Automated reasoning for systems biology and medicine /

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Bibliographic Details
Imprint:Cham, Switzerland : Springer, [2019]
Description:1 online resource
Language:English
Series:Computational biology ; volume 30
Computational biology ; v. 30.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11912756
Hidden Bibliographic Details
Other authors / contributors:Liò, Pietro, editor.
Zuliani, Paolo, editor.
ISBN:9783030172978
303017297X
9783030172985
3030172988
9783030172992
3030172996
9783030172961
3030172961
Digital file characteristics:text file PDF
Notes:Includes bibliographical references and index.
Online resource; title from digital title page (viewed on July 18, 2019).
Summary:"This book presents outstanding contributions in an exciting, new and multidisciplinary research area: the application of formal, automated reasoning techniques to analyse complex models in systems biology and systems medicine. Automated reasoning is a field of computer science devoted to the development of algorithms that yield trustworthy answers, providing a basis of sound logical reasoning. For example, in the semiconductor industry formal verification is instrumental to ensuring that chip designs are free of defects (or "bugs"). Over the past 15 years, systems biology and systems medicine have been introduced in an attempt to understand the enormous complexity of life from a computational point of view. This has generated a wealth of new knowledge in the form of computational models, whose staggering complexity makes manual analysis methods infeasible. Sound, trusted, and automated means of analysing the models are thus required in order to be able to trust their conclusions. Above all, this is crucial to engineering safe biomedical devices and to reducing our reliance on wet-lab experiments and clinical trials, which will in turn produce lower economic and societal costs. Some examples of the questions addressed here include: Can we automatically adjust medications for patients with multiple chronic conditions? Can we verify that an artificial pancreas system delivers insulin in a way that ensures Type 1 diabetic patients never suffer from hyperglycaemia or hypoglycaemia? And lastly, can we predict what kind of mutations a cancer cell is likely to undergo? This book brings together leading researchers from a number of highly interdisciplinary areas, including: · Parameter inference from time series · Model selection · Network structure identification · Machine learning · Systems medicine · Hypothesis generation from experimental data · Systems biology, systems medicine, and digital pathology · Verification of biomedical devices"--Publisher's website.
Other form:Printed edition: 9783030172961
Printed edition: 9783030172985
Printed edition: 9783030172992
Standard no.:10.1007/978-3-030-17297-8