Academic literature on the topic 'Biological networks, simulation, structural analysis, dynamic analysis'
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Journal articles on the topic "Biological networks, simulation, structural analysis, dynamic analysis"
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REMONDINI, D., N. NERETTI, C. FRANCESCHI, P. TIERI, J. M. SEDIVY, L. MILANESI, and G. C. CASTELLANI. "NETWORKS FROM GENE EXPRESSION TIME SERIES: CHARACTERIZATION OF CORRELATION PATTERNS." International Journal of Bifurcation and Chaos 17, no. 07 (July 2007): 2477–83. http://dx.doi.org/10.1142/s0218127407018543.
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We address the problem of finding large-scale functional and structural relationships between genes, given a time series of gene expression data, namely mRNA concentration values measured from genetically engineered rat fibroblasts cell lines responding to conditional cMyc proto-oncogene activation. We show how it is possible to retrieve suitable information about molecular mechanisms governing the cell response to conditional perturbations. This task is complex because typical high-throughput genomics experiments are performed with high number of probesets (103–104 genes) and a limited number of observations (< 102 time points). In this paper, we develop a deepest analysis of our previous work [Remondini et al., 2005] in which we characterized some of the main features of a gene-gene interaction network reconstructed from temporal correlation of gene expression time series. One first advancement is based on the comparison of the reconstructed network with networks obtained from randomly generated data, in order to characterize which features retrieve real biological information, and which are instead due to the characteristics of the network reconstruction method. The second and perhaps more relevant advancement is the characterization of the global change in co-expression pattern following cMyc activation as compared to a basal unperturbed state. We propose an analogy with a physical system in a critical state close to a phase transition (e.g. Potts ferromagnet), since the cell responds to the stimulus with high susceptibility, such that a single gene activation propagates to almost the entire genome. Our result is relative to temporal properties of gene network dynamics, and there are experimental evidence that this can be related to spatial properties regarding the global organization of chromatine structure [Knoepfler et al., 2006].
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Найденов and E. Naydenov. "Development micromachined cyber platforms to cultive endothelial сapillary networks in vitro in the space organized microflows nutrient medium." Journal of New Medical Technologies. eJournal 9, no. 2 (July 6, 2015): 0. http://dx.doi.org/10.12737/10746.
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This work is devoted to the development of technology and special equipment for the cultivation of spontaneously developing functioning endothelial capillary networks in vitro as the basis of artificial cloth-like structures with desired biological properties. It is the scientific and engineering projects RFBR №94-04-13544 «Structural analysis of microvascular bifurcations" and №96-04-50991 «Cell and Tissue Engineering endothelium (formation in endothelial culture in vitro the functioning self-developing capillary networks)." The proposed technology allows the author to form three-dimensional capillary endothelial network around micro-fluidic arrays, immersed in a specially designed dynamic gel. In 2013, the Korean research team under the lea-dership Noo Li Jeon has reproduced, using a similar approach, the phenomenon of self-developing functioning endothelial capillary networks with mass transfer in vitro. It has fully confirmed the validity of the concept pro-posed in the listed projects. Using system of the mathematical modeling Matlab & Simulink and system engi-neering design Cadence Orcad it was developed simulation mathematical model and circuit diagrams experimental reactor modules, it allows to saving considerable financial resources allocated to research and de-velopment of this kind. The resulting model contains 5.4 million basic Simulink blocks and performs more than 7,000 different mathematical functions, reflecting the behavior of devices in stationary and non-stationary conditions. Device control is based on neural network technology. Portable stand-alone microcomputers cyber platform includes microfluidic matrix, generators of microflows liquid phase nutrient medium, life-support systems of endothelial culture system of automatic digital imaging process of angiogenesis, the transmission system of encrypted data over a secure radio, digital control systems. All systems are backed up multiple times, allowing the product to operate in stand-alone mode for a long time (up to a year or more).
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Zhuravlev, Pavel I., and Garegin A. Papoian. "Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework." Quarterly Reviews of Biophysics 43, no. 3 (August 2010): 295–332. http://dx.doi.org/10.1017/s0033583510000119.
