1. Pat Langley Institute for the Study of Learning and Expertise Palo Alto, California and Center for the Study of Language and Information Stanford University, Stanford, California http://www.isle.org/~langley langley@csli.stanford.edu Knowledge and Data in Computational Biological Discovery Thanks to V. Brooks, S. Klooster, A. Pohorille, C. Potter, K. Saito, M. Schwabacher, J. Shrager, and A. Torregrosa.

2. Motivations for Computational Discovery better predict and control future events better predict and control future events understand both previous and future events understand both previous and future events communicate that understanding to others communicate that understanding to others Humans strive to discover new knowledge from experience so that they can: Computational techniques should let us automate and/or assist this discovery process. Recent research on computational discovery has made progress on some of these issues but downplayed others.

3. Three Revolutions The scientific revolution (~1700) introduced formalisms to describe and explain natural phenomena. The scientific revolution (~1700) introduced formalisms to describe and explain natural phenomena. The heuristic search revolution (~1957) introduced computer algorithms to automate problem solving. The heuristic search revolution (~1957) introduced computer algorithms to automate problem solving. The data revolution (~1995) introduced collection of large data repositories for many domains. The data revolution (~1995) introduced collection of large data repositories for many domains. The discovery process has been aided by three major advances: Different paradigms for computer-aided discovery focus on some developments more than others.

4. The Data Mining Paradigm emphasizing the availability of vast amounts of data; emphasizing the availability of vast amounts of data; drawing on computational heuristic search to find regularities in these data; drawing on computational heuristic search to find regularities in these data; using formalisms like decision trees, association rules, and Bayes nets to describe those regularities. using formalisms like decision trees, association rules, and Bayes nets to describe those regularities. One paradigm, often known as data mining or KDD, can be best characterized as: Thus, most KDD researchers favor their own formalisms over those used by scientists and engineers. As a result, their discoveries are seldom very communicable to members of those communities.

5. The Scientific Discovery Paradigm drawing on computational heuristic search to find regularities in scientific data, either historical or novel; drawing on computational heuristic search to find regularities in scientific data, either historical or novel; using formalisms like numeric laws, structural models, and reaction pathways to describe regularities. using formalisms like numeric laws, structural models, and reaction pathways to describe regularities. A second paradigm, computational scientific discovery, can be characterized as: Thus, researchers in this framework favor representations used by scientists and engineers. As a result, their systems discoveries are more communicable to members of those communities.

6. Time Line for Research on Computational Scientific Discovery 1989199019791980198119821983198419851986198719881991199219931994199519961997199819992000 Bacon.1–Bacon.5 Abacus, Coper Fahrehneit, E*, Tetrad, IDS N Hume, ARC DST, GP N LaGrange SDS SSF, RF5, LaGramge Dalton, Stahl RL, Progol Gell-Mann BR-3, Mendel Pauli Stahlp, Revolver Dendral AM GlauberNGlauber IDS Q, Live IE Coast, Phineas, AbE, Kekada Mechem, CDP Astra, GP M HR BR-4 Numeric lawsQualitative lawsStructural modelsProcess models Legend

7. Successes of Computational Scientific Discovery Over the past decade, systems of this type have helped discover new knowledge in many scientific fields: stellar taxonomies from infrared spectra (Cheeseman et al., 1989)stellar taxonomies from infrared spectra (Cheeseman et al., 1989) qualitative chemical factors in mutagenesis (King et al., 1996)qualitative chemical factors in mutagenesis (King et al., 1996) quantitative laws of metallic behavior (Sleeman et al., 1997)quantitative laws of metallic behavior (Sleeman et al., 1997) quantitative conjectures in graph theory (Fajtlowicz et al., 1988)quantitative conjectures in graph theory (Fajtlowicz et al., 1988) temporal laws of ecological behavior (Todorovski et al., 2000)temporal laws of ecological behavior (Todorovski et al., 2000) reaction pathways in catalytic chemistry (Valdes-Perez, 1994, 1997)reaction pathways in catalytic chemistry (Valdes-Perez, 1994, 1997) Each of these has led to publications in the refereed literature of the relevant scientific field.

