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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.
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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 (May 17, 2021): 127. http://dx.doi.org/10.12688/f1000research.50850.3.
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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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SEKUNDA, ANDRÉ, MOHAMMAD KOMAREJI, and ROLAND BOUFFANAIS. "Interplay between signaling network design and swarm dynamics." Network Science 4, no. 2 (May 23, 2016): 244–65. http://dx.doi.org/10.1017/nws.2016.5.
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AbstractDistributed information transfer is of paramount importance to the effectiveness of dynamic collective behaviors, especially when a swarm is confronted with complex environmental circumstances. Recently, the signaling network of interaction underlying such effective information transfers has been revealed in the particular case of bird flocks governed by a topological interaction. Such biological systems are known to be evolutionary optimized, but are also constrained by the very nature of the signaling mechanisms—owing to intrinsic limitations in sensory modalities—enabling communication among individuals. Here, we propose that artificial swarm design can be tackled from the angle of signaling network design. To this aim, we use different network models to investigate the impact of some network structural properties on the effectiveness of a specific emergent swarming behavior, namely global consensus. Two new network models are introduced, which together with the well-known Watts–Strogatz model form the basis for an analysis of the relationship between clustering, shortest path and speed to consensus. A network-theoretic approach combined with spectral graph theory tools are used to propose some signaling network design principles. Eventually, one key design principle—a concomitant reduction in clustering and connecting path—is successfully tested on simulations of swarms of self-propelled particles.
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Yang, Chi-Dung, Hsi-Yuan Huang, Sirjana Shrestha, Yen-Hua Chen, Hsien-Da Huang, and Ching-Ping Tseng. "Large-Scale Functional Analysis of CRP-Mediated Feed-Forward Loops." International Journal of Molecular Sciences 19, no. 8 (August 9, 2018): 2335. http://dx.doi.org/10.3390/ijms19082335.
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Feed-forward loops (FFLs) represent an important and basic network motif to understand specific biological functions. Cyclic-AMP (cAMP) receptor protein (CRP), a transcription factor (TF), mediates catabolite repression and regulates more than 400 genes in response to changes in intracellular concentrations of cAMP in Escherichia coli. CRP participates in some FFLs, such as araBAD and araFGH operons and adapts to fluctuating environmental nutrients, thereby enhancing the survivability of E. coli. Although computational simulations have been conducted to explore the potential functionality of FFLs, a comprehensive study on the functions of all structural types on the basis of in vivo data is lacking. Moreover, the regulatory role of CRP-mediated FFLs (CRP-FFLs) remains obscure. We identified 393 CRP-FFLs in E. coli using EcoCyc and RegulonDB. Dose–response genomic microarray of E. coli revealed dynamic gene expression of each target gene of CRP-FFLs in response to a range of cAMP dosages. All eight types of FFLs were present in CRP regulon with various expression patterns of each CRP-FFL, which were further divided into five functional groups. The microarray and reported regulatory relationships identified 202 CRP-FFLs that were directly regulated by CRP in these eight types of FFLs. Interestingly, 34% (147/432) of genes were directly regulated by CRP and CRP-regulated TFs, which indicates that these CRP-regulated genes were also regulated by other CRP-regulated TFs responding to environmental signals through CRP-FFLs. Furthermore, we applied gene ontology annotation to reveal the biological functions of CRP-FFLs.
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Zhou, Zhi-Guang, Qi-Zheng Yao, Dong Lei, Qing-Qing Zhang, and Ji Zhang. "Investigations on the mechanisms of interactions between matrix metalloproteinase 9 and its flavonoid inhibitors using a combination of molecular docking, hybrid quantum mechanical/molecular mechanical calculations, and molecular dynamics simulations." Canadian Journal of Chemistry 92, no. 9 (September 2014): 821–30. http://dx.doi.org/10.1139/cjc-2014-0180.
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Many experimental studies have found that flavonoids can inhibit the activities of matrix metalloproteinases (MMPs), but the relevant mechanisms are still unclear. In this paper, the interaction mechanisms of MMP-9 with its five flavonoid inhibitors are investigated using a combination of molecular docking, hybrid quantum mechanical and molecular mechanical (QM/MM) calculations, and molecular dynamics simulations. The molecular dynamics simulation results show a good linear correlation between the calculated binding free energies of QM/MM−Poisson–Boltzmann surface area (PBSA) and the experimental −log(EC50) regarding the studied five flavonoids on MMP-9 inhibition in explicit solvent. It is found that compared with the MM−PBSA method, the QM/MM−PBSA method can obviously improve the accuracy for the calculated binding free energies. The predicted binding modes of the five flavonoid−MMP-9 complexes reveal that the different hydrogen bond networks can form besides producing the Zn−O coordination bonds, which can reasonably explain previous experimental results. The agreement between our calculated results and the previous experimental facts indicates that the force field parameters used here are effective and reliable for investigating the systems of flavonoid−MMP-9 interactions, and thus, these simulations and analyses could be reproduced for the other related systems involving protein−ligand interactions. This paper may be helpful for designing the new MMP-9 inhibitors having higher biological activities by carrying out the structural modifications of flavonoid molecules.
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Xu, Xia, Song Xu, Liting Han, and Xufeng Yao. "Coupling analysis between functional and structural brain networks in Alzheimer's disease." Mathematical Biosciences and Engineering 19, no. 9 (2022): 8963–74. http://dx.doi.org/10.3934/mbe.2022416.
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<abstract> <p>The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction. Furthermore, the correlations between rsfMRI-SC/DTI-FC coupling and four neuropsychological scores of mini-mental state examination (MMSE), clinical dementia rating-sum of boxes (CDR-SB), functional activities questionnaire (FAQ) and montreal cognitive assessment (MoCA) were inferred by partial correlation analyses, respectively. The results demonstrated that there existed significant correlation between rsfMRI-FC and DTI-SC (<italic>p</italic> < 0.05), and the coupling of rsfMRI-FC/DTI-SC showed negative correlation with MMSE score (<italic>p</italic> < 0.05), positive correlations with CDR-SB and FAQ scores (<italic>p</italic> < 0.05), and no correlation with MoCA score (<italic>p</italic> > 0.05). It was concluded that there existed FC/SC coupling and varied network characteristics for rsfMRI and DTI, and this would provide the clues to understand the underlying mechanisms of cognitive deficits of AD.</p> </abstract>
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Cho, K. H., and O. Wolkenhauer. "Analysis and modelling of signal transduction pathways in systems biology." Biochemical Society Transactions 31, no. 6 (December 1, 2003): 1503–9. http://dx.doi.org/10.1042/bst0311503.