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AbstractEnergy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein – the native landscape – is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase volume is much smaller, allowing a protein to fully sample its native energy landscape on the biological timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a ‘topographical map’ of the native landscape that can be used for subsequent analysis. Several major approaches to representing this topographical map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein's functional dynamics. Normal mode analysis can provide such variables in cases where functional motions are dictated by the molecule's architecture. Principal component analysis is a more expensive way of inferring the essential variables from the protein's motions, one that requires a long molecular dynamics simulation. Finally, the two popular models for the allosteric switching mechanism, ‘preexisting equilibrium’ and ‘induced fit’, are interpreted within the energy landscape paradigm as extreme points of a continuum of transition mechanisms. Some experimental evidence illustrating each of these two models, as well as intermediate mechanisms, is presented and discussed.
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Muhseen, Ziyad Tariq, Salim Kadhim, Yahiya Ibrahim Yahiya, Eid A. Alatawi, Faris F. Aba Alkhayl, and Ahmad Almatroudi. "Insights into the Binding of Receptor-Binding Domain (RBD) of SARS-CoV-2 Wild Type and B.1.620 Variant with hACE2 Using Molecular Docking and Simulation Approaches." Biology 10, no. 12 (December 10, 2021): 1310. http://dx.doi.org/10.3390/biology10121310.
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Recently, a new variant, B.1620, with mutations (S477N-E484K) in the spike protein’s receptor-binding domain (RBD) has been reported in Europe. In order to design therapeutic strategies suitable for B.1.620, further studies are required. A detailed investigation of the structural features and variations caused by these substitutions, that is, a molecular level investigation, is essential to uncover the role of these changes. To determine whether and how the binding affinity of ACE2–RBD is affected, we used protein–protein docking and all-atom simulation approaches. Our analysis revealed that B.1.620 binds more strongly than the wild type and alters the hydrogen bonding network. The docking score for the wild type was reported to be −122.6 +/− 0.7 kcal/mol, while for B.1.620, the docking score was −124.9 +/− 3.8 kcal/mol. A comparative binding investigation showed that the wild-type complex has 11 hydrogen bonds and one salt bridge, while the B.1.620 complex has 14 hydrogen bonds and one salt bridge, among which most of the interactions are preserved between the wild type and B.1.620. A dynamic analysis of the two complexes revealed stable dynamics, which corroborated the global stability trend, compactness, and flexibility of the three essential loops, providing a better conformational optimization opportunity and binding. Furthermore, binding free energy revealed that the wild type had a total binding energy of −51.14 kcal/mol, while for B.1.628, the total binding energy was −68.25 kcal/mol. The current findings based on protein complex modeling and bio-simulation methods revealed the atomic features of the B.1.620 variant harboring S477N and E484K mutations in the RBD and the basis for infectivity. In conclusion, the current study presents distinguishing features of B.1.620, which can be used to design structure-based drugs against the B.1.620 variant.
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Scaramozzino, Domenico, Giuseppe Lacidogna, Gianfranco Piana, and Alberto Carpinteri. "Numerical Evaluation of Protein Global Vibrations at Terahertz Frequencies by Means of Elastic Lattice Models." Proceedings 67, no. 1 (November 9, 2020): 8. http://dx.doi.org/10.3390/asec2020-07518.
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Proteins represent one of the most important building blocks for most biological processes. Their biological mechanisms have been found to correlate significantly with their dynamics, which is commonly investigated through molecular dynamics (MD) simulations. However, important insights on protein dynamics and biological mechanisms have also been obtained via much simpler and computationally efficient calculations based on elastic lattice models (ELMs). The application of structural mechanics approaches, such as modal analysis, to the protein ELMs has allowed to find impressive results in terms of protein dynamics and vibrations. The low-frequency vibrations extracted from the protein ELM are usually found to occur within the terahertz (THz) frequency range and correlate fairly accurately with the observed functional motions. In this contribution, the global vibrations of lysozyme will be investigated by means of a finite element (FE) truss model, and we will show that there exists complete consistency between the proposed FE approach and one of the more well-known ELMs for protein dynamics, the anisotropic network model (ANM). The proposed truss model can consequently be seen as a simple method, easily accessible to the structural mechanics community members, to analyze protein vibrations and their connections with the biological activity.
