ArticleComputational and structural biotechnology journal2020
A NanoFE simulation-based surrogate machine learning model to predict mechanical functionality of protein networks from live confocal imaging.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.Journal of mathematical biology · 2025Review
- Differential GTP-dependent in-vitro polymerization of recombinant Physcomitrella FtsZ proteins.Scientific reports · 2025Article
- Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review.Computational and structural biotechnology journal · 2021Review
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6 authors.
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Abstract
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
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