1 / 33

Systems Biology

2. Learning objectives At the end of this lecture students will understand:. Why networks are a good formalization for systems biology researchWhen they are not usefulSome widely-used network modelsThe Random Boolean Network algorithmThe Artificial Genome algorithmWhat properties of biological

morey
Télécharger la présentation

Systems Biology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. Systems Biology COMP4001/ COMP7001 19 September 2005

    2. 2 Learning objectives At the end of this lecture students will understand: Why networks are a good formalization for systems biology research When they are not useful Some widely-used network models The Random Boolean Network algorithm The Artificial Genome algorithm What properties of biological systems can be investigated using networks? Asynchronous node updating Constitutive gene activity Feedback loops, delay and the structure of switches Effects of eRNA control

    3. 3 Learning objectives At the end of this lecture students will understand: Why networks are a good formalization for systems biology research When they are not useful Some widely-used network models The Random Boolean Network algorithm The Artificial Genome algorithm What properties of biological systems can be investigated using networks? Asynchronous node updating Constitutive gene activity Feedback loops, delay and the structure of switches Effects of eRNA control

    4. 4 Biological network models Networks are a useful approach to biological systems at multiple levels Subcellular Tissues Organs Organ Systems Organism Population Ecosystem Even at a single level, you can model multiple different things

    5. 5 Genetic regulatory networks Genes produce proteins and RNA which control the activity of other genes Activation Inhibition Biologists tend to think in terms of pathways Crosstalk == networks!

    6. 6 Learning objectives At the end of this lecture students will understand: Why networks are a good formalization for systems biology research When they are not useful Some widely-used network models The Random Boolean Network algorithm The Artificial Genome algorithm What properties of biological systems can be investigated using networks? Asynchronous node updating Constitutive gene activity Feedback loops, delay and the structure of switches Effects of eRNA control

    7. 7 Pitfalls of network modelling Trivial pursuits Many simulations are computer games rather than scientific research Do you have a real research question? Appropriate level of abstraction may not be clear Another formalism may be more appropriate Agent-based model, Cellular automaton Be careful not to write the desired behaviours into the simulation

    8. 8 Learning objectives At the end of this lecture students will understand: Why networks are a good formalization for systems biology research When they are not useful Some widely-used network models The Random Boolean Network algorithm The Artificial Genome algorithm What properties of biological systems can be investigated using networks? Asynchronous node updating Constitutive gene activity Feedback loops, delay and the structure of switches Effects of eRNA control

    9. 9 Random Boolean networks (RBNs) Classic algorithm n nodes with k incoming links per node Each node can be either on (1) or off (0) at any given time Updating is synchronous Update rule is a random Boolean function of inputs, assigned when the network is created, different for each node For each node there will be 22k possible functions Each node has n!/(n-k)! Possible ordered combinations for k links The number of possible networks for a given set of parameters is thus

    10. 10 RBN dynamics Low connectivity Freeze to a single state (point attractor) Moderate connectivity (~2) Limit cycle behaviour High connectivity Chaotic dynamics Edge of chaos at the transition between ordered and chaotic dynamics It has been suggested that complex systems evolve to the edge of chaos because this combines optimal robustness and flexibility

    11. 11 RBN dynamics

    12. 12 State Spaces

    13. 13 The artificial genome

    14. 14 Advantages of AG models More biologically plausible Genome / phenotype representation Development could be modelled Network characteristics can be selected via appropriate parameterization Genome is a convenient representation for EC modelling Genetic operators applied at the genome level act differently from those applied at the network level

    15. 15 Learning objectives At the end of this lecture students will understand: Why networks are a good formalization for systems biology research When they are not useful Some widely-used network models The Random Boolean Network algorithm The Artificial Genome algorithm What properties of biological systems can be investigated using networks? Asynchronous node updating Constitutive gene activity Feedback loops, delay and the structure of switches Effects of eRNA control

    16. 16 Background Network topology affects dynamics Hypothesis: Small network motifs act additively to produce observed dynamics Feedback loops are particularly important in cyclic dynamics Testbed: interesting dynamics in asynchronously updated networks

    17. 17 Evolutionary algorithm f = n(l/2) each run 100 times with different random number seed n = number of times a previous state was revisited l = number of states before revisiting

    18. 18 Limit cycles

    19. 19 Statistics

    20. 20 Triads

    21. 21 Loops

    22. 22 Conclusions Relationship between topology and dynamics is not straightforward Evolution can reliably produce interesting dynamic behaviour under asynchronous updating But resulting network topology is extremely variable Most networks increase numbers of loops, but... Different initial networks may find different solutions to the evolutionary task Network dynamics may not be readily amenable to reductionist analysis

    23. 23 Hypotheses Dogma Gene ? Protein ? Structure and/or Regulation Noncoding RNA Extra layer of control High connectivity Fast action Mostly inhibitory How will this affect network dynamics?

    24. 24 Simulations

    25. 25 Length of attractor

    26. 26 Length of attractor

    27. 27 Length of attractor

    28. 28 Conclusions Major change is between no RNA and RNA As proportion of RNA inhibition increases, length of attractors increases As proportion of RNA increases, interaction between i and c becomes more complex Speed of RNA step makes little difference (?) Presence of constitutively active genes constrains starting states and states which can be visited Interaction between RNA control and constitutive activity? Parameterization?

    29. 29 Modelling the spread of antibiotic resistance Population of constant size Three types of individuals Uncolonized Colonized with antibiotic sensitive bacteria Colonized with antibiotic resistant bacteria

    30. 30 Results no selection

    31. 31 Results - selection

    32. 32 Results - treatment

    33. 33 Results population size

    34. 34 Learning objectives At the end of this lecture students will understand: Why networks are a good formalization for systems biology research When they are not useful Some widely-used network models The Random Boolean Network algorithm The Artificial Genome algorithm What properties of biological systems can be investigated using networks? Asynchronous node updating Constitutive gene activity Feedback loops, delay and the structure of switches Effects of eRNA control

More Related