Agent-based methods for translational cancer multilevel modelling Sylvia Nagl PhD Cancer Systems Science & Biomedical Informatics UCL Cancer Institute London
Jan 03, 2016
Agent-based methods for translational cancer multilevel modelling
Sylvia Nagl PhD
Cancer Systems Science & Biomedical Informatics
UCL Cancer Institute
London
Main points of the talk
• Potential of agent-based modelling
• Systems biology perspective on large cell network simulation
• A new synergy between modelling and wet biology
Hanahan and Weinberg (2000) Cell 100:57-70
The hallmarks of cancer
Systems biology and medicine
• Diseases are abnormal perturbations of biological networks - through defects in molecular mechanisms or environmental stimuli
• Therapies are the interventions needed to restore networks to their normal states
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Butcher et al. (2004) Nature Biotechnology 22:1253
Modelling challenge: genome to phenotype
extended genotype
elementary phenotype
Systems biology and medicine
• Fundamental question of where function lies within a cell – distributed (networks of interacting molecules)– hierarchical
• network motifs and modules • complex network connecting modules
• A globalist view of the dynamics of (large) cell networks is therefore needed
cell and tissue levels
cell networks
molecular interactions (molecular dynamics)
E-science}
Systems biology and cancer
• Given the many components of functional modules, there are different paths to disease-inducing systems failure
• A multitude of ways to ‘solve’ the problems of achieving a survival advantage in cancer cells
• Each patient’s cancer cells evolve through an independent set of genomic lesions and selective environments - a fundamental reason for differences in survival and treatment response
Likelihood of cancer cell death in response to DNA damaging drugs
and radiotherapy
DNA damage response network
Supporting treatment optimisation
in the individual patient
Agent-based modelling
Agent based model
Simulation
A1 A2
A1Ai
A2
One-to-one mapping of cell components to computational agents
Agents at multiple levels:Protein, network motif, module (organelle, cell …)
Interaction rules
Translates wealth of molecular knowledge into component-based models
Patient-specific molecular data
?
TF1
S1 S2 SN
TF2 TFm
Signal-genetic network Environment
Transcription factors
Genes
DNA damage
Changes in genome activation
TF1
S1 S2 SN
TF2 TFm
Signal-genetic network Environment
Transcription factors
Genes
Agent-based modelling:
‘Agent’ (protein, motif, module) => behaviour rules
Kinetics/step function/Boolean variables
scale up to large networks
Challenge: Emergence
• Coherent behaviour of cells emerges from interactions between a large number of system components – proliferation, cell death, resistance to drugs
• ‘Computational’ definition of emergence: Unspecified properties and behaviours arise from interaction between agents rather than as a consequence of a single agent’s actions
• Methods for analysis needed e.g. for therapy target discovery
Detecting event patterns in time
• A simple event is a state transition due to a rule execution• A complex event is made up of a set of interrelated simple events
• Classification of complex events in a simulation allows one to discover associations between processes at different levels
• Published formalism available at www.cs.ucl.ac.uk/staff/C.Chen/research.html
Linking network simulations to integrated cell behaviour requires knowledge external to the simulation, the question of ‘biological meaning’
Challenge: ‘the gap’
A new synergy
• Data generation is still largely motivated by a non-systems-based research paradigm
• Systems biologists then seek to use these data to build and validate models of systems – with difficulties
• We need to rethink the relationship between experiment and modelling – both need to proceed within a complex systems framework – new kinds of experiments needed to investigate multi-level
relationships in the wet system• e. g., global signal network states need to be matched to cell-
level phenotypic measurements over time and under a range of conditions
• E-science systems modelling and experiment need to complement and synergise
Acknowledgements
• Nuno Rocha Nene (CoMPLEX PhD programme)• Chih-Chun Chen (interdisciplinary EPSRC DTA awards)• CR UK, Department of Health
• Published formalism available at www.cs.ucl.ac.uk/staff/C.Chen/research.html
• Decision support tool for ABM techniques www.abmsystemsbiology.info
• My email: [email protected]