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Li, Quan, Ray Luo, and Hai-Feng Chen. "Dynamical important residue network (DIRN): network inference via conformational change." Bioinformatics 35, no. 22 (April 30, 2019): 4664–70. http://dx.doi.org/10.1093/bioinformatics/btz298.
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Abstract Motivation Protein residue interaction network has emerged as a useful strategy to understand the complex relationship between protein structures and functions and how functions are regulated. In a residue interaction network, every residue is used to define a network node, adding noises in network post-analysis and increasing computational burden. In addition, dynamical information is often necessary in deciphering biological functions. Results We developed a robust and efficient protein residue interaction network method, termed dynamical important residue network, by combining both structural and dynamical information. A major departure from previous approaches is our attempt to identify important residues most important for functional regulation before a network is constructed, leading to a much simpler network with the important residues as its nodes. The important residues are identified by monitoring structural data from ensemble molecular dynamics simulations of proteins in different functional states. Our tests show that the new method performs well with overall higher sensitivity than existing approaches in identifying important residues and interactions in tested proteins, so it can be used in studies of protein functions to provide useful hypotheses in identifying key residues and interactions. Supplementary information Supplementary data are available at Bioinformatics online.
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Smith, Jeremy C., Pan Tan, Loukas Petridis, and Liang Hong. "Dynamic Neutron Scattering by Biological Systems." Annual Review of Biophysics 47, no. 1 (May 20, 2018): 335–54. http://dx.doi.org/10.1146/annurev-biophys-070317-033358.
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Dynamic neutron scattering directly probes motions in biological systems on femtosecond to microsecond timescales. When combined with molecular dynamics simulation and normal mode analysis, detailed descriptions of the forms and frequencies of motions can be derived. We examine vibrations in proteins, the temperature dependence of protein motions, and concepts describing the rich variety of motions detectable using neutrons in biological systems at physiological temperatures. New techniques for deriving information on collective motions using coherent scattering are also reviewed.
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Haiman, Zachary B., Daniel C. Zielinski, Yuko Koike, James T. Yurkovich, and Bernhard O. Palsson. "MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics." PLOS Computational Biology 17, no. 1 (January 28, 2021): e1008208. http://dx.doi.org/10.1371/journal.pcbi.1008208.
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Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.
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Selvaraj, Gurudeeban, Satyavani Kaliamurthi, Gilles H. Peslherbe, and Dong-Qing Wei. "Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches." F1000Research 10 (February 18, 2021): 127. http://dx.doi.org/10.12688/f1000research.50850.1.
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Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network. Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target “ERBB4” and candidate drug “Wortmannin” provide insights on the possible personalized therapeutics for emerging COVID-19.
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Selvaraj, Gurudeeban, Satyavani Kaliamurthi, Gilles H. Peslherbe, and Dong-Qing Wei. "Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches." F1000Research 10 (April 6, 2021): 127. http://dx.doi.org/10.12688/f1000research.50850.2.
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Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network. Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target “ERBB4” and candidate drug “Wortmannin” provide insights on the possible personalized therapeutics for emerging COVID-19.
Dissertations / Theses on the topic "Biological networks, simulation, structural analysis, dynamic analysis"
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Caligola, Simone. "Computational Techniques for the Structural and Dynamic Analysis of Biological Networks." Doctoral thesis, 2020. http://hdl.handle.net/11562/1016035.