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There is general agreement that a systems approach is needed for a better understanding of causal and functional relationships that generate the dynamics of biological networks and pathways. These observations have been the basis for efforts to get the engineering and physical sciences involved in life sciences. The emergence of systems biology as a new area of research is evidence for these developments. Dynamic modelling and simulation of signal transduction pathways is an important theme in systems biology and is getting growing attention from researchers with an interest in the analysis of dynamic systems. This paper introduces systems biology in terms of the analysis and modelling of signal transduction pathways. Focusing on mathematical representations of cellular dynamics, a number of emerging challenges and perspectives are discussed.
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Dyachenko, Leonid, Andrey Benyn, and Vladimir Smyrnov. "Dynamic factor to live load regulation during structural calculation of bridges at high-speed networks." Bulletin of scientific research results, no. 3 (October 17, 2017): 15–27. http://dx.doi.org/10.20295/2223-9987-2017-3-15-27.
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Objective: Improvement of dynamic analysis method of simple beam spans in the process of high-speed trains impact. Methods: Mathematical modeling with numerical and analytical methods of building mechanics was applied. Results: The parameters of high-speed trains influence on simple beam spans of bridges were analyzed. The method of dynamic factor to live load determination was introduced. The reliability of the method in question was corroborated by the results of numerical simulation of high-speed trains’ movement by beam spans with different speeds. The introduced algorithm of dynamic analysis was based on the connection between maximum acceleration of a beam span in resonance vibration mode and the basic factors of stress-strain state. The method in question makes it possible to determine both maximum and bottom values of main loading in a construction, which determines the possibility of endurance tests. It was noted that dynamic additions for the components of stress-strain state (bending moments, shear force, vertical deflections) were different. The fact in question determines the necessity of differential approach application to identify dynamic factors in the process of calculation testing on the first and the second groups of limit states. Practical importance: The method of dynamic factors’ determination presented in the study makes it possible to perform dynamic analysis and determine the main loading in simple beam spans without application of numerical modeling and direct analytical analysis, which considerably reduces labor costs on engineering.
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Koschützki, Dirk, Björn H. Junker, Jörg Schwender, and Falk Schreiber. "Structural analysis of metabolic networks based on flux centrality." Journal of Theoretical Biology 265, no. 3 (August 2010): 261–69. http://dx.doi.org/10.1016/j.jtbi.2010.05.009.
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Eltanani, Shadi, Tjeerd V. olde Scheper, and Helen Dawes. "A Novel Criticality Analysis Technique for Detecting Dynamic Disturbances in Human Gait." Computers 11, no. 8 (August 3, 2022): 120. http://dx.doi.org/10.3390/computers11080120.
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The application of machine learning (ML) has made an unprecedented change in the field of medicine, showing a significant potential to automate tasks and to achieve objectives that are closer to human cognitive capabilities. Human gait, in particular, is a series of continuous metabolic interactions specific for humans. The need for an intelligent recognition of dynamic changes of gait enables physicians in clinical practice to early identify impaired gait and to reach proper decision making. Because of the underlying complexity of the biological system, it can be difficult to create an accurate detection and analysis of imbalanced gait. This paper proposes a novel Criticality Analysis (CA) methodology as a feasible method to extract the dynamic interactions involved in human gait. This allows a useful scale-free representation of multivariate dynamic data in a nonlinear representation space. To quantify the effectiveness of the CA methodology, a Support Vector Machine (SVM) algorithm is implemented in order to identify the nonlinear relationships and high-order interactions between multiple gait data variables. The gait features extracted from the CA method were used for training and testing the SVM algorithm. The simulation results of this paper show that the implemented SVM model with the support of the CA method increases the accuracy and enhances the efficiency of gait analysis to extremely high levels. Therefore, it can perform as a robust classification tool for detection of dynamic disturbances of biological data patterns and creates a tremendous opportunity for clinical diagnosis and rehabilitation.
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Vignet, Pierre, Jean Coquet, Sébastien Auber, Matéo Boudet, Anne Siegel, and Nathalie Théret. "Discrete modeling for integration and analysis of large-scale signaling networks." PLOS Computational Biology 18, no. 6 (June 13, 2022): e1010175. http://dx.doi.org/10.1371/journal.pcbi.1010175.
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Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states.
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Sobczak, Paweł, Ewa Stawiarska, Judit Oláh, József Popp, and Tomas Kliestik. "Logistics management of the rail connections using graph theory: the case of a public transportation company on the example of Koleje Dolnośląskie S.A." Engineering Management in Production and Services 10, no. 3 (September 1, 2018): 7–22. http://dx.doi.org/10.2478/emj-2018-0013.
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Abstract The main purpose of the paper was the structural analysis of the connections network used by a railway carrier Koleje Dolnośląskie S.A. operating in southern Poland. The analysis used simulation methods. The analysis and simulation were based on graph theory, which is successfully used in analysing a wide variety of networks (social, biological, computer, virtual and transportation networks). The paper presents indicators which allow judging the analysed connections network according to an appropriate level of transport services. Simulation results allowed proposing some modifications for the improvement of the analysed connections network. The paper also demonstrates that graph theory and network simulations should be used as tools by transportation companies during the stage of planning a connections network.
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Zhao, Wenchuan, Yu Zhang, and Ning Wang. "Development and Performance Analysis of Pneumatic Soft-Bodied Bionic Actuator." Applied Bionics and Biomechanics 2021 (February 17, 2021): 1–13. http://dx.doi.org/10.1155/2021/6623059.
