Automated Reasoning for Systems Biology and Medicine

471 Pages·2019·16.28 MB·English

by  Pietro Liò, Paolo Zuliani

Computational Biology
Pietro Liò
Paolo Zuliani    Editors
Automated Reasoning
for Systems Biology
and Medicine
Computational Biology
Volume 30
Editors-in-Chief
Andreas Dress
CAS-MPG PartnerInstitute for Computational Biology,Shanghai, China
Michal Linial
HebrewUniversity of Jerusalem,Jerusalem, Israel
OlgaTroyanskaya
Princeton University, Princeton, NJ, USA
Martin Vingron
MaxPlanckInstitute for Molecular Genetics, Berlin,Germany
Editorial Board Members
RobertGiegerich, University of Bielefeld,Bielefeld, Germany
Janet Kelso, MaxPlanckInstitute for Evolutionary Anthropology, Leipzig, Germany
Gene Myers,MaxPlanck Institute of Molecular Cell Biology andGenetics, Dresden,
Germany
PavelPevzner, University of California,San Diego, CA,USA
Advisory Editors
Gordon Crippen, University of Michigan,Ann Arbor,MI,USA
JosephFelsenstein, University of Washington,Seattle, WA,USA
Dan Gusfield,University of California, Davis, CA,USA
Sorin Istrail, Brown University, Providence,RI, USA
ThomasLengauer, MaxPlanckInstitute forComputer Science, Saarbrücken, Germany
Marcella McClure, MontanaState University, Bozeman,MT, USA
Martin Nowak, HarvardUniversity, Cambridge, MA, USA
DavidSankoff, University of Ottawa,Ottawa, ON,Canada
RonShamir, TelAvivUniversity, TelAviv, Israel
Mike Steel,University ofCanterbury, Christchurch, NewZealand
Gary Stormo,Washington University in St.Louis, St.Louis, MO, USA
Simon Tavaré,University ofCambridge, Cambridge, UK
Tandy Warnow,University of Illinois at Urbana-Champaign, Urbana, IL, USA
LonnieWelch, OhioUniversity, Athens, OH, USA
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More information about this series at http://www.springer.com/series/5769
ò
Pietro Li Paolo Zuliani
(cid:129)
Editors
Automated Reasoning
for Systems Biology
and Medicine
123
Editors
Pietro Liò PaoloZuliani
Department ofComputer Science Schoolof Computing
andTechnology Newcastle University
University of Cambridge Newcastle, UK
Cambridge, UK
ISSN 1568-2684 ISSN 2662-2432 (electronic)
Computational Biology
ISBN978-3-030-17296-1 ISBN978-3-030-17297-8 (eBook)
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Preface
ThisbookoriginatesfromtheInternationalWorkshoponAutomatedReasoningfor
Systems Biology and Medicine (ARSBM 2016) that was held on 20 September
2016,attheComputerLaboratoryoftheUniversityofCambridge.ThisInstitution
—truly a place of innovators—has a long, successful tradition for cultivating
interdisciplinarity and multidisciplinarity. In the same spirit, this volume presents
theverybestresearchinanexcitingnew,multidisciplinaryarea:theapplicationof
formal, automated reasoning techniques for analysing complex models and data in
Systems Biology and Systems Medicine.
Automated reasoning is the field of computer science devoted to the develop-
ment ofalgorithms that return trusted answers, sothat soundlogical reasoningcan
be built upon. The main focus of the workshop was on the theory of
delta-decidability (Gao, Avigad, Clarke. Delta-Decidability over the Reals, LICS
2012) and its biological and biomedical applications. However, in reality, the
variety of scientific topics discussed ranged from computational modelling, to
formal methods, to machine learning and other fields of computer science. The
workshop predated the conference CMSB 2016 so it generated cross-fertilisation
andresonanceparticularlybetweenfundamentalissuesandinnovativeapplications.
SystemsBiologyandSystemsMedicinestartedinthepast20yearsaseffortsto
understand the enormous complexity of life from a computational point of view.
This has generated a wealth of new knowledge in the form of both computational
models and data, whose staggering complexity makes manual analysis methods
infeasible.Sound,trustedandautomatedwaystoanalysemodelsanddataarethus
required in order to be able to trust the models’ predictions and data analysis
outcomes. Overall, this is crucial to engineering safe biomedical devices and safe
clinical protocols, and reducing our reliance on wet-lab experiments and clinical
trials, thus reducing both economic and societal costs.
Some examples of the questions tackled in the area include: can we automati-
cally revise medications in patients with multiple chronic conditions? Again, can
we verify that an artificial pancreas system delivers insulin in a way that Type 1
diabeticpatientsneversufferfromhyperglycaemiaorhypoglycaemia?Finally,can
we predict the effect of a mutation on cancer cells?
v
vi Preface
These aspects are clearly reflected in the book, which contains 17 high-quality
chaptersfromworld-leadingresearchersworkingonrelatedfields.Eachchapterhas
been peer-reviewed by at least two independent reviewers from an international
pool of experts (see the Reviewers list below). The chapters are grouped in four
different clusters based on the technique used:
