Analysis of Single-Cell Data ODE Constrained Mixture Modeling and A
Carolin Loos introduces two novel approaches for the analysis of single-cell data. Both approaches can be used to study cellular heterogeneity and therefore advance a holistic understanding of biological processes. The first method, ODE constrained mixtur
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Analysis of Single-Cell Data ODE Constrained Mixture Modeling and Approximate Bayesian Computation
BestMasters
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Carolin Loos
Analysis of Single-Cell Data ODE Constrained Mixture Modeling and Approximate Bayesian Computation
Carolin Loos München, Germany
BestMasters ISBN 978-3-658-13233-0 ISBN 978-3-658-13234-7 (eBook) DOI 10.1007/978-3-658-13234-7 Library of Congress Control Number: 2016935216 Springer Spektrum © Springer Fachmedien Wiesbaden 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer Spektrum imprint is published by Springer Nature The registered company is Springer Fachmedien Wiesbaden GmbH
Acknowledgements I would like to begin by offering my sincerest gratitude to my supervisor Dr. Jan Hasenauer for his immense support, advice and encouragement, and for truly inspiring my interest in this field of research. I also gratefully acknowledge Dr. Carsten Marr for always taking the time to answer my questions and help me with my problems. Furthermore, I would like to thank the members of the groups Data-driven Computational Modeling and Quantitative Single Cell Dynamics for their feedback, explanations and interesting discussions. I am highly indebted to Prof. Dr. Dr. Fabian Theis for giving me the opportunity to work on this interesting project at the ICB. It was a pleasure to explore this fascinating field of research and to write my thesis in such an inspiring working environment. Last, but not least, I would like to thank my family and friends f
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