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The design of a pneumatic soft-bodied bionic actuator derives from the structural characteristics and motion mechanism of biological muscles, combined with the nonlinear hyperelasticity of silica gel, which can improve the mobility and environmental adaptability of soft-bodied bionic robots. Based on Yeoh’s second-order constitutive model of silica gel, the deformation analysis model of the actuator is established, and the rationality of the structure design and motion forms of the actuator and the accuracy of the deformation analysis model are verified by using the numerical simulation algorithm. According to the physical model of the pneumatic soft-bodied bionic actuator, the motion and dynamic characteristics of the actuator are tested and analyzed, the curves of motion and dynamic characteristics of the actuator are obtained, and the empirical formula of the bending angle and driving torque of the actuator is fitted out. The results show that the deformation analysis model and numerical simulation method are accurate, and the pneumatic soft-bodied bionic actuator is feasible and effective, which can provide a design method and reference basis for the research and implementation of soft-bodied bionic robot actuator.
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Altay, Gökmen, and Frank Emmert-Streib. "Structural influence of gene networks on their inference: analysis of C3NET." Biology Direct 6, no. 1 (2011): 31. http://dx.doi.org/10.1186/1745-6150-6-31.
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Zhou, Zhaoming, Jinsong Tan, Jia Zhang, and Mian Qin. "Structural optimization and analysis of surface acoustic wave biosensor based on numerical method." International Journal of Distributed Sensor Networks 15, no. 9 (September 2019): 155014771987564. http://dx.doi.org/10.1177/1550147719875648.
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The biosensor based on surface acoustic wave is of great significance for the application of biological clinical detection, but the sensitivity and specificity of surface acoustic wave biosensor still need to be improved. In this article, the structure of surface acoustic wave biosensor and interdigital transducer is designed by studying the theoretical model of surface acoustic wave biosensor. Then the amplitude–frequency response of the device is studied. Later, the simulation model of surface acoustic wave biosensor structure is established, and the performance of surface acoustic wave device is numerically calculated. Meanwhile, the relationship between the applied excitation signal and the resonant frequency is considered, and the performance of the surface wave propagation and attenuation characteristics are analyzed. In addition, the influence of the material of the piezoelectric substrate and the structure of the interdigitated electrode on the surface acoustic wave propagation are studied. The response potential curve and the total displacement of the particle vibration are analyzed, and the structure of the surface acoustic wave device is optimized.
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Guo, Wei-Feng, Xiangtian Yu, Qian-Qian Shi, Jing Liang, Shao-Wu Zhang, and Tao Zeng. "Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis." PLOS Computational Biology 17, no. 5 (May 6, 2021): e1008962. http://dx.doi.org/10.1371/journal.pcbi.1008962.
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In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.
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Zhou, Bin, and Rui Guo. "Integrative Analysis of Genomic and Clinical Data Reveals Intrinsic Characteristics of Bladder Urothelial Carcinoma Progression." Genes 10, no. 6 (June 17, 2019): 464. http://dx.doi.org/10.3390/genes10060464.
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The progression of bladder cancer is generally a complex and dynamic process, involving a variety of biological factors. Here, we aimed to identify a set of survival-related genes that play an important role in the progression of bladder cancer and uncover their synergistic patterns. Based on the large-scale genomic profiling data and clinical information of 404 bladder cancer patients derived from The Cancer Genome Atlas (TCGA) database, we first discovered 1078 survival-related genes related to their survival states using univariate and multivariate Cox proportional hazardous regression. We then investigated the dynamic changes of the cooperative behaviors of these 1078 genes by analyzing their respective genomic features, including copy number variations, DNA methylations, somatic mutations, and microRNA regulatory networks. Our analyses showed that during the progression of bladder cancer, the biological disorder involving the identified survival-related genes can be reflected by multiple levels of abnormal gene regulation, ranging from genomic alteration to post-transcriptional dysregulation. In particular, the stage-specific co-expression networks of these genes undergo a series of structural variations. Our findings provide useful hints on understanding the underlying complex molecular mechanisms related to the evolution of bladder cancer and offer a new perspective on clinical diagnosis and treatment of bladder cancer.
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Gao, Yunyuan, Zhen Cao, Jia Liu, and Jianhai Zhang. "A novel dynamic brain network in arousal for brain states and emotion analysis." Mathematical Biosciences and Engineering 18, no. 6 (2021): 7440–63. http://dx.doi.org/10.3934/mbe.2021368.
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<abstract> <sec><title>Background</title><p>Brain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals.</p> </sec> <sec><title>New Method</title><p>Time factors is integrated to construct a dynamic brain network under high and low arousal conditions. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain.</p> </sec> <sec><title>Results</title><p>The frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal.</p> </sec> <sec><title>Comparison with Existing Method(s)</title><p>Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis.</p> </sec> <sec><title>Conclusions</title><p>The proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis.</p> </sec> </abstract>
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Saidi, Abdelaziz Salah. "Investigation of Structural Voltage Stability in Tunisian Distribution Networks Integrating Large-Scale Solar Photovoltaic Power Plant." International Journal of Bifurcation and Chaos 30, no. 13 (October 2020): 2050259. http://dx.doi.org/10.1142/s0218127420502594.
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This research shows a structural voltage stability analysis of a distribution network incorporating large-scale solar photovoltaic power plant. Detailed modeling of the transmission network and photovoltaic systems is presented and a differential-algebraic equations model is developed. The resulting system state and load-flow Jacobian matrix are reorganized according to the type of the bus system in place of the standard injected complex power equations arrangement. The interactions among system buses for loading tests and solar photovoltaic power penetration are structurally scrutinized. Two-bus bifurcations are revealed to be a predecessor to system voltage collapse. The investigation is carried out by using bifurcation diagrams of photovoltaic generation margin, load-flow analysis, short-circuits, photovoltaic farm disconnections and loading conditions. Furthermore, evaluation of voltage stability reveals that the dynamic component of the voltage strongly depends on fault short-circuit capacity of the power system at the bus, where, the solar system is integrated. The overall result, which encompasses the views from the presented transmission network integration studies, is a positive outcome for future grid integration of solar photovoltaic in the Tunisian system. Tunisia’s utilities policies on integration of solar photovoltaic in distribution network is expected to benefit from the results of the presented study. Moreover, given the huge potential and need for solar photovoltaic penetration into the transmission network, the presented comprehensive analysis will be a valuable guide for evaluating and improving the performances of national transmission networks of other countries too.
