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Boolean Networks and Experiment Design B-Cell Single Ligand Screen

Boolean Networks and Experiment Design B-Cell Single Ligand Screen. Stuart Johnson Bioinformatics and Data Analysis Lab UCSD. Outline. Why Boolean networks? Building/Displaying Boolean Networks Experiment design Procedure Some competing (sub)networks from the B-Cell data Conclusions.

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Boolean Networks and Experiment Design B-Cell Single Ligand Screen

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  1. Boolean Networks and Experiment DesignB-Cell Single Ligand Screen Stuart Johnson Bioinformatics and Data Analysis Lab UCSD

  2. Outline • Why Boolean networks? • Building/Displaying Boolean Networks • Experiment design • Procedure • Some competing (sub)networks from the B-Cell data • Conclusions

  3. Why try Boolean Networks? • Model • Biochemical system • lots of complexity • predictive • lots of meaning very difficultinverse problem doable forward problem • Data • noisy • partial sampling

  4. Why try Boolean Networks? • Boolean networks • some complexity • predictive (exp. design) • data-like • meaning? consistency = causality; should tell us about connectivity easy Boolean data easy

  5. 2nd msg / co-sampled Ca Boolean data TIME P-P, 2nd Msg red=1 at 99% confidence: P(d=NC)<.01 blue=0 everythingelse Experimental conditions

  6. Phosphoproteins Boolean data TIME P-P, 2nd Msg red=1 at 99% confidence: P(d=NC)<.01 blue=0 everythingelse Experimental conditions

  7. late resp. Ca -> PP Ca,cAMP -> No PP early resp. groups ofsiml. resp. Boolean data TIME P-P, 2nd Msg Experimental conditions

  8. Boolean data TIME P-P, 2nd Msg Node = Full column of data; all exp. cond. Experimental conditions

  9. Gq Known ligand/ receptor interactions from AfCS ligand descriptions Inputs, etc. Experimental Conditions Single ligand screen inputs

  10. consistent? ? Extractingpatterns AIG Ca (.5 min) ELC LPA Experimental Conditions

  11. LPA0 Ca0.5 ER12.5 ERK1 (2.5 min) 0 0 0 1 0 ? 0 1 1 1 1 0 Time Experimental Conditions Graph TruthTable displayingand encodingpatterns

  12. all hypotheses:ER1(2.5 min) H1 H2 H1,H2 & H3: Early calcium is associated with ER1 H1: LPA is special (causes an early Ca signal but no ER1) H2: M3A is special (0.5 min Ca, no 1 min Ca, but ER1) H3: no special ligands, ER1 consistent with Ca & cAMP H3

  13. Constructing complete networks Input nodes I1 I2 I3 nodes with truth tables N1 N2 N3 5 x 7 x 3 = 105networksmaximum

  14. Constructing complete networks I1 I2 I3 N1 N2 N3

  15. Constructing complete networks I1 I2 I3 N1 N2 N3

  16. Constructing complete networks I1 I2 I3 N1 N2 N3 • “Feedback” not allowed! a completely determined network can have multiple output states; forward and inverse problems no longer “easy”

  17. 1 output state Experiment Design: networks reproduceresults of completed experiments • All networks: 1 possible output state: • For known inputs, every network simply reproduces results of completed experiments • (Information) Entropy = score = 0

  18. 3 output states Experiment Design: networks are predictive • All networks: multiple possible output states: • these multiple states correspond to unknown entries (?) in truth tables and the different connectivity of the networks • Entropy = score > 0

  19. Dual-ligand experiment design ligand 2 ligand 1 entropy score

  20. Dual-ligand experiment design ligand 2 ligand 1 entropy score ELC + LPA

  21. Procedure Do Experiments DisplayBooleanNetworks BuildBoolean Networks Score classof experiments pick highest scoring exp.

  22. LIG LIG LIG LIG 2M RCP 2M RCP 2M 2M PP PP 1 PP PP 1 PP PP Controlling Complexity: Constraint Graphs • Graphs specify allowable inputs and hops

  23. LIG LIG LIG LIG 2M RCP 2M RCP 2M 2M PP PP 1 PP PP 1 PP PP Controlling Complexity: Constraint Graphs • Graphs specify allowable inputs and hops

  24. LIG RCP 2M PP 1 PP Network display All node rules • Can filter/cluster/display these rules to see: • ligand classification (chemokines, cytokines, etc) • clusters of similar control patterns • etc. - “pathways”

  25. LIG 2M PP Early Calciumvs ... Early Calcium+ cAMP

  26. LIG 2M PP 1 PP ER1 -> ER2,P90 P90 -> AKT ST6 -> ST3

  27. LIG RCP 2M PP Early Ca & Gqcontrol vs ... Early Ca& G12

  28. Conclusions • This is a general method/implementation and will extend to the RAW screens and FXM in some form • Boolean network analysis has many interesting features: • learns from experiments/proposes new exp. • formalizes inclusion of known information as either constraint graphs or hidden nodes • caveat 1: the BN have encoded any real meaning • caveat 2: you can control complexity and digest the networks inferred • http://dev.afcs.org:12057/ for the latest results, navigable/clickable networks and more background

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