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The analysis of biological systems involves the study of networks from different omics such as genomics, transcriptomics, metabolomics and proteomics. In general, the computational techniques used in the analysis of biological networks can be divided into those that perform (i) structural analysis, (ii) dynamic analysis of structural prop- erties and (iii) dynamic simulation. Structural analysis is related to the study of the topology or stoichiometry of the biological network such as important nodes of the net- work, network motifs and the analysis of the flux distribution within the network. Dy- namic analysis of structural properties, generally, takes advantage from the availability of interaction and expression datasets in order to analyze the structural properties of a biological network in different conditions or time points. Dynamic simulation is useful to study those changes of the biological system in time that cannot be derived from a structural analysis because it is required to have additional information on the dynamics of the system. This thesis addresses each of these topics proposing three computational techniques useful to study different types of biological networks in which the structural and dynamic analysis is crucial to answer to specific biological questions. In particu- lar, the thesis proposes computational techniques for the analysis of the network motifs of a biological network through the design of heuristics useful to efficiently solve the subgraph isomorphism problem, the construction of a new analysis workflow able to integrate interaction and expression datasets to extract information about the chromo- somal connectivity of miRNA-mRNA interaction networks and, finally, the design of a methodology that applies techniques coming from the Electronic Design Automation (EDA) field that allows the dynamic simulation of biochemical interaction networks and the parameter estimation.
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Garaga, Arunakumari. "Factors Affecting The Static And Dynamic Response Of Jointed Rock Masses." Thesis, 2008. http://hdl.handle.net/2005/772.
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Infrastructure is developing at an extremely fast pace which includes construction of metros, underground storage places, railway bridges, caverns and tunnels. Very often these structures are found in or on the rock masses. Rock masses are seldom found in nature without joints or discontinuities. Jointed rocks are characterized by the presence of inherent discontinuities of varied sizes with different orientations and intensities, which can have significant effect on their mechanical response. Constructions involving jointed rocks often become challenging jobs for Civil Engineers as the instability of slopes or excavations in these jointed rocks poses serious concerns, sometimes leading to the failure of structures built on them. Experimental investigations on jointed rock masses are not always feasible and pose formidable problems to the engineers. Apart from the technical difficulties of extracting undisturbed rock samples, it is very expensive and time consuming to conduct the experiments on jointed rock masses of huge dimensions. The most popular methods of evaluating the rock mass behaviour are the Numerical methods. In this thesis, numerical modelling of jointed rock masses is carried out using computer program FLAC (Fast Lagrangian Analysis of Continua).
The objective of the present study is to study the effect of various joint parameters on the response of jointed rock masses in static as well as seismic shaking conditions. This is achieved through systematic series of numerical simulations of jointed rocks in triaxial compression, in underground openings and in large rock slopes. This thesis is an attempt to study the individual effect of different joint parameters on the rock mass behaviour and to integrate these results to provide useful insight into the behaviour of jointed rock mass under various joint conditions.
In practice, it is almost impossible to explore all of the joint systems or to investigate all their mechanical characteristics and implementing them explicitly in the model. In these cases, the use of the equivalent continuum model to simulate the behaviour of jointed rock masses could be valuable. Hence this approach is mainly used in this thesis. Some numerical simulations with explicitly modelled joints are also presented for comparison with the continuum modelling. The applicability of Artificial Neural Networks for the prediction of stress-strain response of jointed rocks is also explored. Static, pseudo-static and dynamic analyses of a large rock slope in Himalayas is carried out and parametric seismic analysis of rock slope is carried out with varying input shaking, material damping and shear strength parameters.
Results from the numerical studies showed that joint inclination is the most influencing parameter for the jointed rock mass behaviour. Rock masses exhibit lowest strength at critical angle of joint inclination and the deformations around excavations will be highest when the joints are inclined at an angle close to the critical angle. However at very high confining pressures, the influence of joint inclination gets subdued. Under seismic base shaking conditions, the deformations of rock masses largely depend on the acceleration response with time, frequency content and duration rather than the peak amplitude or the magnitude of earthquake. All these aspects are discussed in the light of results from numerical studies presented in this thesis.
Books on the topic "Biological networks, simulation, structural analysis, dynamic analysis"
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Zaheer Ul-Haq and Angela K. Wilson, eds. Frontiers in Computational Chemistry: Volume 6. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150368481220601.