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Patil, Vishal S., Darasaguppe R. Harish, Umashankar Vetrivel, Subarna Roy, Sanjay H. Deshpande, and Harsha V. Hegde. "Hepatitis C Virus NS3/4A Inhibition and Host Immunomodulation by Tannins from Terminalia chebula: A Structural Perspective." Molecules 27, no. 3 (February 5, 2022): 1076. http://dx.doi.org/10.3390/molecules27031076.
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Terminalia chebula Retz. forms a key component of traditional folk medicine and is also reported to possess antihepatitis C virus (HCV) and immunomodulatory activities. However, information on the intermolecular interactions of phytochemicals from this plant with HCV and human proteins are yet to be established. Thus, by this current study, we investigated the HCV NS3/4A inhibitory and host immune-modulatory activity of phytocompounds from T. chebula through in silico strategies involving network pharmacology and structural bioinformatics techniques. To start with, the phytochemical dataset of T. chebula was curated from biological databases and the published literature. Further, the target ability of the phytocompounds was predicted using BindingDB for both HCV NS3/4A and other probable host targets involved in the immune system. Further, the identified targets were docked to the phytochemical dataset using AutoDock Vina executed through the POAP pipeline. The resultant docked complexes with significant binding energy were subjected to 50 ns molecular dynamics (MD) simulation in order to infer the stability of complex formation. During network pharmacology analysis, the gene set pathway enrichment of host targets was performed using the STRING and Reactome pathway databases. Further, the biological network among compounds, proteins, and pathways was constructed using Cytoscape 3.6.1. Furthermore, the druglikeness, side effects, and toxicity of the phytocompounds were also predicted using the MolSoft, ADVERpred, and PreADMET methods, respectively. Out of 41 selected compounds, 10 were predicted to target HCV NS3/4A and also to possess druglike and nontoxic properties. Among these 10 molecules, Chebulagic acid and 1,2,3,4,6-Pentagalloyl glucose exhibited potent HCV NS3/4A inhibitory activity, as these scored a lowest binding energy (BE) of −8.6 kcal/mol and −7.7 kcal/mol with 11 and 20 intermolecular interactions with active site residues, respectively. These findings are highly comparable with Asunaprevir (known inhibitor of HCV NS3/4A), which scored a BE of −7.4 kcal/mol with 20 key intermolecular interactions. MD studies also strongly suggest that chebulagic acid and 1,2,3,4,6-Pentagalloyl glucose as promising leads, as these molecules showed stable binding during 50 ns of production run. Further, the gene set enrichment and network analysis of 18 protein targets prioritized 10 compounds and were predicted to potentially modulate the host immune system, hemostasis, cytokine levels, interleukins signaling pathways, and platelet aggregation. On overall analysis, this present study predicts that tannins from T. chebula have a potential HCV NS3/4A inhibitory and host immune-modulatory activity. However, further experimental studies are required to confirm the efficacies.
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Rakha, Hesham A., and Michel W. Van Aerde. "Comparison of Simulation Modules of TRANSYT and INTEGRATION Models." Transportation Research Record: Journal of the Transportation Research Board 1566, no. 1 (January 1996): 1–7. http://dx.doi.org/10.1177/0361198196156600101.
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The TRANSYT simulation/optimization model serves as an unofficial international standard against which many measure the efficiency of other methods of coordinating networks of traffic signals that operate at a constant and common cycle length. However, dynamics due to traffic rerouting, the simultaneous operation of adjacent traffic signals at different cycle lengths, the effect of queue spillbacks on the capacity of upstream links, and various forms of real-time intersection control cannot be modeled using a static model such as TRANSYT. This has created a unique niche for a more dynamic signal network simulation tool. Before modeling such special dynamic scenarios, there first exists a need to validate the static signal control features of such a model and to determine if its unique dynamic features still permit it to yield credible static results. This study has two objectives. First, it attempts to illustrate the extent to which estimates of vehicle travel time, vehicle delay, and number of vehicle stops are related when a standard static signal network is examined using both TRANSYT and INTEGRATION. Second, it strives to illustrate that the types of more complex signal timing problems, which at present cannot be examined by the TRANSYT model, can be examined using the dynamic features of INTEGRATION. The results are intended to permit a better appreciation of both their differences and similarities and permit a more informed decision as to when and where each model should be used. Also demonstrated is that INTEGRATION simulates traffic-signalized networks in a manner that is consistent with TRANSYT for conditions in which TRANSYT is valid. Specifically, the difference in total travel time and percentage of vehicle stops is within 5 percent. In addition, it is also shown that INTEGRATION can simulate conditions that represent the limitations to the current TRANSYT model, such as degrees of saturation in excess of 95 percent and adjacent signals operating at different cycle length durations. This analysis of the simulation features of TRANSYT and INTEGRATION is intended to be a precursor to a comparison of their respective optimization routines.
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Li, Yixuan. "Finite Element Structure Analysis of Automobile Suspension Control Arm Based on Neural Network Control." Security and Communication Networks 2021 (June 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/9978701.
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The control arm is an important transmission and guidance device in the Macpherson suspension system, which has an important impact on the ride comfort, operation stability, and safety of the vehicle, so it is necessary to study the structural performance of the control arm. In this paper, based on neural network control model, finite element analysis, and fatigue analysis theory, the strength, stiffness, and dynamic and fatigue performance of the control arm are studied and analyzed. Taking the ground contact force of the tire as the input condition, the static analysis of the front suspension is carried out, and the boundary condition of the load of the control arm is extracted. The finite element strength of the control arm is calculated in the OptiStruct solver under the conditions of uneven road, braking, and turning. At the same time, the longitudinal stiffness and lateral stiffness of the control arm are analyzed. The simulation results show that the control arm has better strength and stiffness performance.
