In this study we investigate receptor–ligand binding in the context of antibody–antigen binding. We established a quantitative mapping between macroscopic binding rates of a deterministic differential equation model and their microscopic equivalents as obtained from simulating the spatiotemporal binding kinetics by a stochastic agent-based model. Furthermore, various properties of B cell-derived receptors like their dimensionality of motion, morphology, and binding valency are considered and their impact on receptor–ligand binding kinetics is investigated. The different morphologies of B cell-derived receptors include simple sperical representations as well as more realistic Y-shaped morphologies. These receptors move in different dimensionalities, i.e. either as membrane-anchored receptors or as soluble antibodies. The mapping of the macroscopic and microscopic binding rates allowed us to quantitatively compare different agent-based model variants for the different types of B cell-derived receptors. Our results indicate that the dimensionality of motion governs the binding kinetics and that this predominant impact is quantitatively compensated by the bivalency of these receptors.
Model for antigen binding by B cell-derived receptors
Publications
IMFSegNet: Cost-effective and objective quantification of intramuscular fat in histological sections by deep learning.
Praetorius JP, Walluks K, Svensson CM, Arnold D, Figge MT#
The assessment of muscle condition is of great importance in various research areas. In particular, evaluating the degree of intramuscular fat (IMF) in tissue sections is a challenging task, which today is still mostly performed qualitatively or quantitatively by a highly subjective and error-prone manual analysis. We here realize the mission to make automated IMF […]
Selective Uptake Into Inflamed Human Intestinal Tissue and Immune Cell Targeting by Wormlike Polymer Micelles
Gardey E, Cseresnyés Z, Sobotta FH, Eberhardt J, Haziri D, Grunert PC, Kuchenbrod MT, Gruschwitz FV, Hoeppener S, Schumann M, Gaßler N, Figge MT, Stallmach A#, Brendel JC#
Inflammatory bowel disease (IBD) has become a globally prevalent chronic disease with no causal therapeutic options. Targeted drug delivery systems with selectivity for inflamed areas in the gastrointestinal tract promise to reduce severe drug-related side effects. By creating three distinct nanostructures (vesicles, spherical, and wormlike micelles) from the same amphiphilic block copolymer poly(butyl acrylate)-block-poly(ethylene oxide) […]
pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.
Schälte Y*, Fröhlich F*, Jost PJ*, Vanhoefer J*, Pathirana D, Stapor P, Lakrisenko P, Wang D, Raimúndez E, Merkt S, Schmiester L, Städter P, Grein S, Dudkin E, Doresic D, Weindl D, Hasenauer J#
Summary: Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. pyPESTO is a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, […]