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Development, complexity and biased evolution

Development, complexity and biased evolution. Nic Geard SENSe, University of Southampton (formerly at: ARC Centre for Complex Systems, The University of Queensland, Australia). Background. Recently arrived in the UK to work with Seth Bullock at University of Southampton

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Development, complexity and biased evolution

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  1. Development, complexity and biased evolution Nic Geard SENSe, University of Southampton (formerly at: ARC Centre for Complex Systems, The University of Queensland, Australia)

  2. Background • Recently arrived in the UK to work with Seth Bullock at University of Southampton • Prior to that, I obtained my PhD from the University of Queensland in Australia, advised by Professor Janet Wiles. • Thesis – Artificial Ontogenies: A Computational Model of the Control and Evolution of Development

  3. II. Gene Expression III. Phenotype I. Genome ..GTCATACTATAATCCTGGTCATCATGTCTGCTCTACATCGTGTCTACTCTGTTATACTTTACTGTCTTACTCTCACATATATCTCGTCACTGCATGCCATGTTACATCGTGTCTACTCTGTTATACTTTACTACATATATCTCGTCACTGCCTGT... Anterior Posterior zygote 1st cleavage: P 1 AB 2nd cleavage: neurons EMS P epidermis 2 pharynx 3rd cleavage: MS E C P 3 4th cleavage: pharynx intestine epidermis muscle D P 4 muscle germ line Sequence Gene expressionNetwork Interactions GrammarLineage trees Mapping biology onto computation Evolution: variation and selection gene expression, development, environment, etc. Organism DNA Sequence

  4. What role does development play in evolution? • Developmental reprogramming: mutational change affecting the developmental trajectory of an organism (Arthur 2002) • If reprogramming is more likely to produce some trajectories than others, then evolution may be biased towards those trajectories. • What is a good computational model for studying developmental reprogramming? W. Arthur. The emerging conceptual framework of evolutionary developmental biology, Nature, 2002.

  5. How to model developmental space? Cell lineages: • Early development of some species is characterized by invariant patterns of cell division and differentiation (e.g. C. elegans) • Cell lineages provide a clear record of a developmental trajectory • An organizational, rather than a morphological, representation of development

  6. gene regulatory network cell dynamics cell lineage Modelling the control of development • Recurrent neural network model of gene regulation • Inputs = environmental context • Outputs = division and differentiation triggers • Each cell contains the same network, but with a different state • ‘Phenotype’ = terminal cells of lineage

  7. Measuring developmental complexity R. Azevedo et al. The simplicity of metazoan cell lineages, Nature, 2005.

  8. How does developmental complexity vary? • The space of possible developmental trajectories is vast • By parameterizing the model system, we can visualise ‘slices’ through this space • By making the visualisation interactive, we can efficiently identify major characteristics • LinMap – an interactive visualisation tool N. Geard & J. Wiles. LinMap: Visualising complexity gradients in evolutionary space, Artificial Life, submitted.

  9. Complexity Gradients λ W

  10. Complex behaviour as a transitional phenomenon

  11. Different complexity measures Number of differentiated cells Number of terminal cells Weightedcomplexity Non-deterministic complexity N=8, k=8

  12. Are all phenotypes equally available for natural selection? Traditional view Developmental bias W. Arthur. Biased Embryos and Evolution, 2004.

  13. 8 red, 16 yellow Distribution of lineages with two cell fates: A (red) vs B (yellow) # B cells 4 red, 4 yellow #A cells

  14. The gene network generates a very different distribution of frequent phenotypes compared to a stochastic (Markovian) model # B cells #A cells

  15. Features of distribution vary with size and connectivity

  16. What are the implications for evolution? • Adaptive task : Match a cell fate distribution derived from a biological cell lineage: e.g. • C. elegans (male) V6Lpap – • red = hypodermis; green = neuron; • blue = apoptosis; yellow = structural

  17. Dynamic lineages are significantly less complex than stochastic lineages real lineage

  18. Summary • A tractable model of development. • Methods for measuring and visualising the structure of developmental space. • The intrinsic dynamics of the GRN model result in some lineages/phenotypes being generated more frequently than others. • This biased production of variation is reflected in the direction of adaptation. • A possible explanation for the complexity observed in real cell lineages … (?)

  19. Acknowledgements • Janet Wiles, Kai Willadsen and James Watson (UQ, Brisbane) • Ricardo Azevedo and Rolf Lohaus (UT, Houston) Further Information • LinMap software (Java) and publications available from : http://www.itee.uq.edu.au/~nic • Geard, N., (2006). PhD Thesis. • Geard, N. & Wiles, J., (2006). Investigating ontogenetic space with developmental cell lineages, Artificial Life X. • Geard, N. & Wiles, J., (2005). A Gene Network Model for Developing Cell Lineages. Artificial Life11(3):249-268.

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