8. Research Themes focusing on domains that involve temporal and spatial data focusing on domains that involve temporal and spatial data generating explanations that involve hidden objects/variables generating explanations that involve hidden objects/variables drawing on domain knowledge to constrain the search process drawing on domain knowledge to constrain the search process developing interactive discovery tools for use by scientists developing interactive discovery tools for use by scientists We aim to extend previous approaches to computational discovery of communicable knowledge by: Within these guidelines, we are open to any search algorithm that can produce such communicable knowledge. As in earlier work, our notation for discovered knowledge will be the same as that used by experts in the domain.

9. Some Interesting Ecological Questions What environmental variables determine the production of carbon and the generation of various gases? What environmental variables determine the production of carbon and the generation of various gases? What functional forms relate these predictive variables to the ones they influence? What functional forms relate these predictive variables to the ones they influence? How do extreme values of these variables affect behavior of the ecosystem? How do extreme values of these variables affect behavior of the ecosystem? Are the Earth ecosystem parameters constant or have values changed in recent years? Are the Earth ecosystem parameters constant or have values changed in recent years?

10. The Task of Ecological Modeling Given: A model of Earths ecosystem (CASA) stated as difference equations that involve observable and hidden variables. Given: Values of observable variables (rainfall, sunlight, NPP) as they change over both time and space. Given: Inferred values for global parameters and intrinsic properties associated with discrete variables (e.g., ground cover). Find: A revised ecosystem model with altered equations and/or parametric values that better fits the data. S_LEAF NPP M_LEAFM_ROOTS_ROOT MIN_NLEAF_MICSOIL_MIC

11. The NPPc Portion of CASA NPPc = month max (E · IPAR, 0) E = 0.56 · T1 · T2 · W E = 0.56 · T1 · T2 · W T1 = 0.8 + 0.02 · Topt – 0.0005 · Topt 2 T1 = 0.8 + 0.02 · Topt – 0.0005 · Topt 2 T2 = 1.18 / [(1 + e 0.2 · (Topt – Tempc – 10) ) · (1 + e 0.3 · (Tempc – Topt – 10) )] T2 = 1.18 / [(1 + e 0.2 · (Topt – Tempc – 10) ) · (1 + e 0.3 · (Tempc – Topt – 10) )] W = 0.5 + 0.5 · EET / PET W = 0.5 + 0.5 · EET / PET PET = 1.6 · (10 · Tempc / AHI) A · PET-TW-M if Tempc > 0 PET = 1.6 · (10 · Tempc / AHI) A · PET-TW-M if Tempc > 0 PET = 0 if Tempc < 0 PET = 0 if Tempc < 0 A = 0.00000068 · AHI 3 – 0.000077 · AHI 2 + 0.018 · AHI + 0.49 A = 0.00000068 · AHI 3 – 0.000077 · AHI 2 + 0.018 · AHI + 0.49 IPAR = 0.5 · FPAR-FAS · Monthly-Solar · Sol-Conver IPAR = 0.5 · FPAR-FAS · Monthly-Solar · Sol-Conver FPAR-FAS = min [(SR-FAS – 1.08) / SR (UMD-VEG), 0.95] FPAR-FAS = min [(SR-FAS – 1.08) / SR (UMD-VEG), 0.95] SR-FAS = (Mon-FAS-NDVI + 1000) / (Mon-FAS-NDVI – 1000) SR-FAS = (Mon-FAS-NDVI + 1000) / (Mon-FAS-NDVI – 1000)

12. The NPPc Portion of CASA NPPc IPAR PET T1T2We_max E EET Tempc Topt NDVI SOLAR AHI A PET TWM SR FPAR VEG

13. The RF6 Discovery Algorithm 1. Creates a multilayer neural network that links predictive with predicted variables using additive and product units. 2. Invokes the BPQ algorithm to search through the weight space defined by this network. They have shown this approach can discover an impressive class of numeric equations from noisy data. Saito and Nakano (2000) describe RF6, a discovery system that: 3. Transforms the resulting network into a polynomial equation of the form y = c i x j d ij. of the form y = c i x j d ij.

