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A NanoFE simulation-based surrogate machine learning model to predict mechanical functionality of protein networks from live confocal imaging.
Asgharzadeh, Pouyan; Birkhold, Annette I; Trivedi, Zubin; Özdemir, Bugra; Reski, Ralf; Röhrle, Oliver.
Afiliação
  • Asgharzadeh P; Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany.
  • Birkhold AI; Stuttgart Center for Simulation Science (SC SimTech), Stuttgart, Germany.
  • Trivedi Z; Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany.
  • Özdemir B; Stuttgart Center for Simulation Science (SC SimTech), Stuttgart, Germany.
  • Reski R; Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany.
  • Röhrle O; Plant Biotechnology, Faculty of Biology, University of Freiburg, Freiburg, Germany.
Comput Struct Biotechnol J ; 18: 2774-2788, 2020.
Article em En | MEDLINE | ID: mdl-33101614
Sub-cellular mechanics plays a crucial role in a variety of biological functions and dysfunctions. Due to the strong structure-function relationship in cytoskeletal protein networks, light can be shed on their mechanical functionality by investigating their structures. Here, we present a data-driven approach employing a combination of confocal live imaging of fluorescent tagged protein networks, in silico mechanical experiments and machine learning to investigate this relationship. Our designed image processing and nanoFE mechanical simulation framework resolves the structure and mechanical behaviour of cytoskeletal networks and the developed gradient boosting surrogate models linking network structure to its functionality. In this study, for the first time, we perform mechanical simulations of Filamentous Temperature Sensitive Z (FtsZ) complex protein networks with realistic network geometry depicting its skeletal functionality inside organelles (here, chloroplasts) of the moss Physcomitrella patens. Training on synthetically produced simulation data enables predicting the mechanical characteristics of FtsZ network purely based on its structural features ( R 2 ⩾ 0.93 ), therefore allowing to extract structural principles enabling specific mechanical traits of FtsZ, such as load bearing and resistance to buckling failure in case of large network deformation.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article