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Planning for Gene Regulatory Network Intervention

Planning for Gene Regulatory Network Intervention. Daniel Bryce Arizona State University Seungchan Kim Arizona State University & Translational Genomics Research Institute. Prior Work. Planning for Finding Pathways

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Planning for Gene Regulatory Network Intervention

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  1. Planning for Gene Regulatory Network Intervention Daniel Bryce Arizona State University Seungchan Kim Arizona State University & Translational Genomics Research Institute

  2. Prior Work • Planning for Finding Pathways • S. Khan, K. Decker, W. Gillis, and C. Schmidt. “A multi-agent system-driven AI planning approach to biological pathway discovery.” In Proceedings of ICAPS’03, 2003. • Fifth International Planning Competition, 2006. • Reasoning about change in cellular processes • N. Tran and C. Baral. “Issues in reasoning about interaction networks in cells: necessity of event ordering knowledge.” In Proceedings of AAAI’05, 2005. • Extracting and Expressing Transition Functions from Micro-array experiments, Markov chain analysis. • S. Kim, H. Li, E. Dougherty, N. Cao, Y. Chen, M. Bittner, and E. Suh. “Can Markov chain models mimic biological regulation?” Journal of Biological Systems, 10(4):337–357, 2002. • I. Shmulevich, E. Dougherty, S. Kim, and W. Zhang.”Probabilistic boolean networks: a rule-based uncertainty model for gene regulatory networks.” Bioinformatics 18(2):261–274, 2002. • Non-AI work on planning interventions. • A. Datta, A. Choudhary, M. Bittner, and E. Dougherty. “External control in Markovian genetic regulatory networks: the imperfect information case.” Bioinformatics, 20(6):924–930, 2004. Bryce & Kim -- IJCAI-07

  3. Questions of interest: How does cancer occur? How can we prevent cancer? How do we kill specific cells? Can we control Differentiation? e.g., Program stem cell to become Liver Cell Can we change Phenotype? e.g., Revert liver cell to back to stem cell, then differentiate to heart cell Gene Correlations g2 g1 g4 g3 Dynamics Model g4 g3 g2 g1 g4’ g3’ g2’ g1’ Gene Regulatory Networks (GRNs) Tissue Cell Type (Phenotype, e.g., liver cell) Micro array Data From: [Wuensche, PSB-98] Bryce & Kim -- IJCAI-07

  4. Gene Regulatory Network Behavior Extra cellular signals can effect the cell state transitions (e.g., Chemotherapy, Pharmaceuticals, and Stress) Edge Thickness == Pr(s | s’) Cancer Phenotype Partial Observations of molecular components or physiology are available Steady States (normal) Transient States (intermediate) Undesirable State Bryce & Kim -- IJCAI-07

  5. Datta et. al. Assumptions Synchronous Events Exact Representation Optimal Bounded Length Plans Datta et. al. Approach Enumerate Reachable Belief States Dynamic Programming Our Approach AI Planning AO* Search GRN Intervention Planning Non-Intervention Observation Intervention Observation Bryce & Kim -- IJCAI-07

  6. WNT5A GRN Highly active WNT5A indicates proliferation of cancer 2 (non)interventions 2 variations: direct and indirect control 2 observations 7 genes (binary valued) Randomly Generated GRN 4 (non)interventions 2 observations 7 genes (binary valued) Compare AI Planning with Datta et. al. Scaling horizon Sensitivity to Reward Function Metric: Total Time Evaluation Bryce & Kim -- IJCAI-07

  7. WNT5A GRN (from TGEN dataset) • Indirect Control • Intevene Pirin gene • Observe WNT5A gene • Direct Control • Intervene WNT5A gene • Observe Pirin gene Bryce & Kim -- IJCAI-07

  8. Random GRN (4 acts) Goal Reward (AO*) Enumeration AO* exploits Reward Function for Pruning (Improved Scalability In Some Cases) Bryce & Kim -- IJCAI-07

  9. Assumptions Revisited • Finite Horizon • Not all treatments require same length • Synchronous Change • Actions overloaded to include GRN change • 7 Genes and 1 intervention • Within human comprehension Bryce & Kim -- IJCAI-07

  10. Indefinite or Finite Horizon? • Indefinite Horizon: If goal state is a steady state, then no need to plan more actions to meet a given horizon Bryce & Kim -- IJCAI-07

  11. Asynchronous Change • Decouple Intervention from Gene Regulatory Network Simulation • Triggers (Tran and Baral, AAAI’05) • Probabilistic Exogenous Events (Blythe, UAI’94) Bryce & Kim -- IJCAI-07

  12. Larger GRNs • 50-5000 genes • More Interventions and Observations • Representation: • ADD for transition relation blows up • DBN is better, but exact inference can be costly • Extensions of Thrun’s MC-POMDP’s, sample based representation, is in the right direction • Search Heuristics: • McLUG: Planning Graphs with Probabilistic Actions Bryce & Kim -- IJCAI-07

  13. Conclusion • Off-the-shelf AI planning improves upon state of the art in Intervention Problems • Future Research Needed: • Scaling • Indefinite Horizon • Extra Actions and Observations • Sample-based Representation • Search Heuristics • Modeling • Asynchronous Probabilistic Change • Plan Explanation Bryce & Kim -- IJCAI-07

  14. Extra Slides Bryce & Kim -- IJCAI-07

  15. Empirical Comparison AO* Datta Enumeration With no heuristics Search performance Correlates with Reward Function Total Time and Expanded Nodes Better in all Cases Bryce & Kim -- IJCAI-07