14. Improving the NPPc Portion of CASA 1. Transform the NPPc model into a multilayer neural network that predicts the same behavior. 2. Identify portions of the NPPc model that are likely candidates for improvement. 3. Run the RF6 algorithm to revise those portions of the model (e.g., specified parameters or equations). 4. Transform the revised multilayer network back into numeric equations using the improved components. This suggests an approach to revising the NPPc model to better fit the observed data:

15. Three Facets of Model Revision Altering the value of parameters in a specified equation; Altering the value of parameters in a specified equation; Changing the associated values for an intrinsic property; and Changing the associated values for an intrinsic property; and Replacing the equation for a term with another expression. Replacing the equation for a term with another expression. Rather than initializing weights randomly, the system starts with weights based on parameters in the original model. We have applied this strategy to improve three different portions of the NPPc submodel. We have adapted RF6 to revise an existing quantitative model in three distinct ways:

16. Altering Parameters in the NPPc Model Initial model: T2 = 1.18 / [(1 + e 0.2 · (Topt – Tempc – 10) ) · (1 + e 0.3 · (Tempc – Topt – 10) )] T2 = 1.18 / [(1 + e 0.2 · (Topt – Tempc – 10) ) · (1 + e 0.3 · (Tempc – Topt – 10) )] Cross-validated RMSE = 467.910 Behavior: Gaussian-like function of temperature difference. Revised model: T2 = 1.80 / [(1 + e 0.05 · (Topt – Tempc – 10.8) ) · (1 + e 0.3 · (Tempc – Topt – 90.33) )] T2 = 1.80 / [(1 + e 0.05 · (Topt – Tempc – 10.8) ) · (1 + e 0.3 · (Tempc – Topt – 90.33) )] Cross-validated RMSE = 461.466 [ one percent reduction ] Behavior: nearly flat function in actual range of temperature difference. Conclusion: The T2 temperature stress term contributes little to the overall predictive ability of the NPPc submodel.

17. Revising Intrinsic Values in the Model The NPPc submodel includes one intrinsic property, SR, associated with the variable for vegetation type, UMD-VEG. The corresponding RF6 network includes one hidden node for SR and one dummy input variable for each vegetation type. Veg type A B C D E F G H I J K Veg type A B C D E F G H I J K Initial 3.06 4.35 4.35 4.05 5.09 3.06 4.05 4.05 4.05 5.09 4.05 Initial 3.06 4.35 4.35 4.05 5.09 3.06 4.05 4.05 4.05 5.09 4.05 Revised 2.57 4.77 2.20 3.99 3.70 3.46 2.34 0.34 2.72 3.46 1.60 Revised 2.57 4.77 2.20 3.99 3.70 3.46 2.34 0.34 2.72 3.46 1.60 RMSE = 467.910 for the original model; RMSE = 448.376 for the revised model, an improvement of four percent. Observation: Nearly all intrinsic values are lower in the revised model.

18. Revising Equations in the NPPc Model Initial model: E = 0.56 · T1 · T2 · W E = 0.56 · T1 · T2 · W Cross-validated RMSE = 467.910 Behavior: Each stress term decreases the photosynthetic efficiency E. Revised model: E = 0.521 · T1 0.00 · T2 0.03 · W 0.00 E = 0.521 · T1 0.00 · T2 0.03 · W 0.00 Cross-validated RMSE = 446.270 [ five percent reduction ] Behavior: T1 and W have no effect on E and T2 has only a minor effect. Conclusion: The stress terms are not useful to the NPPc model, most likely because of recent improvements in NDVI measures.

