Automated Reasoning for Systems Biology and Medicine

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 interested in 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.

Part I: Model Checking.- Chapter 1. Model Checking Approach to the Analysis of Biological Systems.- Chapter2. Automated Reasoning for the Synthesis and Analysis of Biological Programs.- Chapter 3. Statistical Model Checking based Analysis Techniques of Biological Networks.- Chapter 4. Models, Devices, Properties and Verification for the Artificial Pancreas.- Chapter 5. Using State Space Exploration to Determine How Gene Regulatory Networks Constrain Mutation Order in Cancer Evolution.- Part II: Formal Methods and Logic.- Chapter 6. Set-based Analysis for Biological Modelling.- Chapter 7. Logic and Linear Programs to Understand Cancer Response.- Chapter 8. Logic-Based Formalization of System Requirements for Integrated Clinical Environments.- Chapter 9. Balancing prescriptions with Constraint Solvers.- Chapter 10. Metastable Regimes and Tipping Points of Biochemical Networks with Potential Applications in Precision Medicine.- Part III:  Stochastic Modelling and Analysis.-Chapter 11. Stochastic Spatial Modelling of the Remyelination Process in Multiple Sclerosis Lesions.- Chapter 12. Approximation Techniques for Stochastic Analysis of Biological Systems.- Chapter 13. A Graphical Approach for the Hybrid Modelling of Intracellular Calcium Dynamics Based on Coloured Hybrid Petri Nets.- Chapter 14. Methods for Personalised Delivery Rate Computation for IV Administered Anesthetic Propofol.- Part IV: Machine Learning and Artificial Intelligence.- Chapter 15. Towards the Integration of Metabolic Network Modelling and Machine Learning for the Routine Analysis of High-Throughput Patient Data.- Chapter 16. Opportunities and Challenges in Applying Artificial Intelligence to Bioengineering.- Chapter 17. Deep Learning with Convolutional Neural Networks for Histopathology Image Analysis.