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Data‐driven multiscale modeling of self‐assembly and hierarchical structural formation in biological macromolecular systems
Publikationstyp
Conference Poster not in Proceedings
Date Issued
2020
Sprache
English
Author(s)
TORE-URI
Journal
Start Page
1249
End Page
1249
Citation
Chemie Ingenieur Technik 92 (9): 1249-1249 (2020)
Publisher DOI
Macromolecular systems are present inmany applications of biotechnology andprocess engineering and the physical phenomena involved therein often spreadover vast scales of size and time. To gaininsight, generally applicable models are developed to transfer the essential dynamics (including directional dependency)[1] and complex interaction of biologicalmacromolecules from MD [2] to DEM ina modeling methodology termed by usthe ‘‘molecular discrete element method’’(MDEM). The models are parameterized bottom-up and validated top-down bycomparison with experimental data, whichis obtained from BLI and DLS. As a modelsystem the multi-enzyme pyruvate dehy-drogenase complex (PDC) is used, whichfeatures organized self-structuring pro-cesses and a highly regulated multi-enzymatic machinery dependent upon thestructure.Obtained results for the PDC compo-nent E2 show that the continuous formation and breakup of enzymatic agglomerates can be predicted using the developedMDEM methodology. This approach requires no experimental data fitting andproduces accurate scale-bridging kineticsas well as agglomerate sizes matching corresponding dynamic light scattering data.
DDC Class
620: Ingenieurwissenschaften