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Zhu, Zhaozhong, Yunshi Fan, Yang Liu, Taijiao Jiang, Yang Cao, and Yousong Peng. "Prediction of antiviral drugs against African swine fever viruses based on protein–protein interaction analysis." PeerJ 8 (April 1, 2020): e8855. http://dx.doi.org/10.7717/peerj.8855.
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The African swine fever virus (ASFV) has severely influenced the swine industry of the world. Unfortunately, there is currently no effective antiviral drug or vaccine against the virus. Identification of new anti-ASFV drugs is urgently needed. Here, an up-to-date set of protein–protein interactions between ASFV and swine were curated by integration of protein–protein interactions from multiple sources. Thirty-eight swine proteins were observed to interact with ASFVs and were defined as ASFV-interacting swine proteins. The ASFV-interacting swine proteins were found to play a central role in the swine protein–protein interaction network, with significant larger degree, betweenness and smaller shortest path length than other swine proteins. Some of ASFV-interacting swine proteins also interacted with several other viruses and could be taken as potential targets of drugs for broad-spectrum effect, such as HSP90AB1. Finally, the antiviral drugs which targeted ASFV-interacting swine proteins and ASFV proteins were predicted. Several drugs with either broad-spectrum effect or high specificity on ASFV-interacting swine proteins were identified, such as Polaprezinc and Geldanamycin. Structural modeling and molecular dynamics simulation showed that Geldanamycin could bind with swine HSP90AB1 stably. This work could not only deepen our understanding towards the ASFV-swine interactions, but also help for the development of effective antiviral drugs against the ASFVs.
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Shokouhi, Seyed KS, Yong Yuan, and Hongping Zhu. "Optimal placement of sensors and piezoelectric friction dampers in the pipeline networks based on nonlinear dynamic analysis." Journal of Low Frequency Noise, Vibration and Active Control 36, no. 1 (March 2017): 56–82. http://dx.doi.org/10.1177/0263092317693504.
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Experiences of past earthquakes demonstrate that pipeline systems have no proper performance when exposed to severe earthquakes. In this study, sensor and damper placement approaches are presented for doing reliable health monitoring and seismic retrofitting of the piping networks. Since most of the available sensor placement methods are based on modal analysis results, the authors propose a new scheme that relies on the nonlinearity which utilizes nonlinear time history analysis results, and genetic algorithm is selected to act as the methodology of optimization as well. The results demonstrate that the proposed optimal sensor configuration strategy is more accurate and efficient than the extended modal assurance criterion method. To assess the number of sensors, a sensitivity analysis is undertaken in which the number of sensors computed optimally by the proposed algorithm contains the least convergence error. In addition, the number of iterations and the time consumed in the proposed approach are considerably less than the extended modal assurance criterion method. Moreover, the efficiency of the proposed sensor placement scheme was compared with a new algorithm proposed by Sun and Büyüköztürk, named discrete artificial bee colony, where the simulation result demonstrates high accuracy of the proposed sensor configuration approach. The initial time history analysis results show the vulnerable points of the system, which destroyed due to the applied seismic waves. Hence, to enhance the seismic performance of the system, piezoelectric friction dampers are optimally placed, where it can be clearly seen that the optimal arrangement of piezoelectric friction dampers in the piping system can significantly decrease the seismic response.
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Ma, Minglin, Yaping Lu, Zhijun Li, Yichuang Sun, and Chunhua Wang. "Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor." Fractal and Fractional 7, no. 1 (January 11, 2023): 82. http://dx.doi.org/10.3390/fractalfract7010082.
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In order to enrich the dynamic behaviors of discrete neuron models and more effectively mimic biological neural networks, this paper proposes a bistable locally active discrete memristor (LADM) model to mimic synapses. We explored the dynamic behaviors of neural networks by introducing the LADM into two identical Rulkov neurons. Based on numerical simulation, the neural network manifested multistability and new firing behaviors under different system parameters and initial values. In addition, the phase synchronization between the neurons was explored. Additionally, it is worth mentioning that the Rulkov neurons showed synchronization transition behavior; that is, anti-phase synchronization changed to in-phase synchronization with the change in the coupling strength. In particular, the anti-phase synchronization of different firing patterns in the neural network was investigated. This can characterize the different firing behaviors of coupled homogeneous neurons in the different functional areas of the brain, which is helpful to understand the formation of functional areas. This paper has a potential research value and lays the foundation for biological neuron experiments and neuron-based engineering applications.
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Kong, Wei, Xiaoyang Mou, Xing Zhi, Xin Zhang, and Yang Yang. "Dynamic Regulatory Network Reconstruction for Alzheimer’s Disease Based on Matrix Decomposition Techniques." Computational and Mathematical Methods in Medicine 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/891761.
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Alzheimer’s disease (AD) is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF) activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA) algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA), which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.
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Uyulan, Caglar, and Ersen Arslan. "Simulation and time-frequency analysis of the longitudinal train dynamics coupled with a nonlinear friction draft gear." Nonlinear Engineering 9, no. 1 (February 7, 2020): 124–44. http://dx.doi.org/10.1515/nleng-2020-0003.
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AbstractTrain safety and operational efficiency can be improved by investigating the dynamics of the train under varying conditions. Longitudinal train dynamics (LTD) simulations performed for such purposes, usually by utilising a nonlinear time-domain model. This paper covers two modes of LTD results corresponding to the time domain and frequency domain analysis. Time-domain solutions are essential to evaluate the full response used for parameter optimisation and controller design studies while frequency domain solutions can provide significant but straightforward clues regarding system dynamics. An advanced draft gear model, which works under a four-stage process is constructed considering all structural components, geometric relationships, friction modelling and dynamic characteristics such as hysteresis, stiffening, state transition, locked unloading, softening. Then, this model is parametrically reduced and implemented into an LTD simulation. The simulation in the time domain is conducted assuming a locomotive connected with a nine wagon via “ode3” fixed-step solver. The transfer function among the first wagon acceleration (output) and the locomotive force (input) estimated through system identification methodology. Then, the identification results interpreted by investigating step-response characteristic and best response giving parameter set is selected. Next, the modal and spectral analysis performed to reveal the behaviour of the in-train forces and the effects of vibration. This paper discusses a reliable methodology for the longitudinal dynamic analysis of the multi-bodied train in time and frequency domain and clarifies in-train vibration behaviour under the existence of sophisticated draft gear.