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Frontiers in Computational Chemistry presents contemporary research on molecular modeling techniques used in drug discovery and the drug development process: computer aided molecular design, drug discovery and development, lead generation, lead optimization, database management, computer and molecular graphics, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including analyses of biological activity. The sixth volume of this series features these six different perspectives on the application of computational chemistry in rational drug design: 1. Computer-aided molecular design in computational chemistry 2. The role of ensemble conformational sampling using molecular docking & dynamics in drug discovery 3. Molecular dynamics applied to discover antiviral agents 4. Pharmacophore modeling approach in drug discovery against the tropical infectious disease malaria 5. Advances in computational network pharmacology for Traditional Chinese Medicine (TCM) research 6. Progress in electronic-structure based computational methods: from small molecules to large molecular systems of biological significance
Book chapters on the topic "Biological networks, simulation, structural analysis, dynamic analysis"
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Kuzniar, Krystyna, and Zenon Waszczyszyn. "Neural Networks for the Simulation and Identification Analysis of Buildings Subjected to Paraseismic Excitations." In Intelligent Computational Paradigms in Earthquake Engineering, 393–432. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-099-8.ch016.
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The chapter deals with an application of neural networks to the analysis of vibrations of medium-height prefabricated buildings with load-bearing walls subjected to paraseismic excitations. Neural network technique was used for identification of dynamic properties of actual buildings, simulation of building responses to paraseismic excitations as well as for the analysis of response spectra. Mining tremors in strip mines and in the most seismically active mining regions in Poland with underground exploitation were the sources of these vibrations. On the basis of the experimental data obtained from the measurements of kinematic excitations and dynamic building responses of actual structures the training and testing patterns were formulated. It was stated that the application of neural networks enables us to predict the results with accuracy quite satisfactory for engineering practice. The results presented in this chapter lead to a conclusion that the neural technique gives new prospects of efficient analysis of structural dynamics problems related to paraseismic excitations.
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Dematties, Dario, George K. Thiruvathukal, Silvio Rizzi, Alejandro Wainselboim, and B. Silvano Zanutto. "Towards High-End Scalability on Biologically-Inspired Computational Models." In Parallel Computing: Technology Trends. IOS Press, 2020. http://dx.doi.org/10.3233/apc200077.
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The interdisciplinary field of neuroscience has made significant progress in recent decades, providing the scientific community in general with a new level of understanding on how the brain works beyond the store-and-fire model found in traditional neural networks. Meanwhile, Machine Learning (ML) based on established models has seen a surge of interest in the High Performance Computing (HPC) community, especially through the use of high-end accelerators, such as Graphical Processing Units(GPUs), including HPC clusters of same. In our work, we are motivated to exploit these high-performance computing developments and understand the scaling challenges for new–biologically inspired–learning models on leadership-class HPC resources. These emerging models feature sparse and random connectivity profiles that map to more loosely-coupled parallel architectures with a large number of CPU cores per node. Contrasted with traditional ML codes, these methods exploit loosely-coupled sparse data structures as opposed to tightly-coupled dense matrix computations, which benefit from SIMD-style parallelism found on GPUs. In this paper we introduce a hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) parallelization scheme to accelerate and scale our computational model based on the dynamics of cortical tissue. We ran computational tests on a leadership class visualization and analysis cluster at Argonne National Laboratory. We include a study of strong and weak scaling, where we obtained parallel efficiency measures with a minimum above 87% and a maximum above 97% for simulations of our biologically inspired neural network on up to 64 computing nodes running 8 threads each. This study shows promise of the MPI+OpenMP hybrid approach to support flexible and biologically-inspired computational experimental scenarios. In addition, we present the viability in the application of these strategies in high-end leadership computers in the future.
Conference papers on the topic "Biological networks, simulation, structural analysis, dynamic analysis"
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Royston, Thomas. "Leveraging the Equivalence of Hysteresis Models From Different Fields for Analysis and Numerical Simulation of Jointed Structures." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34212.
