accelerating the drug discovery process with mathematical ...€¦ · this has huge implicatio對ns...
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Accelerating the drug discovery process with mathematical
modelling and MATLAB
David Gavaghan University of Oxford
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Overview • The increasing need for mathematical training in the life
sciences – Predictive and quantitative modelling of biological systems
• What is a Doctoral Training Centre? – The Oxford DTC Programmes – Role of MATLAB within our programmes
• Development of the Chaste software package – Links to industry, particularly Pharma
• Making Chaste available through MATLAB
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Multi-scale, multi-physics
• Biological systems are
– Multiscale: involve interdependent processes occurring on multiple spatial and temporal scales
– Multiphysics: involve multiple physical mechanisms (transport, signalling, reaction….)
– Complex: self-healing, adaptive, plastic, self-organising, reactive
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Courtesy of Peter Kohl (Harefields)
Normal beating Fibrillation
Model complexity is dependent on the scientific questions being asked…….
Pras Pathmanathan (FDA)
• Scientific questions: what is the optimal shock strength to reverse this process? What is the precise effect of a drug?
• Aiming for predictive, quantitative understanding in biology • Requires a new approach to scientific training
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• Quantitative and Systems Pharmacology in the Post-genomic Era: New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms
• An NIH White Paper by the QSP Workshop Group – October, 2011.
• Peter K. Sorger (co-chair), Sandra R.B. Allerheiligen (co-chair), Darrell R. Abernethy, et al
Why is this important to the Pharmaceutical industry?
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• Quantitative and Systems Pharmacology in the Post-genomic Era: New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms
• An NIH White Paper by the QSP Workshop Group – October, 2011.
• Peter K. Sorger (co-chair), Sandra R.B. Allerheiligen (co-chair), Darrell R. Abernethy, et al
Definition: The goal of QSP is to understand, in a precise, predictive manner, how drugs modulate cellular networks in space and time and how they impact human pathophysiology. QSP aims to develop formal mathematical and computational models that incorporate data at several temporal and spatial scales; these models will focus on interactions among multiple elements (biomolecules, cells, tissues etc.) as a means to understand and predict therapeutic and toxic effects of drugs.
Definition: The goal of QSP is to understand, in a precise, predictive manner, how drugs modulate cellular networks in space and time and how they impact human pathophysiology. QSP aims to develop formal mathematical and computational models that incorporate data at several temporal and spatial scales; these models will focus on interactions among multiple elements (biomolecules, cells, tissues etc.) as a means to understand and predict therapeutic and toxic effects of drugs.
The report also makes the overarching recommendation:
Because industry has an acute need for trainees with strong skills in quantitative reasoning, network biology, and animal and human pharmacology, industry should
be engaged in education as well as research.
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EPSRC Life Sciences Interface Doctoral Training Centres
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What is a Doctoral Training Centre? • Introduced by EPSRC’s Life Sciences Interface Programme in 2002
• Address need for scientists capable of quantitative and predictive research in biological and medical sciences
• Typically fund centre costs plus 5 cohorts each of ten students
• Min of 25% taught training, strong emphasis on “transferable skills”
• Oxford LSI DTC was one of the first two (other being in Edinburgh)
• Three years ago rolled out across EPSRC portfolio (£300M) 2002 and 2012
cohorts
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The Oxford DTC Programmes
• Three current Programmes – Life Sciences Interface (2002) – Systems Biology (2007) – Systems Approaches to Biomedical Science (2009, Industrial)
• Industry partners include GSK, AZ, Roche, Novartis, Pfizer
• Total of ~300 students to date, ~120 completed PhDs
• ~25% go into industry, 75% into academic research
Strong focus on mathematical training (regardless of background) largely facilitated by MATLAB
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Use of MATLAB within the Programmes
• Gain understanding and insight in the mathematical courses (basic to advanced)
• Data analysis – basic graphical tool through to advanced statistical, image
processing and data visualisation
• Computational modelling – Prototyping through to research software
• Toolboxes routinely used – Bioinformatics, Image processing, PDE, Statistics, [Parallel]
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Examples of MATLAB in DTC Research
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Chris Arthurs and David Kay Adaptive p-refinement
Tom Doel and Vicente Grau Lobe segmentation in the lung
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The majority of computational modelling research in the DTC is done in MATLAB but…
• Some problems are of a scale and complexity that bespoke scientific software must be developed
– anatomically detailed multiscale, multiphysics problems such as whole-organ modelling
– individual-based models bridging hybrid discrete/continuous, stochastic/deterministic such as cancer modelling
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CHASTE: a MATLAB-inspired software development project
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What is Chaste?
“Cancer Heart and Soft Tissue
Environment”
http://www.cs.ox.ac.uk/chaste/
• Started in 2005 as a 4-week DTC course in Software Engineering
• Library of Open Source (BSD) code for large-scale biological problems
• Aim: produce a robust, extensible, reliable, re-usable and well-documented code base
• Functionality: coupled ODEs, PDEs, agent-based and hybrid on desktop and HPC
• Main applications: Cardiac and Cell-Based
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Chaste development approach • Focus from the start on software engineering issues
– Object-oriented – C++
• Agile approach – Test-driven (test first) – Pair programming – Frequent refactoring – Team ownership
• Code base contains >300,000 lines of code and ~200,000 lines of test
• Growing user base – has been downloaded well over 1000 times from over 400 unique IP addresses including by FDA and NASA
J. Pitt-Francis, et al. Chaste: a test-driven approach to software development for biological modelling. Comp Phys Comm 180:2452-2471, 2009.