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Patra, Sabyasachi, and Anjali Mohapatra. "Application of dynamic expansion tree for finding large network motifs in biological networks." PeerJ 7 (May 17, 2019): e6917. http://dx.doi.org/10.7717/peerj.6917.
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Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, and protein function annotation. However, identification of large network motifs is a challenging task as it involves the graph isomorphism problem. Although this problem has been studied extensively in the literature using different computational approaches, still there is a lot of scope for improvement. Motivated by the challenges involved in this field, an efficient and scalable network motif finding algorithm using a dynamic expansion tree is proposed. The novelty of the proposed algorithm is that it avoids computationally expensive graph isomorphism tests and overcomes the space limitation of the static expansion tree (SET) which makes it enable to find large motifs. In this algorithm, the embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of a parent node, either by adding a vertex or by adding an edge. This process does not involve any graph isomorphism check. The time complexity of vertex addition and edge addition are O(n) and O(1), respectively. The growth of a dynamic expansion tree (DET) depends on the availability of patterns in the target network. Pruning of branches in the DET significantly reduces the space requirement of the SET. The proposed algorithm has been tested on a protein–protein interaction network obtained from the MINT database. The proposed algorithm is able to identify large network motifs faster than most of the existing motif finding algorithms.
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Zhang, Yu, Wenchuan Zhao, Ning Wang, and Dengyu Lu. "Development and Performance Analysis of Pneumatic Soft-Bodied Bionic Basic Execution Unit." Journal of Robotics 2020 (November 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/8860550.
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This paper studies the design of pneumatic soft-bodied bionic basic execution unit with soft-rigid combination, which can be used as an actuator for pneumatic soft-bodied robots and soft-bodied manipulators. This study is inspired by structural characteristics and motion mechanism of biological muscles, combined with the nonlinear hyperelasticity of silica gel and the insertion of thin leaf spring structure in the nonretractable layer. Response surface analysis and numerical simulation algorithm are used to determine the optimal combination of structural dimension parameters by taking the maximum output bending angle of the basic executing unit as the optimization objective. Based on Odgen’s third-order constitutive model, the deformation analysis model of the basic execution unit is established. The physical model of pneumatic soft-bodied bionic basic execution unit is prepared through 3D printing, shape deposition, soft lithography, and other processing methods. Finally, the motion and dynamic characteristics of the physical model are tested through experiments and result analysis, thus obtaining curves and empirical formulas describing the motion and dynamic characteristics of the basic execution unit. The relevant errors are compared with the deformation analysis model of the execution unit to verify the feasibility and effectiveness of the design of the pneumatic soft-bodied bionic basic execution unit. The above research methods, research process, and results can provide a reference for the research and implementation of pneumatic and hydraulic driven soft-bodied robots and grasping actuators of soft-bodied manipulators.
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ARMBRUSTER, BENJAMIN, LI WANG, and MARTINA MORRIS. "Forward reachable sets: Analytically derived properties of connected components for dynamic networks." Network Science 5, no. 3 (June 29, 2017): 328–54. http://dx.doi.org/10.1017/nws.2017.10.
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AbstractFormal analysis of the emergent structural properties of dynamic networks is largely uncharted territory. We focus here on the properties of forward reachable sets (FRS) as a function of the underlying degree distribution and edge duration. FRS are defined as the set of nodes that can be reached from an initial seed via a path of temporally ordered edges; a natural extension of connected component measures to dynamic networks. Working in a stochastic framework, we derive closed-form expressions for the mean and variance of the exponential growth rate of the FRS for temporal networks with both edge and node dynamics. For networks with node dynamics, we calculate thresholds for the growth of the FRS. The effects of finite population size are explored via simulation and approximation. We examine how these properties vary by edge duration and different cross-sectional degree distributions that characterize a range of scientifically interesting normative outcomes (Poisson and Bernoulli). The size of the forward reachable set gives an upper bound for the epidemic size in disease transmission network models, relating this work to epidemic modeling (Ferguson, 2000; Eames, 2004).
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Zhang, Qiao, Chunming Dong, Zongze Shao, and Donghui Zhou. "Analysis of the Descent Process and Multi-Objective Optimization Design of a Benthic Lander." Journal of Marine Science and Engineering 11, no. 1 (January 15, 2023): 224. http://dx.doi.org/10.3390/jmse11010224.
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The growing need for deep-sea biological research and environmental monitoring has expanded the demand for benthic landers. Compared with remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), benthic landers can reduce overall operation cost and also possess longer endurance. Configuring a suitable descent velocity is important for benthic lander designs, helping them avoid retrieval failure and improve sea trial efficiencies. In this study, an effective scheme for the configuration and optimization of a self-developed benthic lander was outlined. First, the structural characteristics of the benthic lander were analyzed, and then a dynamic model was established. Second, the hydrodynamic coefficients of the benthic lander during its descent process were calculated using computational fluid dynamics (CFD) methods. Third, the MATLAB Simulink simulation environment was used to solve the dynamic model, and then the multi-objective optimization algorithm was introduced for the optimization design. Finally, the model was validated based on sea trial data, which demonstrated that the designed configuration and optimization scheme were correct and efficient. Collectively, this work provides a useful reference for the rational configuration and practical application of benthic landers.
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Gormley, Michael, Viswanadha U. Akella, Judy N. Quong, and Andrew A. Quong. "An Integrated Framework to Model Cellular Phenotype as a Component of Biochemical Networks." Advances in Bioinformatics 2011 (November 29, 2011): 1–14. http://dx.doi.org/10.1155/2011/608295.