19. Future Work on Ecological Modeling Apply revision method to other parts of NPPc submodel and other static parts of CASA model. Apply revision method to other parts of NPPc submodel and other static parts of CASA model. Extend revision method to improve parts of CASA that involve difference equations. Extend revision method to improve parts of CASA that involve difference equations. Develop software for visualizing both spatial and temporal anomalies, as well as relating them to model. Develop software for visualizing both spatial and temporal anomalies, as well as relating them to model. Implement an interactive system that lets scientists direct high-level search for improved ecosystem models. Implement an interactive system that lets scientists direct high-level search for improved ecosystem models.

20. Visualizing Errors in the Model We can easily plot an improved models errors in spatial terms. Such displays can help suggest causes for prediction errors and thus ways to further improve the model.

21. Some Interesting Biological Questions How do organisms acclimate to increased temperature or ultraviolet radiation? Why do we observe bleaching of plant cells under high light conditions? What differences in biological processes exist between a mutant organism and the original? What are the effects on an organisms biological processes when one of its important genes is removed?

22. Modeling Results in Microarrary Experiments Given: A mutated organism with different macroscopic behavior in that environmental setting. Given: Observed expression levels, over time, of the mutants enzymes in the setting. Find: A revised model with altered reactions and regulations that explains the expression levels. Given: Qualitative knowledge about an organisms reactions and regulations for some environmental setting.

23. Modeling Microarrary Results on Photosynthesis Given: A mutated strain of Cyanobacteria that does not bleach when exposed to high ultraviolet light. Given: Observed expression levels, over time, of the mutants enzymes in the presence of high ultraviolet light. Find: A revised model with altered reactions and regulations that explains the expression levels and the failure to bleach. Given: Qualitative knowledge about reactions and regulations for Cyanobacteria in a high ultraviolet situation.

24. Why do plants modify their photosynthetic apparatus in high light? A Model of Photosynthesis Regulation HL -N -S -P -Cl nblS RR cpcX hliA psbx... Blue/UV-A Photoreceptor nblR nblB nblA Degradation of psaF,psaA,psaB Survival in High Light Modification of Photosynthesis

25. Collecting Data on Photosynthetic Processes Stress (e.g., High Light) Adaptation Period Sampling mRNA/cDNA Equlibrium Period MicroArray Trace Continuous Culture (Chemostat) /wwwscience.murdoch.edu.au/teach www.affymetrix.com/ Health of Culture Time

26. Microarray Data on Photosynthetic Regulation

27. Six Steps in Revising Regulation Models Our approach to revising an existing model involves six steps: 1. Generate candidate models with a single process removed. 2. Predict qualitative correlations between enzymes for each model. 3. Calculate the observed correlations between enzymes over time. 4. Measure the percentage of correct predictions for each model. 5. Select the revised model with the highest predictive accuracy. 6. Repeat this strategy until no revision leads to improvement. Thus, our system carries out heuristic search through the space of models, guided by candidates abilities to explain the data.

28. Heuristic Search Through a Space of Models Initial model Revision 1.1 Revision 1.2 Revision 1.3 Revision 1.4 Revision 2.1 Revision 2.2 Revision 2.3 Revision 2.4 Revision 3.1 Revision 3.2 Revision 3.3 Revision 3.4

29. The mutant is NblR deficient, so it does not down regulate NblA/B. HL -N -S -P -Cl nblS RR cpcX hliA psbx... Blue/UV-A Photoreceptor nblR nblB nblA Survival in High Light Modification of Photosynthesis A Revised Model of Photosynthesis Regulation X Degradation of psaF,psaA,psaB

