From Frosen Image-Based Bioflow Simulations to Integrative Modeling of Living Interfaces

Kelimu, Muhetaer; Thiriet, Marc

doi:10.4028/www.scientific.net/AMR.33-37.863

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The Study of Numerical Stability in the SPH Method
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A Simple Method in Preventing Numerical Fractures of SPH Computations
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The Numerical Simulation of a Bird-Impact on an Aircraft Windshield by Using the SPH Method
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An Information-Theoretic Approach for Computational Material Modeling
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From Frosen Image-Based Bioflow Simulations to Integrative Modeling of Living Interfaces
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Analysis of Stick-Slip Motion by the Rate-Dependent Friction Model
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Development of Multi-Scale Computation Framework to Investigate the Failure Behavior of the Materials
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Numerical Limit Analysis of Two Dimensional Structures by the MLPG Method with Natural Neighbour Interpolation
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Computer Simulation of Irradiation Growth in Zirconium
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Home Advanced Materials Research Advances in Fracture and Materials BehaviorFrom Frosen Image-Based Bioflow Simulations to...

From Frosen Image-Based Bioflow Simulations to Integrative Modeling of Living Interfaces

Abstract:

Air and blood flow in a set of deformable conduits. Nowadays, computational models of biofluid flow are based on zoomed domains reconstructed from medical image processing. Such modeling is already very useful in medical practice. However it splits the domain of interest from the remaining parts of the network. Most often, crude boundary conditions are used (stress free outlet BCs). Moreover, the living system corresponds to a frosen state, although physiological flows interact with cell lining the interface between fluid and solid. Therefore, computational models of flow in normal and damaged bioconduits require couplings. The talk will illustrate cases for which the nanoscale must be incorporate for future research.

Info:

Periodical:

Advanced Materials Research (Volumes 33-37)

Main Theme:

Advances in Fracture and Materials Behavior

Edited by:

Wei Yang, Mamtimin Geni, Tiejun Wang and Zhuo Zhuang

Pages:

863-866

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.33-37.863

Citation:

M. Kelimu and M. Thiriet, "From Frosen Image-Based Bioflow Simulations to Integrative Modeling of Living Interfaces", Advanced Materials Research, Vols. 33-37, pp. 863-866, 2008

Online since:

March 2008

Authors:

Muhetaer Kelimu, Marc Thiriet

Keywords:

Image Based Simulation, Mechanotransduction, Multi-Scale Modelling, Navier-Stokes Equation

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$38.00

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Paper TitlePages

Sub-Pharmacophore Generation of JNK3 Inhibitors

Authors: Yan Ling Zhang, Yuan Ming Wang, Yan Jiang Qiao

Abstract: The structure-based pharmacophore (SBP) model is consisted by the complementarity of the chemical features and space of the interaction between the ligand and receptor. The SBP models always have a high specificity which can only represent the specific class of the ligand. To simplify the models, sub-pharmacophore was then proposed in present study, and was expected to have and only have the most important or the common chemical features which play the major role in the interaction of ligand and receptor. Sub-pharmacophore should contain 4-6 features, the higher specificity with more features, and vice versa. The sub-pharmacophore was generated by the random combination of features from the structure-based models. With the MDL Drug Data Report database used as the testing database, a new metric CAI (comprehensive appraisal index), which integrated the metrics of E and A%, was defined and used to determine the best sub-pharmacophore model. C-Jun N-terminal kinase (JNKs) is one of the mitogen-activated protein kinase family, and widely involved in immune response and inflammatory response, and other pathological processes. JNK3 is mainly distributed in the brain and nervous system. In present study, twenty-five initial SBP models of JNK3 inhibitors were directly constructed from the Protein Data Bank (PDB) complexes by the LigandScout software. Then, 1018 sub-pharmacophore models were obtained from the 25 initial models. Finally, the best sub-pharmacophore was determined which was simplified from the initial model generated from the 3FI2 complex, and included four features: one hydrogen bond donor, one hydrogen bond acceptor, and two hydrophobic groups. The metrics of E, A% and CAI value of the best sub-pharmacophore model are 17.47, 31.15 and 5.44, respectively. The potential JNK3 inhibitors were then identified from Chinese herbs with the best sub-pharmacophore model, and 286 compounds were obtained.

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