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Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.
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Vrahatis, Aristidis G., Konstantina Dimitrakopoulou, Panos Balomenos, Athanasios K. Tsakalidis, and Anastasios Bezerianos. "CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis." Bioinformatics 32, no. 6 (November 14, 2015): 884–92. http://dx.doi.org/10.1093/bioinformatics/btv673.
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Abstract Motivation: In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific ‘active parts’ of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical ‘themes’—in the form of enriched biologically relevant microRNA-mediated subpathways—that determine the functionality of signaling networks across time. Results: To address these challenges, we developed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) by integrating time series mRNA/microRNA expression data with KEGG pathway maps and microRNA-target interactions. Specifically, microRNA-mediated subpathway topologies are extracted and evaluated based on the temporal transition and the fold change activity of the linked genes/microRNAs. Further, we provide measures that capture the structural and functional features of subpathways in relation to the complete organism pathway atlas. Our application to synthetic and real data shows that CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways. Availability and implementation: CHRONOS is freely available at http://biosignal.med.upatras.gr/chronos/. Contact: tassos.bezerianos@nus.edu.sg. Supplementary information: Supplementary data are available at Bioinformatics online.
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Matsuura, Tomoaki, Naoki Tanimura, Kazufumi Hosoda, Tetsuya Yomo, and Yoshihiro Shimizu. "Reaction dynamics analysis of a reconstitutedEscherichia coliprotein translation system by computational modeling." Proceedings of the National Academy of Sciences 114, no. 8 (February 6, 2017): E1336—E1344. http://dx.doi.org/10.1073/pnas.1615351114.
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To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on anEscherichia coli-based reconstituted in vitro protein synthesis system. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand these complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure, and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion, and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other large-scale biological reaction networks. This developed analysis might be useful for understanding large-scale biological reactions by visualizing complex dynamics, thereby extracting the characteristics of the reaction network, including phase transitions.
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Couillard, D., F. D'amours, and G. Patry. "Étude comparative de trois modèles dynamiques de boues activées." Canadian Journal of Civil Engineering 16, no. 3 (June 1, 1989): 400–407. http://dx.doi.org/10.1139/l89-063.
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Three complete dynamic models are proposed to simulate the transient behaviour of the activated sludge process. The authors compare the responses obtained by simulation with datas found in the literature for the Norwich wastewater plant (England). A structured biological model with bi-substrate component (particles and soluble material) together with a nitrification model taking into account the organic nitrogen content of the affluent present the most realistic profils for the conditions studied. A sensitivity analysis reveals that a generalization of the model is possible if some parameters identified as critical are adjusted. Key words: mathematical model, activated sludge, water treatment, dynamic modelling, comparison of models.
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Gao, Hanjun, Jianfei Sun, Wuyi Chen, Yidu Zhang, and Qiong Wu. "Structural bionic design for a machine tool column based on leaf veins." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 16 (August 23, 2017): 2764–73. http://dx.doi.org/10.1177/0954406217726565.
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Some biological structures, such as leaf veins, bamboo and animal bones, have excellent mechanical properties after millions of years evolution. By studying the distribution characteristics of biological structures, the performances of mechanical components can be improved using structural bionic design method. In this paper, the internal stiffening ribs of a machine tool column are rearranged based on the structure of leaf veins, and a bionic column is designed. Static and modal analysis of the conventional and bionic column is conducted to investigate the static and dynamic performances by finite element method. Then, static loading experiment and modal test are carried out for further verification. The simulation results have good agreement with the experiment results. Compared with the conventional column, the maximum deformation of the bionic column in experiment is reduced by 24.69%, and the first six-order natural frequencies of bionic column are increased by 48.39%, 12.98%, 10.70%, 5.11%, 3.07%, and 8.44%, respectively.
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Chen, Aimin, Caixia Liu, and Junwei Wang. "Birhythmicity and Hard Excitation from Coupled Synthetic Feedback Loops." Journal of Applied Mathematics 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/694854.
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Synthetic biology opens up the possibility of creating circuits that would not survive in the natural world and studying their behaviors in living cells, expanding our notion of biology. Based on this, we analyze on a synthetic biological system the effect of coupling between two instability-generating mechanisms. The systems considered are two topologically equivalent synthetic networks. In addition to simple periodic oscillations and stable steady state, the system can exhibit a variety of new modes of dynamic behavior: coexistence between two stable periodic regimes (birhythmicity) and coexistence of a stable periodic regime with a stable steady state (hard excitation). Birhythmicity and hard excitation have been proved to exist in biochemical networks. Through bifurcation analysis on these two synthetic cellular networks, we analyze the function of network structure for the collapse and revival of birhythmicity and hard excitation with the variation of parameters. The results have illustrated that the bifurcation space can be divided into four subspaces for which the dynamical behaviors of the system are generically distinct. Our analysis corroborates the results obtained by numerical simulation of the dynamics.
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Cao, M., K. W. Wang, Y. Fujii, and W. E. Tobler. "Development of a Friction Component Model for Automotive Powertrain System Analysis and Shift Controller Design based on Parallel-Modulated Neural Networks." Journal of Dynamic Systems, Measurement, and Control 127, no. 3 (August 17, 2004): 382–405. http://dx.doi.org/10.1115/1.1978909.
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In this study, a new hybrid-neural-network-based friction component model is developed for powertrain (PT) dynamic analysis and controller design. This new model, with significantly improved input-output scalability over conventional neural network configuration, has the capability to serve as a forward as well as an inverse system model. The structural information of the available physical and empirical correlations is utilized to construct a parallel-modulated neural network (PMNN) architecture consisting of small parallel sub-networks reflecting specific mechanisms of the friction component engagement process. The PMNN friction component model isolates the contribution of engagement pressure on engagement torque while identifying the nonlinear characteristics of the pressure-torque correlation. Furthermore, it provides a simple torque formula that is scalable with respect to engagement pressure. The network is successfully trained, tested and analyzed, first using analytical data at the component level and then using experimental data measured in a transmission system. The PMNN friction component model, together with a comprehensive powertrain model, is implemented to simulate the shifting process of an automatic transmission (AT) system under various operating conditions. Simulation results demonstrate that the PMNN model can be effectively applied as a part of powertrain system model to accurately predict transmission shift dynamics. A pressure-profiling scheme using a quadratic polynomial pressure-torque relationship of the PMNN model is developed for transmission shift controller design. The results illustrate that the proposed pressure profiling technique can be applied to a wide range of operating conditions. This study demonstrates the potential of the PMNN architecture as a new dynamic system-modeling concept: It not only outperforms the conventional network modeling techniques in accuracy and numerical efficiency, but also provides a new tool for transmission controller design to improve shift quality.