(cid:129) Model Checking
(cid:129) Formal Methods and Logic
(cid:129) Stochastic Modelling and Analysis
(cid:129) Machine Learning and Artificial Intelligence
To help the reader with a life sciences background, another organisation of the
chapters can be given in terms of application areas touched upon:
Applicationarea Chapter(s)
Artificialpancreas 4
Autophagy/apoptosis 3
Calciumdynamics 13
Cancer 5,7,10,15,17
Cardiaccells 3
Cellcycle 1
Clinicalenvironments&guidelines 8,9
Diabetes(seeArtificialpancreas)
Epidemics 6
Epidermaldifferentiation 2
Geneticcircuits(seeSyntheticbiology)
Ironhomeostasis 6
Metabolicnetworks 7,15
Myeloiddifferentiation 2
Pharmacokinetics 14
Remyelination 11
Signallingpathways 1,3,7,10,13
Stemcells 2
Syntheticbiology 12,16
Thisvolumemakesthemostadvancedcontributionoftheinternationalcommu-
nitytotheresearchissuessurroundingthefascinatingworldofautomatedreasoning
inSystemsBiologyandSystemsMedicine.Wehopethatyouenjoythechaptersas
muchasweenjoyedorganisingtheworkshopandputtingthiscollectiontogether.
Preface vii
We thank the invited speakers to the workshop and the authors who submitted
their work to this volume, and we thank the reviewers for their hard work in
reviewing the submissions and taking part in post-review discussions. Finally, we
would like to express our sincere thanks to Springer for their support during the
production of the volume.
Cambridge, UK Pietro Liò
Newcastle, UK Paolo Zuliani
Contents
Part I Model Checking
1 Model Checking Approach to the Analysis
of Biological Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Nikola Beneš, Luboš Brim, Samuel Pastva and David Šafránek
2 Automated Reasoning for the Synthesis and Analysis
of Biological Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Sara-Jane Dunn and Boyan Yordanov
3 Statistical Model Checking-Based Analysis of Biological
Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Bing Liu, Benjamin M. Gyori and P. S. Thiagarajan
4 Models, Devices, Properties, and Verification of Artificial
Pancreas Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Taisa Kushner, B. Wayne Bequette, Faye Cameron,
Gregory Forlenza, David Maahs and Sriram Sankaranarayanan
5 Using State Space Exploration to Determine How Gene
Regulatory Networks Constrain Mutation Order in Cancer
Evolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Matthew A. Clarke, Steven Woodhouse, Nir Piterman,
Benjamin A. Hall and Jasmin Fisher
Part II Formal Methods and Logic
6 Set-Based Analysis for Biological Modeling . . . . . . . . . . . . . . . . . . 157
Thao Dang, Tommaso Dreossi, Eric Fanchon, Oded Maler,
Carla Piazza and Alexandre Rocca
7 Logic and Linear Programs to Understand Cancer Response. . . . . 191
Misbah Razzaq, Lokmane Chebouba, Pierre Le Jeune,
Hanen Mhamdi, Carito Guziolowski and Jérémie Bourdon
ix
x Contents
8 Logic-Based Formalization of System Requirements
for Integrated Clinical Environments . . . . . . . . . . . . . . . . . . . . . . . 215
Cinzia Bernardeschi, Andrea Domenici and Paolo Masci
9 Balancing Prescriptions with Constraint Solvers. . . . . . . . . . . . . . . 243
Juliana K. F. Bowles and Marco B. Caminati
10 MetastableRegimesandTippingPointsofBiochemicalNetworks
with Potential Applications in Precision Medicine. . . . . . . . . . . . . . 269
Satya Swarup Samal, Jeyashree Krishnan, Ali Hadizadeh Esfahani,
Christoph Lüders, Andreas Weber and Ovidiu Radulescu
Part III Stochastic Modelling and Analysis
11 Stochastic Spatial Modelling of the Remyelination Process
in Multiple Sclerosis Lesions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299
Ludovica Luisa Vissat, Jane Hillston and Anna Williams
12 Approximation Techniques for Stochastic Analysis
of Biological Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
Thakur Neupane, Zhen Zhang, Curtis Madsen, Hao Zheng
and Chris J. Myers
13 A Graphical Approach for Hybrid Modelling of Intracellular
Calcium Dynamics Based on Coloured Hybrid Petri Nets . . . . . . . 349
Amr Ismail, Mostafa Herajy and Monika Heiner
14 Methods for Personalised Delivery Rate Computation for IV
Administered Anesthetic Propofol. . . . . . . . . . . . . . . . . . . . . . . . . . 369
Alena Simalatsar, Monia Guidi, Pierre Roduit and Thierry Buclin
Part IV Machine Learning and Artificial Intelligence
15 Towards the Integration of Metabolic Network Modelling
and Machine Learning for the Routine Analysis
of High-Throughput Patient Data. . . . . . . . . . . . . . . . . . . . . . . . . . 401
Maria Pires Pacheco, Tamara Bintener and Thomas Sauter
16 Opportunities and Challenges in Applying Artificial
Intelligence to Bioengineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425
Fusun Yaman, Aaron Adler and Jacob Beal
17 Deep Learning with Convolutional Neural Networks
for Histopathology Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 453
Dragan Bošnački, Natal van Riel and Mitko Veta
Index .... .... .... .... .... ..... .... .... .... .... .... ..... .... 471