10 research outputs found
M-Sabrina/MinDE_analysis_2022: v1.0.0
No full textTools for MinDE pattern analysis, CD Lab, Delft, 202
M-Sabrina/MinDE_analysis_2022:
No full textbug fixes, new supplementary notebooks, save_output option for quickstart noteboo
Comparison of different evaluation strategies for single-molecule force spectroscopy of antibody/antigen interactions
No full textZiel dieser Masterarbeit war es, ein Framework zu entwickeln, das die bisher etablierten Methoden zur Datenauswertung in der dynamischen Kraftspektroskopie (DFS) ergänzt. Um einen Datensatz zur Verfügung zu haben, an dem dieses Framework getestet werden kann, wurde eine Interaktionsstudie an C-reaktivem Protein (CRP) und einem monoklonalem Antikörper (anti-CRP) mithilfe eines Atom-kraftmikroskops (AFM) durchgeführt. Die gesammelten Abrisskräfte wurden hinsichtlich ihrer Abhängigkeit von der loading rate mittels diverser Methoden und theoretischer Modelle analysiert, insbesondere der Modelle von Friddle und Bell-Evans. Das Ziel dieser Analyse ist, charakteristische Parameter der Interaktion zu ermitteln, wie die kinetische Dissoziationsrate k_off. Der wichtigste Ansatz basierte auf dem Binnen der Daten in eine gewisse Anzahl an Bins pro Zehnerpotenz im Bezug auf die loading rate. Dies erlaubt eine Analyse der Kraft-Verteilungsfunktionen, errechnet für die Datenpunkte in den jeweiligen Bins. Diese Kraft-Verteilungsfunktionen ermöglichen es, individuelle Populationen innerhalb der Daten zu identifizieren, welche dann von multiplen Bindungen oder anderen Interaktionstypen stammen können. Zu diesem Zweck wurden die bin-weisen Kraft-Verteilungen mit Gauß-Funktionen gefittet, wobei jede Gauß-Funktion als eine Population interpretiert wurde. Die vollständige Analyse umfasst Least-Squares-Fits der ursprünglichen Daten, der mittleren Kräfte berechnet für die gebinnten Daten und der mittleren Kräfte, die man aus den isolierten Populationen erhalten hat. Die Methodik wurde in einem MATLAB-basierten Framework implementiert, mit dem zukünftig ähnliche Analysen vorgenommen werden können. Das Framework wurde auch an den Daten eines anderen Systems (mesenchymale Stammzellen und ein spezifischer Antikörper) getestet.The goal of this masters thesis was to develop a framework that would complement formerly established methods in dynamic force spectroscopy (DFS) data evaluation. To have a dataset on which this evaluation framework could be put to the test, an interaction study on C-reactive protein (CRP) and a monoclonal antibody (anti-CRP) was performed using an atomic force microscope (AFM). The collected unbinding force data were analyzed with respect to their dependence on the loading rate with a few methods and theoretical models, most prominently Friddle and Bell-Evans model. The goal of this analysis is to extract characteristic parameters of the interaction, like the kinetic off-rate k_off. The most important approach was based on binning of the data into a certain number of bins per logarithmic decade with respect to the loading rate. Doing so allows to perform an analysis of the force distribution functions calculated for the datapoints in the respective bins. These force distributions make it possible to identify individual populations within the data, which may correspond to multibonds or other types of interactions occurring within the system. For this purpose, Gaussian functions were fitted onto the bin-wise force distributions and interpreted as representing one population each. The whole analysis comprises least-squares fitting of the original data, of the mean forces calculated for the bins and of the mean forces obtained for the isolated populations. This method was implemented in a MATLAB-based framework that allows to perform similar analyses on other systems in the future. The framework was also tested on data recorded on another biological system (mesenchymal stem cells and a specific monoclonal antibody).submitted by Sabrina Meindlhumer, B.Sc.Masterarbeit Universität Linz 201
Comparison of different evaluation strategies for single-molecule force spectroscopy of antibody/antigen interactions
No full textZiel dieser Masterarbeit war es, ein Framework zu entwickeln, das die bisher etablierten Methoden zur Datenauswertung in der dynamischen Kraftspektroskopie (DFS) ergänzt. Um einen Datensatz zur Verfügung zu haben, an dem dieses Framework getestet werden kann, wurde eine Interaktionsstudie an C-reaktivem Protein (CRP) und einem monoklonalem Antikörper (anti-CRP) mithilfe eines Atom-kraftmikroskops (AFM) durchgeführt. Die gesammelten Abrisskräfte wurden hinsichtlich ihrer Abhängigkeit von der loading rate mittels diverser Methoden und theoretischer Modelle analysiert, insbesondere der Modelle von Friddle und Bell-Evans. Das Ziel dieser Analyse ist, charakteristische Parameter der Interaktion zu ermitteln, wie die kinetische Dissoziationsrate k_off. Der wichtigste Ansatz basierte auf dem Binnen der Daten in eine gewisse Anzahl an Bins pro Zehnerpotenz im Bezug auf die loading rate. Dies erlaubt eine Analyse der Kraft-Verteilungsfunktionen, errechnet für die Datenpunkte in den jeweiligen Bins. Diese Kraft-Verteilungsfunktionen ermöglichen es, individuelle Populationen innerhalb der Daten zu identifizieren, welche dann von multiplen Bindungen oder anderen Interaktionstypen stammen können. Zu diesem Zweck wurden die bin-weisen Kraft-Verteilungen mit Gauß-Funktionen gefittet, wobei jede Gauß-Funktion als eine Population interpretiert wurde. Die vollständige Analyse umfasst Least-Squares-Fits der ursprünglichen Daten, der mittleren Kräfte berechnet für die gebinnten Daten und der mittleren Kräfte, die man aus den isolierten Populationen erhalten hat. Die Methodik wurde in einem MATLAB-basierten Framework implementiert, mit dem zukünftig ähnliche Analysen vorgenommen werden können. Das Framework wurde auch