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Towards More Realistic Affinity Maturation Modeling

Towards More Realistic Affinity Maturation Modeling. Erich R. Schmidt, Steven H. Kleinstein Department of Computer Science, Princeton University July 19, 2001. Recent germinal center models: simple responses (haptens – Ox, NP) single affinity-increasing mutation simple B cell model

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Towards More Realistic Affinity Maturation Modeling

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  1. Towards More Realistic Affinity Maturation Modeling Erich R. Schmidt, Steven H. Kleinstein Department of Computer Science, Princeton University July 19, 2001

  2. Recent germinal center models: simple responses (haptens – Ox, NP) single affinity-increasing mutation simple B cell model no inter-cellular signals no internal dynamics Address limitations: more complex receptor affinity space multiple affinity-increasing mutations more realistic model of B cell inter-cellular signals signal memory Germinal center models

  3. affinitylandscape internaldynamics populationdynamics Specific: Ox, NP Discrete/stochastic simulation More complex, realistic Simulation B cell receptor affinity B cell Germinal center

  4. K=0 K=medium K=high Ox,NP NK : easy to model different antigen, check stats vs. experimental data Affinity landscapes:NK landscape model • N: sequence length  receptor space size • K: internal interactions  landscape ruggedness

  5. NK parameter values • proposed by Kauffman/Weinberger: • correctly predicts: • number of steps to local optima • fraction of higher-affinity neighbors • “conserved” sites in local optima

  6. Individual mutations vs. population dynamics • Kauffman/Weinberger: • single cell walk • mutations: uphill • no time • no other events • Our simulation: • entire population dynamics • mutations: random • time-dependent • division, death

  7. Specific: phOx, NP Discrete/stochastic simulation Simulation B cell receptor affinity B cell Germinal center More complex, realistic

  8. functionalnodes output nodes(rates) fitnessfunction(division) mutation death division B cell model – decision making network input node(receptoraffinity)

  9. Germinal center model • single seed • all cells share same parameters • dynamic, stochastic, discrete • simulate for 14 days • different steps: change network parameters • search: best network for affinity maturation

  10. NK Ox,NP Expectations • Previous work: Ox, NP • single affinity-increasing mutation • fitness function = threshold • NK landscape • rugged, multiple peaks • expected smaller slope

  11. Results • threshold • select for small percentage of affinity-increasing mutations • high-affinity seed

  12. Results • low affinity seed • smaller slope • very hard to walk up: smaller slope doesn’t help overall affinity maturation

  13. Conclusions • dynamic model on NK landscape • generates affinity maturation • not reaching local optima • best division rate is a threshold function • affinity of seeding cell important factor • total mutation count consistent with bio data • Kauffman: all mutations up • our simulation: random mutations (up+down)

  14. B cell receptor affinity B cell Germinal center Morerealistic Specific: phOx, NP Discrete/stochastic simulation More complex, realistic Future work • more complex decision network • optimization problem: mutate network, not only parameters

  15. Acknowledgements • Steven Kleinstein, Jaswinder Pal Singh • Martin Weigert • Stuart A. Kauffman, Edward D. Weinberger, Bennett Levitan (Santa Fe)

  16. The End

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