© 2019

Automated Reasoning for Systems Biology and Medicine

  • Pietro Liò
  • Paolo Zuliani
  • The first dedicated volume on formal reasoning techniques applied to medical systems, including personalized medicine

  • Includes novel contributions on automated reasoning, formal methods, and verification by internationally leading researchers

  • Features state-of-the-art research, including chapters on machine learning and artificial intelligence applications


Part of the Computational Biology book series (COBO, volume 30)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Model Checking

    1. Front Matter
      Pages 1-1
    2. Nikola Beneš, Luboš Brim, Samuel Pastva, David Šafránek
      Pages 3-35
    3. Bing Liu, Benjamin M. Gyori, P. S. Thiagarajan
      Pages 63-92
    4. Taisa Kushner, B. Wayne Bequette, Faye Cameron, Gregory Forlenza, David Maahs, Sriram Sankaranarayanan
      Pages 93-131
    5. Matthew A. Clarke, Steven Woodhouse, Nir Piterman, Benjamin A. Hall, Jasmin Fisher
      Pages 133-153
  3. Formal Methods and Logic

    1. Front Matter
      Pages 155-155
    2. Thao Dang, Tommaso Dreossi, Eric Fanchon, Oded Maler, Carla Piazza, Alexandre Rocca
      Pages 157-189
    3. Misbah Razzaq, Lokmane Chebouba, Pierre Le Jeune, Hanen Mhamdi, Carito Guziolowski, Jérémie Bourdon
      Pages 191-213
    4. Cinzia Bernardeschi, Andrea Domenici, Paolo Masci
      Pages 215-242
    5. Juliana K. F. Bowles, Marco B. Caminati
      Pages 243-267
    6. Satya Swarup Samal, Jeyashree Krishnan, Ali Hadizadeh Esfahani, Christoph Lüders, Andreas Weber, Ovidiu Radulescu
      Pages 269-295
  4. Stochastic Modelling and Analysis

    1. Front Matter
      Pages 297-297
    2. Ludovica Luisa Vissat, Jane Hillston, Anna Williams
      Pages 299-326
    3. Thakur Neupane, Zhen Zhang, Curtis Madsen, Hao Zheng, Chris J. Myers
      Pages 327-348
    4. Alena Simalatsar, Monia Guidi, Pierre Roduit, Thierry Buclin
      Pages 369-397
  5. Machine Learning and Artificial Intelligence

    1. Front Matter
      Pages 399-399

About this book


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 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


Model Selection Parameter Inference Network Structure Identification Machine Learning Model Checking

Editors and affiliations

  • Pietro Liò
    • 1
  • Paolo Zuliani
    • 2
  1. 1.Department of Computer Science and TechnologyUniversity of CambridgeCambridgeUK
  2. 2.School of ComputingNewcastle UniversityNewcastleUK

About the editors

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.

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“This book presents a comprehensive spectrum of model-focused analysis techniques for biological systems: model checking, theorem proving, and machine learning. The model classes include discrete, stochastic, and hybrid models, with a focus on mathematical reasoning. The state-of-the-art chapters deliver perspectives and case studies in each area, providing 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, UK)