Automated Reasoning for Systems Biology and Medicine

Description

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 Front Matter ....Pages i-xi Front Matter ....Pages 1-1 Model Checking Approach to the Analysis of Biological Systems (Nikola Beneš, Luboš Brim, Samuel Pastva, David Šafránek)....Pages 3-35 Automated Reasoning for the Synthesis and Analysis of Biological Programs (Sara-Jane Dunn, Boyan Yordanov)....Pages 37-62 Statistical Model Checking-Based Analysis of Biological Networks (Bing Liu, Benjamin M. Gyori, P. S. Thiagarajan)....Pages 63-92 Models, Devices, Properties, and Verification of Artificial Pancreas Systems (Taisa Kushner, B. Wayne Bequette, Faye Cameron, Gregory Forlenza, David Maahs, Sriram Sankaranarayanan)....Pages 93-131 Using State Space Exploration to Determine How Gene Regulatory Networks Constrain Mutation Order in Cancer Evolution (Matthew A. Clarke, Steven Woodhouse, Nir Piterman, Benjamin A. Hall, Jasmin Fisher)....Pages 133-153 Front Matter ....Pages 155-155 Set-Based Analysis for Biological Modeling (Thao Dang, Tommaso Dreossi, Eric Fanchon, Oded Maler, Carla Piazza, Alexandre Rocca)....Pages 157-189 Logic and Linear Programs to Understand Cancer Response (Misbah Razzaq, Lokmane Chebouba, Pierre Le Jeune, Hanen Mhamdi, Carito Guziolowski, Jérémie Bourdon)....Pages 191-213 Logic-Based Formalization of System Requirements for Integrated Clinical Environments (Cinzia Bernardeschi, Andrea Domenici, Paolo Masci)....Pages 215-242 Balancing Prescriptions with Constraint Solvers (Juliana K. F. Bowles, Marco B. Caminati)....Pages 243-267 Metastable Regimes and Tipping Points of Biochemical Networks with Potential Applications in Precision Medicine (Satya Swarup Samal, Jeyashree Krishnan, Ali Hadizadeh Esfahani, Christoph Lüders, Andreas Weber, Ovidiu Radulescu)....Pages 269-295 Front Matter ....Pages 297-297 Stochastic Spatial Modelling of the Remyelination Process in Multiple Sclerosis Lesions (Ludovica Luisa Vissat, Jane Hillston, Anna Williams)....Pages 299-326 Approximation Techniques for Stochastic Analysis of Biological Systems (Thakur Neupane, Zhen Zhang, Curtis Madsen, Hao Zheng, Chris J. Myers)....Pages 327-348 A Graphical Approach for Hybrid Modelling of Intracellular Calcium Dynamics Based on Coloured Hybrid Petri Nets (Amr Ismail, Mostafa Herajy, Monika Heiner)....Pages 349-367 Methods for Personalised Delivery Rate Computation for IV Administered Anesthetic Propofol (Alena Simalatsar, Monia Guidi, Pierre Roduit, Thierry Buclin)....Pages 369-397 Front Matter ....Pages 399-399 Towards the Integration of Metabolic Network Modelling and Machine Learning for the Routine Analysis of High-Throughput Patient Data (Maria Pires Pacheco, Tamara Bintener, Thomas Sauter)....Pages 401-424 Opportunities and Challenges in Applying Artificial Intelligence to Bioengineering (Fusun Yaman, Aaron Adler, Jacob Beal)....Pages 425-452 Deep Learning with Convolutional Neural Networks for Histopathology Image Analysis (Dragan Bošnački, Natal van Riel, Mitko Veta)....Pages 453-469 Back Matter ....Pages 471-474