| dc.contributor.advisor | Ebert, Antje PD Dr. | |
| dc.contributor.author | Xu, Hang | |
| dc.date.accessioned | 2023-07-05T15:44:31Z | |
| dc.date.available | 2024-06-29T00:50:05Z | |
| dc.date.issued | 2023-07-05 | |
| dc.identifier.uri | http://resolver.sub.uni-goettingen.de/purl?ediss-11858/14756 | |
| dc.identifier.uri | http://dx.doi.org/10.53846/goediss-9980 | |
| dc.format.extent | XX Seiten | de |
| dc.language.iso | eng | de |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.ddc | 571.4 | de |
| dc.title | Integrational modelling for molecular analysis in a CRISPR/Cas edited iPSC-cardiomyocyte model of Dilated Cardiomyopathy | de |
| dc.type | cumulativeThesis | de |
| dc.contributor.referee | Habeck, Michael Dr. | |
| dc.date.examination | 2023-06-30 | de |
| dc.description.abstracteng | Machine learning has shown promising results in the prediction and diagnosis of cardiac diseases. By analyzing large amounts of patient data, such as imaging, genomics, or molecular phenotyping data, machine learning algorithms can identify patterns and make prediction for disease phenotype. DCM is a complex cardiac disease that is influenced by a variety of genetic and environmental factors. Genetic mutations in sarcomere proteins frequently cause dilated cardiomyopathy (DCM) and lead to heart failure. Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are an alternative model for disease modelling and drug testing. The CRISPR/Cas9 site-specific gene editing in combination with iPSC-based disease modeling enables to deepen insight into molecular disease dysfunctions as well as development of new diagnostic tools. In this study, we generated a human iPSC-CM model of the inherited mutation in sarcomeric troponin (TnT), TnT-R141W, that causes DCM in patients. We focused on key molecular disease phenotypes observed in DCM iPSC-CMs due to sarcomere protein disorganization in presence of DCM mutations: altered Ca2+ handling, reduced beating force, and impaired contractility. We applied a non-negative blind deconvolution (NNBD)-based method to analyze and uniform the parameters from these datasets. We also implemented a multimodal data fusion approach integrating features of Ca2+ transients, beating force, and contractility profiling. A combination of different machine learning algorithms was tested for their capacity to classify DCM patho-phenotypes. Overall, these findings may support further development of machine learning tools for diagnostic applications and evaluation of therapeutic agents. | de |
| dc.contributor.coReferee | Salditt, Tim Prof. Dr. | |
| dc.subject.eng | Contractility | de |
| dc.subject.eng | atomic force microscopy | de |
| dc.subject.eng | signal transduction | |
| dc.identifier.urn | urn:nbn:de:gbv:7-ediss-14756-5 | |
| dc.affiliation.institute | Göttinger Graduiertenschule für Neurowissenschaften, Biophysik und molekulare Biowissenschaften (GGNB) | de |
| dc.subject.gokfull | Biologie (PPN619462639) | de |
| dc.description.embargoed | 2024-06-29 | de |
| dc.identifier.ppn | 1852119241 | |
| dc.notes.confirmationsent | Confirmation sent 2023-07-05T19:45:01 | de |