30. Observed and Predicted Correlations Observed: nblS,nblR + nblS,nblA × nblS,nblB × nblS,psaF × nblS,psaA × nblS,paaB × nblR,nblA × nblR,nblB × nblR,psaF × nblR,psaA × nblR,psaB × nblA,psaF + nblA,psaA + nblA,psaB + nblA,psaF + nblA,psaA + nblS,nblR + nblS,nblA + nblS,nblB + nblS,psaF + nblS,psaA + nblS,paaB + nblR,nblA + nblR,nblB + nblR,psaF + nblR,psaA + nblR,psaB + nblA,psaF + nblA,psaA + nblA,psaB + nblA,psaF + nblA,psaA + nblS,nblR + nblS,nblA × nblS,nblB × nblS,psaF × nblS,psaA × nblS,paaB × nblR,nblA × nblR,nblB × nblR,psaF × nblR,psaA × nblR,psaB × nblA,psaF + nblA,psaA + nblA,psaB + nblA,psaF + nblA,psaA + Original:Revised:

31. Future Work on Biological Modeling Add more knowledge about photosynthetic pathways and use to interpret additional microarray data. Incorporate ability to introduce new regulation influences in addition to removing existing ones. Expand modeling formalism to include abstract processes like signal transduction and allosteric modulation. Implement an interactive system that lets scientists direct high-level search for improved biological process models.

32. Concluding Remarks attempts to move beyond description and prediction to both explanation and understanding; attempts to move beyond description and prediction to both explanation and understanding; uses domain knowledge to initialize search and to characterize differences from revised model; uses domain knowledge to initialize search and to characterize differences from revised model; presents the new knowledge in some communicable notation that is familiar to domain experts. presents the new knowledge in some communicable notation that is familiar to domain experts. In summary, unlike work in the data mining paradigm, our research on computational discovery: Such techniques will improve the way we manipulate, utilize, and understand complex scientific and engineering data.

34. Improving the Prediction of NDVI The Normalized Difference Vegetative Index (NDVI) is a central part of CASA that is measured by satellite sensors. Unfortunately, NDVI is only available for the years since 1983, when satellites with these sensors were launched. Potter and Brooks (1998) report a predictive model of NDVI that is a piecewise linear function of temperature, rainfall, and moisture. We hoped to improve this model using Cubist, which induces a set of regression rules from continuous data.

35. Form of the CASA NPPc Data TempNPPcToptEETPETNDVIAHIVeg January February March May April June July August September November October December Grid 1,1... Grid 360,360

36. An Improved Piecewise Linear Model Cubist produced a revised NDVI model with five piecewise linear components rather than two, all based on rainfall. This model explains 88% of the variance, compared with 74% of the variance for the Potter and Brooks model.

37. Visualizing the Improved Model One way to visualize the model involves plotting rules spatially. Our Earth science collaborators found this useful, as regions often correspond to recognizable ecological zones.

38. The Task of Metabolic Modeling Given: Knowledge about the metabolism of an organism stated as biochemical reactions. Given: Observed environmental situations and expression levels of enzymes from microarrays. Find: A complete metabolic model that explains the observed expression levels. Acetoacetyl-CoA EC2.8.3.5 Acetoacetate Acetyl-CoA EC4.1.3.5EC4.1.3.4 Intermediate

39. Five Steps in Metabolic Model Revision Our general approach to metabolic modeling involves six steps: 1. Represent biochemical reactions known for the organism. 2. Find complete metabolic pathways through heuristic search. 3. Order metabolic pathways using matches to microarray data. 4. Simulate natural or experimental knockouts of genes/enzymes. 5. Propose bridging reactions that explain the observed behavior. 6. Order reactions using reaction analogy and DNA sequences. We will illustrate these steps with an example from glycolysis and the TCA cycle.

40. Step 1. Represent Biochemical Reactions

41. CYTOSOLIC:glucose + ATP ---[Hexokinase]--> glucose 6-phosphate + ADP CYTOSOLIC:1,3-bisphosphoglycerate + ADP ---[Phosphoglycerate kinase]--> 3-phosphoglycerate + ATP MITOCHONDRIAL:isocitrate + NAD+ ---[Isocitrate dehydrogenase]--> a-ketoglutarate + NADH + H+ + Co2 MITOCHONDRIAL:succinyl CoA + GDP + phosphatate ---[Succinyl CoA synthase]--> succinate + GTP + CoA Step 1. Represent Biochemical Reactions

