processes including signal transduction, autocrine loops and developmental patterning. The success of EGFR modeling can be a guide to combining models and experiments productively to understand complex biological processes as integrated systems.
Tate, J. (2003). "Molecular visualization." Methods Biochem Anal 44: 135-58.
Teuscher, C., D. Mange, et al. (2003). "Bio-inspired computing tissues: towards machines that evolve, grow, and learn." Biosystems 68(2-3): 235-44.
Biological inspiration in the design of computing machines could allow the creation of new machines with promising characteristics such as fault-tolerance, self-replication or cloning, reproduction, evolution, adaptation and learning, and growth. The aim of this paper is to introduce bio-inspired computing tissues that might constitute a key concept for the implementation of 'living' machines. We first present a general overview of bio-inspired systems and the POE model that classifies bio-inspired machines along three axes. The Embryonics project-inspired by some of the basic processes of molecular biology-is described by means of the BioWatch application, a fault-tolerant and self-repairable watch. The main characteristics of the Embryonics project are the multicellular organization, the cellular differentiation, and the self-repair capabilities. The BioWall is intended as a reconfigurable computing tissue, capable of interacting with its environment by means of a large number of touch-sensitive elements coupled with a color display. For illustrative purposes, a large-scale implementation of the BioWatch on the BioWall's computational tissue is presented. We conclude the paper with a description of bio-inspired computing tissues and POEtic machines.
Torres, J., T. J. Stevens, et al. (2003). "Membrane proteins: the 'Wild West' of structural biology." Trends Biochem Sci 28(3): 137-44.
Historically, the task of determining the structure of membrane proteins has been hindered by experimental difficulties associated with their lipid-embedded domains. Here, we provide an overview of recently developed experimental and predictive tools that are changing our view of this largely unexplored territory - the 'Wild West' of structural biology. Crystallography, single-particle methods and atomic force microscopy are being used to study huge membrane proteins with increasing detail. Solid-state nuclear magnetic resonance strategies provide orientational constraints for structure determination of transmembrane (TM) alpha-helices and accurate measurements of intramolecular distances, even in very complex systems. Longer distance constraints are determined by site-directed spin-labelling electron paramagnetic resonance, but current labelling strategies still constitute some limitation. Other methods, such as site-specific infrared dichroism, enable orientational analysis of TM alpha-helices in aligned bilayers and, combined with novel computational and predictive tools that use evolutionary conservation data, are being used to analyze TM alpha-helical bundles.
Valencia, A. and F. Pazos (2003). "Prediction of protein-protein interactions from evolutionary information." Methods Biochem Anal 44: 411-26.
Volkmann, N. and D. Hanein (2003). "Electron microscopy." Methods Biochem Anal 44: 115-33.
Wang, T. and R. C. Wade (2003). "Implicit solvent models for flexible protein-protein docking by molecular dynamics simulation." Proteins 50(1): 158-69.
The suitability of three implicit solvent models for flexible protein-protein docking by procedures using molecular dynamics simulation is investigated. The three models are (i) the generalized Born (GB) model implemented in the program AMBER6.0; (ii) a distance-dependent dielectric (DDD) model; and (iii) a surface area-dependent model that we have parameterized and call the NPSA model. This is a distance-dependent dielectric model modified by neutralizing the ionizable side-chains and adding a surface area-dependent solvation term. These solvent models were first tested in molecular dynamics simulations at 300 K of the native structures of barnase, barstar, segment B1 of protein G, and three WW domains. These protein structures display a range of secondary structure contents and stabilities. Then, to investigate the performance of the implicit solvent models in protein docking, molecular dynamics simulations of barnase/barstar complexation, as well as PIN1 WW domain/peptide complexation, were conducted, starting from separated unbound structures. The simulations show that the NPSA model has significant advantages over the DDD and GB models in maintaining the native structures of the proteins and providing more accurate docked complexes.
Weiss, J. N., Z. Qu, et al. (2003). "Understanding biological complexity: lessons from the past." Faseb J 17(1): 1-6.
Advances in molecular biology now permit complex biological systems to be tracked at an exquisite level of detail. The information flow is so great, however, that using intuition alone to draw connections is unrealistic. Thus, the need to integrate mathematical biology with experimental biology is greater than ever. To achieve this integration, obstacles that have traditionally prevented effective communication between theoreticians and experimentalists must be overcome, so that experimentalists learn the language of mathematics and dynamical modeling and theorists learn the language of biology. Fifty years ago Alan Hodgkin and Andrew Huxley published their quantitative model of the nerve action potential; in the same year, Alan Turing published his work on pattern formation in activator-inhibitor systems. These classic studies illustrate two ends of the spectrum in mathematical biology: the detailed model approach and the minimal model approach. When combined, they are highly synergistic in analyzing the mechanisms underlying the behavior of complex biological systems. Their effective integration will be essential for unraveling the physical basis of the mysteries of life.
Werner, E. (2003). "Meeting report: in silico cell signaling underground." Sci STKE 2003(170): PE8.
Cell signaling is becoming a darling of systems biology. Because cell signaling is a relatively well understood, complex, but not overwhelming area of biology, it has become an attractive focal point for computational modeling and simulation efforts by engineers, computer scientists, mathematicians, and systems biologists. The flow of information within and between cells points to a possible conceptual and theoretical kinship with similar phenomena in computer networks, communication theory, information theory, and information flow in engineered systems. Although still in its infancy, the field is experiencing a growth in interest, as evidenced by the poster sessions at the recent International Conference on Systems Biology (ICSB 2002). This report focuses on some of those efforts to mathematically model and simulate cell signaling and signal transduction networks.
Westbrook, J. D. and P. M. Fitzgerald (2003). "The PDB format, mmCIF, and other data formats." Methods Biochem Anal 44: 161-79.
Yang, Y. S., H. D. Song, et al. (2003). "The gene expression profiling of human visceral adipose tissue and its secretory functions." Biochem Biophys Res Commun 300(4): 839-46.
In order to fully understand the physiological functions of adipose tissue, especially its secretory functions, and to provide a basis for the identification of novel obesity related genes, the gene expression profiling of human visceral adipose tissue was established by using cDNA array. 33P-labelled cDNA, derived from visceral adipose tissue total RNA, was hybridized to a cDNA array containing over 16,000 expressed sequence-tagged clones which represent human singleton genes. The expressed sequence tag (EST) was considered to be expressed in visceral adipose tissue when the ratio of signal to noise was greater than or equal to 2. The results were analyzed with bioinformatics. Totally, 8230 genes were found to be expressed in visceral adipose tissue with 5200 known genes and 3030 known ESTs. Most of 84 secretory proteins, 120 receptors, and 74 transcription factors expressed in adipose tissue were newly identified. Many appetite-regulating related peptides or receptors and some reproduction-related genes were first found to be expressed in adipose tissue. Eight autocrine/paracrine systems were described for the first time in the visceral adipose tissue. These results clearly demonstrate that the visceral adipose tissue has important secretory functions and there is a complex local autocrine/paracrine regulatory network. The present work suggests that the visceral adipose tissue is an important component of the neuroendocrine-immune network and plays an important role in regulating appetite not only via endocrine but also via autocrine/paracrine systems. The visceral adipose tissue might also play a role in regulating reproduction and sexual function.