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An important problem that spans across many types of systems (e.g. mechanical and biological) is how to model the dynamics of joints or interfaces in built-up structures in such a way that the complex dynamic and energy dissipative behavior that depends on micro-scale phenomena at the joint/interface is accurately captured, yet in a framework that is amenable to efficient computational analyses of the larger macro-scale system of which the joint or interface is a (spatially) small part. Simulating joint behavior in finite element analysis by meshing the joint regions finely enough to capture relevant micromechanics is impractical for large-scale structural systems due to the prohibitively small time steps required and/or resulting matrix ill-conditioning. A more practical approach is to devise constitutive models for the overall behavior of individual joints that accurately capture their nonlinear and energy-dissipative behavior and to incorporate the constitutive response locally into the otherwise often-linear structural model. Recent studies have successfully captured and simulated mechanical joint dynamics using computationally simple phenomenological models of combined elasticity and slip with associated friction and energy dissipation, known as Iwan models. In the present article, the author reviews the relationship, and in some cases exact equivalence, of one type of Iwan model to several other models of hysteretic behavior that have been used to simulate a wide range of physical phenomena. Specifically, it is shown that the “parallel-series” Iwan model has been referred to in other fields by different names, including “Maxwell Resistive-Capacitor” and “Ishlinskii”. Given this, the author establishes the relationship of this Iwan model to several other hysteresis models, most significantly the classical Preisach model. Having established these relationships, it is then possible to extend analytical tools developed for a specific hysteresis model to all of the models with which it is related. Such analytical tools include experimental identification, inversion and analysis of vibratory energy flow and dissipation. A numerical case study of a simple system that includes an Iwan-modeled joint illustrates these points.
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Davidson, Jacob D., and N. C. Goulbourne. "Connecting Chain Chemistry and Network Topology With the Large Deformation Mechanical Response of Elastomers." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-88551.
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Elastomers are polymers able to undergo large, reversible deformations, and their mechanical properties depend on the chemistry of individual chains as well as the topology of the crosslinked network. In this work we analyze the connection between micro-scale network structure and the macroscopic mechanical properties by performing molecular dynamics (MD) simulations using the Kremer & Grest bead-spring model. The chain length and the density at which crosslinking is performed are varied in order to produce systems ranging from crosslink-dominated to highly entangled, and stress-stretch results are obtained via MD in the large deformation regime. In analogy with recent work on social, technological, and biological networks, we apply mathematical graph theory to describe elastomer networks in a multi-scale modeling framework. A matrix formulation of crosslinked polymers is presented and applied in order to identify the network structure resulting from both chemical crosslinks and physical crosslinks (entanglements). We show that spectral analysis of the crosslink and chain entanglement adjacency matrices along with the corresponding degree distributions can be used to identify and differentiate between the different materials. The spectrum of the crosslink adjacency matrix resembles a sparse regular graph, and spectrum of the intermolecular chain entanglement matrix for the highly entangled systems is shown to resemble a random graph; however, deviations are noted which require further study. A comparison of the network properties with the stress-stretch response demonstrates the influence of both crosslinks and entanglements on the large deformation mechanical behavior of an elastomer material.
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Omar, Tarek A., Nabih E. Bedewi, and Azim Eskandarian. "Recurrent Artificial Neural Networks for Crashworthiness Analysis." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-1190.
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Abstract The initial velocity and structural characteristics of any vehicle are the main factors affecting the vehicle response in case of frontal Impact. Finite Element (FE) simulations are essential tools for crashworthiness analysis, however, the FE models are getting bigger which increases the simulation time and cost. An advanced recurrent Artificial Neural Network (ANN) is used to store the nonlinear dynamic characteristics of the vehicle structure. Therefore, hundreds of impact scenarios can be performed quickly with much less cost by using the trained networks. The equation of motion of the dynamic system was used to define the inputs and outputs of the ANN. The back-propagation learning rule was used to adjust the connecting weights and biases of the developed Network. To include the dynamics of the system, the delayed acceleration was fed back as an input to the ANN together with the velocity and displacement. A Finite Element (FE) model for a simple box beam with rigid mass attached to it was developed to represent a general crushable object. The simulation results were performed by impacting this model into a rigid wall with different initial velocities. The displacement, velocity and acceleration curves obtained from the simulation — for the C.G. of the moving mass — were used to train the ANN. After a successful training phase, the ANN was tested by predicting a new acceleration curve. The points of the acceleration curve were predicted sequentially since only one point of the curve is predicted through one cycle of the NN operation. The predicted acceleration curve showed a good correlation with the actual curve obtained from the simulation. During the recall phase, the predicted acceleration of a new state was integrated twice to obtain the velocity and displacement by using a second order integration scheme. Then, the displacement, velocity and acceleration of this new state were fed to the ANN to predict the next state acceleration, and so forth. The results indicated that the recurrent ANN can accurately capture the frontal crash characteristics of any impacting structure, and predict the crash performance of the same structure for any other crash scenario within the training limits. The current paper considered only the front impact, however, an offset and oblique impact scenarios will be included in further research.
