agent-based methods for translational cancer multilevel modelling sylvia nagl phd cancer systems...

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  • Agent-based methods for translational cancer multilevel modellingSylvia Nagl PhDCancer Systems Science & Biomedical InformaticsUCL Cancer InstituteLondon

  • 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

  • The hallmarks of cancerHanahan and Weinberg (2000) Cell 100:57-70

  • 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

  • Modelling challenge: genome to phenotype

    Butcher et al. (2004) Nature Biotechnology 22:1253

    extended genotype elementary phenotype

  • Systems biology and medicineFundamental question of where function lies within a cell distributed (networks of interacting molecules)hierarchicalnetwork motifs and modules complex network connecting modules

    A globalist view of the dynamics of (large) cell networks is therefore needed E-science

    }

  • Systems biology and cancerGiven 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 patients 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 radiotherapyDNA damage response networkSupporting treatment optimisation in the individual patient

  • Agent-based modellingOne-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?

  • DNA damageChanges in genome activation

  • Agent-based modelling:Agent (protein, motif, module) => behaviour rulesKinetics/step function/Boolean variables scale up to large networks

  • Challenge: EmergenceCoherent 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 agents actions

    Methods for analysis needed e.g. for therapy target discovery

  • Detecting event patterns in timeA simple event is a state transition due to a rule executionA 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

  • Challenge: the gapLinking network simulations to integrated cell behaviour requires knowledge external to the simulation, the question of biological meaning

  • A new synergy

    Data generation is still largely motivated by a non-systems-based research paradigmSystems biologists then seek to use these data to build and validate models of systems with difficultiesWe 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 systeme. 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

  • AcknowledgementsNuno 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: s.nagl@ucl.ac.uk

    Cancer dells maintain the ability to survive and proliferate against a wide range of anticancer therapies.

    Their robustness is seen as an emergent property arising through the interplay of genomicinstability and selective pressure.

    Mapping between environment signals that bind to specific receptors, transcription factors inside the cell, and the genes they regulate. Notice the composite motifs with different time scales. Some proteins encoded the genes are themselves transcription factors that can activate or inhibit the expression of other genes.Mapping between environment signals that bind to specific receptors, transcription factors inside the cell, and the genes they regulate. Notice the composite motifs with different time scales. Some proteins encoded the genes are themselves transcription factors that can activate or inhibit the expression of other genes.The complex event formalism can be used to describe events at any level of abstraction that can be distinguished in a system. If we take a simple event to be a state transition due to a rule execution, we can say that a complex event is an event made up of a set of interrelated simple events, where the relative `locations of each of the simple events satisfy certain constraints with respect to one another e.g. temporal, spatial relationships. Complex event types can be used to define sets of behaviours which satisfy particular constraints. By being able to identify and classify complex events in a simulation, we can discover associations between behaviours at different levels.

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