an den Daten eines anderen Systems (mesenchymale Stammzellen und ein spezifischer Antikörper) getestet.The goal of this masters thesis was to develop a framework that would complement formerly established methods in dynamic force spectroscopy (DFS) data evaluation. To have a dataset on which this evaluation framework could be put to the test, an interaction study on C-reactive protein (CRP) and a monoclonal antibody (anti-CRP) was performed using an atomic force microscope (AFM). The collected unbinding force data were analyzed with respect to their dependence on the loading rate with a few methods and theoretical models, most prominently Friddle and Bell-Evans model. The goal of this analysis is to extract characteristic parameters of the interaction, like the kinetic off-rate k_off. The most important approach was based on binning of the data into a certain number of bins per logarithmic decade with respect to the loading rate. Doing so allows to perform an analysis of the force distribution functions calculated for the datapoints in the respective bins. These force distributions make it possible to identify individual populations within the data, which may correspond to multibonds or other types of interactions occurring within the system. For this purpose, Gaussian functions were fitted onto the bin-wise force distributions and interpreted as representing one population each. The whole analysis comprises least-squares fitting of the original data, of the mean forces calculated for the bins and of the mean forces obtained for the isolated populations. This method was implemented in a MATLAB-based framework that allows to perform similar analyses on other systems in the future. The framework was also tested on data recorded on another biological system (mesenchymal stem cells and a specific monoclonal antibody).submitted by Sabrina Meindlhumer, B.Sc.Masterarbeit Universität Linz 201
Quantitative analysis of surface wave patterns of Min proteins
No full textThe Min protein system is arguably the best-studied model system for biological pattern formation. It exhibits pole-to-pole oscillations in E. coli bacteria as well as a variety of surface wave patterns in in vitro reconstitutions. Such Min surface wave patterns pose particular challenges to quantification as they are typically only semi-periodic and non-stationary. Here, we present a methodology for quantitatively analysing such Min patterns, aiming for reproducibility, user-independence, and easy usage. After introducing pattern-feature definitions and image-processing concepts, we present an analysis pipeline where we use autocorrelation analysis to extract global parameters such as the average spatial wavelength and oscillation period. Subsequently, we describe a method that uses flow-field analysis to extract local properties such as the wave propagation velocity. We provide descriptions on how to practically implement these quantification tools and provide Python code that can directly be used to perform analysis of Min patterns.BN/Cees Dekker La
Additional data for: Directing Min protein patterns with advective bulk flow
No full text<p>Data and analysis associated with 2024 addition to:<br>"Direction Min protein patterns with advective bulk flow" (NatComms, 2023)<br>Companion repository to <a href="../records/7339803">10.5281/zenodo.7339803</a></p>
<p>Please consider the readme for more information.</p>
DataSheet1_Quantitative analysis of surface wave patterns of Min proteins.pdf
No full textThe Min protein system is arguably the best-studied model system for biological pattern formation. It exhibits pole-to-pole oscillations in E. coli bacteria as well as a variety of surface wave patterns in in vitro reconstitutions. Such Min surface wave patterns pose particular challenges to quantification as they are typically only semi-periodic and non-stationary. Here, we present a methodology for quantitatively analysing such Min patterns, aiming for reproducibility, user-independence, and easy usage. After introducing pattern-feature definitions and image-processing concepts, we present an analysis pipeline where we use autocorrelation analysis to extract global parameters such as the average spatial wavelength and oscillation period. Subsequently, we describe a method that uses flow-field analysis to extract local properties such as the wave propagation velocity. We provide descriptions on how to practically implement these quantification tools and provide Python code that can directly be used to perform analysis of Min patterns.</p
Directing Min protein patterns with advective bulk flow
No full textThe Min proteins constitute the best-studied model system for pattern formation in cell biology. We theoretically predict and experimentally show that the propagation direction of in vitro Min protein patterns can be controlled by a hydrodynamic flow of the bulk solution. We find downstream propagation of Min wave patterns for low MinE:MinD concentration ratios, upstream propagation for large ratios, but multistability of both propagation directions in between. Whereas downstream propagation can be described by a minimal model that disregards MinE conformational switching, upstream propagation can be reproduced by a reduced switch model, where increased MinD bulk concentrations on the upstream side promote protein attachment. Our study demonstrates that a differential flow, where bulk flow advects protein concentrations in the bulk, but not on the surface, can control surface-pattern propagation. This suggests that flow can be used to probe molecular features and to constrain mathematical models for pattern-forming systems.BN/Cees Dekker La
Establishing an Effective Open Science Team: A Recipe for Cultural Change in Institutions
No full textFull book available at https://www.ala.org/sites/default/files/2025-06/9798892553667.pd