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De Pinho Alho, Alexandre Teixeira, and Douglas Papera. "The Application of a Simulation Tool for the Analysis of Offloading Operations in FPSO Units." In ASME 2004 23rd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/omae2004-51128.
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The cargo system of FPSO units is of particular interest due to its rigorous operational requirements. In addition to structural and static stability requirements, the offloading operation of FPSO units should satisfy rigorous requirements related to the dynamic behavior of the vessel. In this sense, the cargo offloading process of FPSO units represents a critical aspect related to its design and operation. Unfortunately, due to the great computation effort usually required to analyze complex pipe networks, it is not a common practice to perform the analysis of the cargo system functioning at the design stage. This paper presents the analysis of the offloading operation of a typical FPSO unit. The analysis comprehends the simulation of the operation of the cargo system during the offloading process. The author adopted a new method to the analysis of pipe networks that seems to be more adequate for the analysis of fully branched systems. The results obtained indicate that the simulation of the cargo system operation is a useful tool for the analysis of the offloading process of FPSO units both at the design stage and during site operation.
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Diesselhorst, Tilman, and Werner Schnellhammer. "Efficient Modeling of the Relevant Effects for Water Hammer Calculation." In ASME 2006 Pressure Vessels and Piping/ICPVT-11 Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/pvp2006-icpvt-11-93386.
Full textAbstract:
The essential features for an adequate simulation of water hammer effects are incorporated in the one-dimensional fluiddynamic code ROLAST. This code was developed for the calculation of transient load cases in piping networks, including active and passive components like pumps, valves, check valves, vessels, and allowing for various boundary and initial conditions. The essential models for water hammer treatment are described like condensation, air bubble behaviour and venting, and especially their adaptation to the 1D flow formulation. Examples of validation are given. The role of dissolved air is discussed. The effects of dynamic friction and coupling with the structural analysis on a realistic damping behavior are explained. The code allows the calculation of realistic water hammer loads on the system by considering the reaction of components like check valves at the some time.
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Gonzalez, Gabriel Mattos, Marcos Queija de Siqueira, Marina Leivas Simão, Paulo Maurício Videiro, and Luis Volnei Sudati Sagrilo. "On the Use of Artificial Neural Networks for Estimating the Long-Term Mooring Lines Response Considering Wind Sea and Swell." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18868.
Full textAbstract:
Abstract Nowadays, artificial intelligence algorithms are regaining visibility mainly due to the increase in computational capability. Among those, artificial neural networks (ANN) are very useful for the regression of highly nonlinear phenomena, such as the dynamic response of offshore structures. Due to the escalating demand in the oil and gas industry, offshore fields have been explored in deeper waters, which leads to more severe environmental conditions. A reliable and efficient evaluation of the long-term response of mooring systems, a crucial element of floating offshore structures, is then imperative. The estimation of the mooring long-term response is usually obtained numerically through the convolution of the short-term responses, based on short-term stationary environmental conditions (typically 3-h). Each of these short-term responses is obtained through a time-domain dynamic structural analysis from which statistical parameters of interest are calculated, such as the mean of the tension maxima sample or the maxima frequency. Such analyses tend to be quite time-consuming and a reliable estimator of these short-term statistical parameters may be of great help. In this paper, an ANN is trained to predict the short-term extreme peak response statistical parameters. The used training datasets include the wave significant height and spectral peak period for both wind sea and swell waves, generated by Importance Sampling Monte Carlo Simulation (ISMCS) method. Fixed directions of wind sea and swell are considered. It is shown that the ANN successfully predicts the short-term response statistical parameters for both cases, which are later used for the evaluation of the long-term N-year mooring response.