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Chaste projects with Industry • EPSRC Integrative Biology e-Science (2004-7)
– IBM
• EU FP7 preDiCT project: prediction of Drug Cardiac Toxicity (2008-11) – Fujitsu Laboratories of Europe (UK), GlaxoSmithKline (UK), Novartis (Switzerland),
F. Hoffman-La Roche, AstraZeneca, Pfizer
• EPSRC 2020 Science Project (2011-2015) – Microsoft Research
• GSK embedding Chaste in the drug development pipeline with the intention of replacing some animal tests
• Collaborations with AZ/Medimmune in cancer modelling
• Collaboration with Mathworks in building a MATLAB front end to Chaste
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Embedding Chaste Functionality into Matlab
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Problem statement • Cell-based version of Chaste requires strong C++ skills
• The process of running simulations very time-consuming
• Not easy to interact with Chaste simulations “on-the-fly”
• Makes Chaste much less attractive to end users, particularly in industry
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• Developed earlier this year via a Mathworks-funded 3-month internship for Tom Dunton (3rd Year DTC student)
• Aim – build a MATLAB front-end to Chaste for a cell-based application problem giving greater control of the simulations
• Provides the user with the modelling capability of Chaste and the visualisation and analytical power of MATLAB
Prototype solution
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The Crypt Renewal Cycle (turnover in 4-6 days)
1. Proliferation of stem cells (bottom of crypt)
2. Transit cells divide 2-3 times (lower third of crypt)
3. Cells migrate to the surface
4. Transit cells differentiate (midcrypt region)
5. Senile cells removed from surface (midpoint between crypts) 1
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2
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Colorectal cancer is the result of multiple genetic mutations which disrupt the normal processes of cell
proliferation, cell differentiation and cell death
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Cell-based modelling of tumour development
• Sub-cellular level: typically cell-cycle models (deterministic ode, stochastic), interacts with tissue level (e.g. via WNT signalling)
• Tissue level: continuous field equations describing e.g. inter-cellular signalling, nutrient uptake (cells act as source/sinks), transport phenomena
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Test problem • Want to look at how the
location and type of mutation (cell properties) affects – the number of cells in the crypt – persistence of mutation
Method: • Initialize a crypt and simulate
until it has equilibrated • Introduce a set of mutations • Simulate the progression of
the mutation
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A MATLAB interface to Chaste
• Use MEX as the way to integrate C++ and MATLAB
• Mirror Chaste classes in object-oriented MATLAB
• Create a functional interface to MATLAB classes – createmesh( myCrypt, ‘mesh’, 10, 6); – solvesystem( myCrypt, 20, 1);
• The evolution of the simulation can be visualised and the MATLAB graphical user interface enables interaction during the simulation
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Structure of MATLAB-Chaste interface
GUI
Object-oriented MATLAB
MEX-functions
Chaste libraries C++ repository of Chaste functionality
MATLAB’s bridge to C/C++
Take advantage of MATLAB’s GUI support. OO MATLAB allows natural development of sophisticated GUIs
Modularity of the MATLAB code improves scalability and maintainability for larger software projects
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Basic test case with no user interaction
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User defined mutation of cells
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User defined mutation of cells
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Current Chaste Functionality (James Osborne and Sara-Jane Dunn)
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Self-organisation of the colorectal crypt
Tumour spheroid with a necrotic core Over-proliferation leading
to polyp formation
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Summary and Future Work • Research and training at the LSI is an exciting and
rewarding area
• Project with Mathworks ongoing – looking at extending to encompass further functionality
• Work with Pharma on cardiotoxicity being extended (funding from NC3Rs and EPSRC)
• Cancer modelling work ongoing with AZ, MedImmune and Roche.
• DTC training at PhD level extended to post-doc level through 2020 Science programme
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Acknowledgements
• Tom Dunton, James Osborne
• Chaste cardiac team: Joe Pitt-Francis, Blanca Rodriguez, Pras Pathmanathan, Jon Cooper, Miguel Bernabeu, Gary Mirams, Alberto Corrias, Raf Bordas, Alan Garny, Nejib Zemzemi, Alfonso Bueno-Orovio
• Chaste cell-based team: Helen Byrne, Jon Whiteley, James Osborne, Alex Fletcher, Sara-Jane Dunn, Sophie Kershaw, Alex Walters, Philip Murray
• Numerics: David Kay, Jon Whiteley
• Funding: EPSRC, BBSRC, MRC, EU FP7, Wellcome Trust
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Appendix of slides
• Some more slides from Tom’s presentation below
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What do we want to control in Chaste?
• Simulation – cell centre or vertex, length of simulation
• Crypt parameters – width, height
• Cell parameters – cell-cycle model,
• Mutations – mutation type, mutation parameters
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MEX-file interface
• Two MEX-functions o Initialize.cpp → set up the simulation o Simulate.cpp → advance the simulation
• Use Chaste’s serialization to save the
simulation in its current state, then re-load it to continue the simulation
• Two time scales to the interface o Small time step used within Chaste to solve the
system
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MEX-file interface
• Many options in Chaste are templated, so are set at compile time
• To solve this problem we have to use a switch in the MEX-file
• Parameters of interest are passed back to MATLAB o cells → structure array with information for
each cell d matri ith location of the nodes in the
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MATLAB crypt class
• Simulations are set up using a MATLAB crypt class
• crypt’s methods control all aspects of the simulation: verifying input parameters, calling the MEX-functions and controlling GUI
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Designing the crypt class
• The methods implemented in the crypt class mirror the structure of simulations in Chaste
• Set and get access methods are employed to ensure parameters are correct
• Needs to exercise some control over the directories that Chaste writes to (avoid overwriting etc.)
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A few simple GUI elements
• Gives on-the-fly visualizations of the crypt state
• Can be easily extended to display various crypt parameters during the simulation
• As a demonstration of how to collect user input, mutations and labels can be applied with the mouse