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Tang, Wei, Yu Liu, Chao Zhang, Juan Cheng, Hu Peng, and Xun Chen. "Green Fluorescent Protein and Phase-Contrast Image Fusion via Generative Adversarial Networks." Computational and Mathematical Methods in Medicine 2019 (December 4, 2019): 1–11. http://dx.doi.org/10.1155/2019/5450373.
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In the field of cell and molecular biology, green fluorescent protein (GFP) images provide functional information embodying the molecular distribution of biological cells while phase-contrast images maintain structural information with high resolution. Fusion of GFP and phase-contrast images is of high significance to the study of subcellular localization, protein functional analysis, and genetic expression. This paper proposes a novel algorithm to fuse these two types of biological images via generative adversarial networks (GANs) by carefully taking their own characteristics into account. The fusion problem is modelled as an adversarial game between a generator and a discriminator. The generator aims to create a fused image that well extracts the functional information from the GFP image and the structural information from the phase-contrast image at the same time. The target of the discriminator is to further improve the overall similarity between the fused image and the phase-contrast image. Experimental results demonstrate that the proposed method can outperform several representative and state-of-the-art image fusion methods in terms of both visual quality and objective evaluation.
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Hamedi, Hamidreza, and Rouzbeh Shad. "Lane-Changing Trajectory Prediction Modeling Using Neural Networks." Advances in Civil Engineering 2022 (February 17, 2022): 1–22. http://dx.doi.org/10.1155/2022/9704632.
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Concerning autonomous driving, lane-changing (LC) is essential, particularly within complicated dynamic settings. It is a challenging task to model LC since driving behavior is complicated and uncertain. The present study adopts a dual-layer feed-forward backpropagation neural network involving sigmoid hidden neurons and linear output neurons for evaluating intrinsic LC complexity. Furthermore, the estimation and validation of the model were performed by large-scale trajectory data. Empirical LC data were obtained from the Next Generation Simulation (NGSIM) project for training and testing the neural network-based LC model. The findings revealed that the introduced model could make precise LC predictions of vehicles under small trajectory errors and satisfactory accuracy. The present work assessed LC beginning/endpoints and velocity estimates by analyzing the vehicles around. It was observed that the neural network model yielded almost the same predictions as the observational LC trajectories as well as following vehicle trajectories on the original and target lanes. Furthermore, for LC behavior characteristic validation, the neural network-produced LC gap distributions underwent comparisons to real-life data, demonstrating the characteristics of LC gap distributions not to differ from the real-life LC behavior substantially. Eventually, the introduced neural network-based LC model was compared to a support vector regression-based LC model. It was found that the trajectory predictions of both models were adequately consistent with the observational data and could capture both lateral and longitudinal vehicle movements. In turn, this demonstrates that the neural network and support vector regression models had satisfactory performance. Also, the proposed models were evaluated using new inputs such as speed, gap, and position of the subject vehicle. The analysis findings indicated that the performance of the proposed NN and SVR models was higher than the model with new inputs.
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Lu, Tsan-Wen, Phillip C. Aoto, Jui-Hung Weng, Cole Nielsen, Jennifer N. Cash, James Hall, Ping Zhang, Sanford M. Simon, Michael A. Cianfrocco, and Susan S. Taylor. "Structural analyses of the PKA RIIβ holoenzyme containing the oncogenic DnaJB1-PKAc fusion protein reveal protomer asymmetry and fusion-induced allosteric perturbations in fibrolamellar hepatocellular carcinoma." PLOS Biology 18, no. 12 (December 28, 2020): e3001018. http://dx.doi.org/10.1371/journal.pbio.3001018.
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When the J-domain of the heat shock protein DnaJB1 is fused to the catalytic (C) subunit of cAMP-dependent protein kinase (PKA), replacing exon 1, this fusion protein, J-C subunit (J-C), becomes the driver of fibrolamellar hepatocellular carcinoma (FL-HCC). Here, we use cryo-electron microscopy (cryo-EM) to characterize J-C bound to RIIβ, the major PKA regulatory (R) subunit in liver, thus reporting the first cryo-EM structure of any PKA holoenzyme. We report several differences in both structure and dynamics that could not be captured by the conventional crystallography approaches used to obtain prior structures. Most striking is the asymmetry caused by the absence of the second cyclic nucleotide binding (CNB) domain and the J-domain in one of the RIIβ:J-C protomers. Using molecular dynamics (MD) simulations, we discovered that this asymmetry is already present in the wild-type (WT) RIIβ2C2 but had been masked in the previous crystal structure. This asymmetry may link to the intrinsic allosteric regulation of all PKA holoenzymes and could also explain why most disease mutations in PKA regulatory subunits are dominant negative. The cryo-EM structure, combined with small-angle X-ray scattering (SAXS), also allowed us to predict the general position of the Dimerization/Docking (D/D) domain, which is essential for localization and interacting with membrane-anchored A-Kinase-Anchoring Proteins (AKAPs). This position provides a multivalent mechanism for interaction of the RIIβ holoenzyme with membranes and would be perturbed in the oncogenic fusion protein. The J-domain also alters several biochemical properties of the RIIβ holoenzyme: It is easier to activate with cAMP, and the cooperativity is reduced. These results provide new insights into how the finely tuned allosteric PKA signaling network is disrupted by the oncogenic J-C subunit, ultimately leading to the development of FL-HCC.