Automated Reasoning for Systems Biology and Medicine (eBook, PDF)
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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…mehr

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Produktbeschreibung
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
“This book presents a comprehensive spectrum of model-focused analysis techniques for biological systems ...an essential resource for tracking the developments of a fast moving field that promises to revolutionize biology and medicine by the automated analysis of models and data.” Prof Luca Cardelli FRS, University of Oxford
Autorenporträt
Dr. Paolo Zuliani is a Senior Lecturer at the School of Computing at Newcastle University, UK. He received his Laurea degree in Computer Science from the Università degli Studi di Milano, Italy, and his DPhil in Computer Science from the University of Oxford, UK. Dr. Zuliani's areas of expertise include formal and automated reasoning methods for computing systems, with a focus on probabilistic and quantum systems. He is particularly interested in the verification of biological systems, cyber-physical systems, and quantum programs. Pietro Liò is a Professor of Computational Biology at the Department of Computer Science and Technology at the University of Cambridge, UK. He holds a PhD in Complex Systems and Non Linear Dynamics (University of Firenze, Italy) and a PhD in Genetics (University of Pavia, Italy). His research interests include developing methodologies by integrating bioinformatics, machine learning and modelling approaches. In particular, he is interestedin artificial intelligence/machine learning and computational biology methods for biological and health data, predictive models in personalised and precision medicine, machine learning methods for the integration of multi-scale, multi-omics and multi-physics data, and predictive comorbidity models. He is on the steering committee of Cambridge Big Data, the MPhil in Computational Biology and the UK Virtual Physiological Human.