  16. The Network Bryce & Kim -- IJCAI-07

  17. The Parameters and Functions Bryce & Kim -- IJCAI-07

  18. Computational Biology • Bioinformatics • Knowledge Discovery & Data-mining • Manage and Analyze Biological Data • Systems Biology • Simulation • Model Dynamic Systems Bryce & Kim -- IJCAI-07

  19. Representing State Distributions Algebraic Decision Diagram Explicit Vector g1 g2 g2 .2 .25 .35 Bryce & Kim -- IJCAI-07

  20. Representing State Distributions Algebraic Decision Diagram Explicit Vector g1 g2 .2 .25 .35 Bryce & Kim -- IJCAI-07

  21. Representing Probabilistic Actions Explicit Transition Matrix Algebraic Decision Diagram g1 g’1 g’1 g2 g2 g2 g2 g’2 g’2 g’2 g’2 g’2 g’2 g’2 g’2 0 .1 .2 .3 .5 .6 .8 1 Bryce & Kim -- IJCAI-07

  22. Representing Probabilistic Actions Explicit Transition Matrix Algebraic Decision Diagram g1 g’1 g’1 g2 g2 g2 g2 g’2 g’2 g’2 g’2 g’2 g’2 g’2 0 .1 .2 .3 .5 .6 .8 1 Bryce & Kim -- IJCAI-07

  23. Modeling Network Dynamics (- (influence2 ?g3 ?g4 ?g) (noise)) (noise) (noise) (- (influence1 ?g1 ?g2 ?g) (noise)) (predicts ?g3 ?g4 ?g) (predicts ?g1 ?g2 ?g) ?g1 ?g2 ?g3 ?g4 0 1 ?g Bryce & Kim -- IJCAI-07

  24. Network Dynamics Encoding <dynamics> Bind all genes to variables (forall (?g ?g1 ?g2 ?g3 ?g4 - gene) ;;constraint for grounding that binds only those genes ?g1 - ?g4 that ;;predict ?g. External control actions add predicates to the ;;antecedent below so that ?g does not bind to controlled genes. (when (and (predicts1 ?g1 ?g2 ?g) (predicts2 ?g3 ?g4 ?g)) (probabilistic (- (influence1 ?g1 ?g2 ?g) (noise)) ;;predictor 1 probability (and (when (or ;;conditions to set ?g up (and (not (up-regulated ?g1)) (not (up-regulated ?g2)) (pred-fn ?g1 ?g2 ?g zz)) (and (not (up-regulated ?g1)) (up-regulated ?g2) (pred-fn ?g1 ?g2 ?g zo)) (and (up-regulated ?g1) (not (up-regulated ?g2)) (pred-fn ?g1 ?g2 ?g oz)) (and (up-regulated ?g1) (up-regulated ?g2) (pred-fn ?g1 ?g2 ?g oo)) ) (up-regulated ?g)) ;;set ?g up (when (or ;;conditions to set ?g down (and (not (up-regulated ?g1)) (not (up-regulated ?g2)) (not (pred-fn ?g1 ?g2 ?g zz))) (and (not (up-regulated ?g1)) (up-regulated ?g2) (not (pred-fn ?g1 ?g2 ?g zo))) (and (up-regulated ?g1) (not (up-regulated ?g2)) (not (pred-fn ?g1 ?g2 ?g oz))) (and (up-regulated ?g1) (up-regulated ?g2) (not (pred-fn ?g1 ?g2 ?g oo))) ) (not (up-regulated ?g))) ;;set ?g down ) (- (influence2 ?g3 ?g4 ?g) (noise)) ;;predictor 2 probability (and [...]) ;;predictor 2, similar to predictor 1 (noise) (up-regulated ?g) ;;noise to set ?g up (noise) (not (up-regulated ?g)) ;;noise to set ?g down ) ) ) Binding constraints probability Of using predictor1 conditions to up-regulate with predictor1 up-regulate with predictor1 Conditions to down-regulate down-regulate with predictor1 Rules for predictor2 and noise Bryce & Kim -- IJCAI-07

  25. Network Parameters .68 .30 .01 .01 (predicts stc2 ret2 s100p) (predicts wnt5a ret2 s100p) wnt5a ret2 stc2 ret2 0 1 s100p Bryce & Kim -- IJCAI-07

  26. Predictor Encoding (= (noise) .01) ;s100p predictor1 (predicts1 wnt5a ret2 s100p) (pred-fn wnt5a ret2 s100p zo) ;1 (pred-fn wnt5a ret2 s100p oz) ;1 (pred-fn wnt5a ret2 s100p oo) ;1 (= (influence1 wnt5a ret2 s100p) .69) ;s100p predictor2 (predicts2 stc2 ret2 s100p) (pred-fn stc2 ret2 s100p oz) ;1 (pred-fn stc2 ret2 s100p oo) ;1 (= (influence2 stc2 ret2 s100p) .31) Bryce & Kim -- IJCAI-07

  27. Control Encoding <control>, perfect/partial obeservation (:action down-regulate :parameters (?gr ?go - gene) :precondition (and (observed ?go) (controlled ?gr) (started)) :effect (and (decrease (reward) 1) (when (up-regulated ?gr) (not (up-regulated ?gr))) <dynamics + (not (= ?g ?gr))> ) :observation ( ((up-regulated ?go) (up-regulated ?go) 1) ((not (up-regulated ?go)) (not (up-regulated ?go)) 1) ) ) Could be better model!? (when (up-regulated ?gr) (probabilistic .75 (not (up-regulated ?gr)))) Bryce & Kim -- IJCAI-07

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