42. Step 2. Find Pathways by Heuristic Search Target = Malate Solution for Fructose environment fructose ---[Fructokinase]--> fructose 1-phosphate fructose 1-phosphate ---[Fructose 1-phosphate aldolase]--> glyceraldehyde + dihydrozyacetone phosphate dihydrozyacetone phosphate ---[Isomerase]--> glyceraldehyde 3-phosphate phosphatate + NAD+ + glyceraldehyde 3-phosphate ---[Triose phosphate dehydrogenase]--> 1,3-bisphosphoglycerate 1,3-bisphosphoglycerate + ADP ---[Phosphoglycerate kinase]--> 3-phosphoglycerate + ATP 3-phosphoglycerate ---[Phosphoglyceromutase]--> 2-phosphoglycerate 2-phosphoglycerate ---[Enolase]--> phosphoenolpyruvate + H2O phosphoenolpyruvate + ATP ---[Pyruvate kinase]--> pyruvate + ADP malate + NAD+ ---[Malate dehydrogenase]--> oxaloacetate + NADH + H+ pyruvate + NAD+ + CoA ---[NIL]--> NADH + H+ + Co2 + acetyl CoA acetyl CoA + oxaloacetate ---[Citrate synthase]--> citrate + CoA citrate ---[Aconitase]--> isocitrate isocitrate + NAD+ ---[Isocitrate dehydrogenase]--> a-ketoglutarate + NADH + H+ + Co2 a-ketoglutarate + NAD+ + CoA ---[a-ketogluterate dehydrogenase complex]--> succinyl CoA + NADH + H+ + Co2 succinyl CoA + GDP + phosphatate ---[Succinyl CoA synthase]--> succinate + GTP + CoA succinate + FAD ---[Succinate dehydrogenase]--> fumarate + FADH2 fumarate + H2O ---[Fumerase]--> malate Solution for Glucose environment glucose + ATP ---[Hexokinase]--> glucose 6-phosphate + ADP glucose 6-phosphate ---[Phosphoglucomutase]--> fructose 6-phosphate fructose 6-phosphate + ATP ---[Phosphofructokinase]--> fructose 1,6 bisphosphate + ADP fructose 1,6 bisphosphate ---[Aldolase]--> dihydrozyacetone phosphate + glyceraldehyde 3-phosphate phosphatate + NAD+ + glyceraldehyde 3-phosphate ---[Triose phosphate dehydrogenase]--> 1,3-bisphosphoglycerate 1,3-bisphosphoglycerate + ADP ---[Phosphoglycerate kinase]--> 3-phosphoglycerate + ATP [...same as above from this point onward...]

43. Step 3. Order Pathways by Likelihood Given Data www.affymetrix.com/ fructose ---[Fructokinase]--> fructose 1-phosphate fructose 1-phosphate ---[Fructose 1-phosphate aldolase]--> glyceraldehyde + dihydrozyacetone phosphate dihydrozyacetone phosphate ---[Isomerase]--> glyceraldehyde 3-phosphate phosphatate + NAD+ + glyceraldehyde 3-phosphate ---[Triose phosphate dehydrogenase]--> NADH + H+ + 1,3-bisphosphoglycerate 1,3-bisphosphoglycerate + ADP ---[Phosphoglycerate kinase]--> 3-phosphoglycerate + ATP 3-phosphoglycerate ---[Phosphoglyceromutase]--> 2-phosphoglycerate 2-phosphoglycerate ---[Enolase]--> phosphoenolpyruvate + H2O phosphoenolpyruvate + ATP ---[Pyruvate kinase]--> pyruvate + ADP malate + NAD+ ---[Malate dehydrogenase]--> oxaloacetate + NADH + H+ pyruvate + NAD+ + CoA ---[NIL]--> NADH + H+ + Co2 + acetyl CoA acetyl CoA + oxaloacetate ---[Citrate synthase]--> citrate + CoA citrate ---[Aconitase]--> isocitrate isocitrate + NAD+ ---[Isocitrate dehydrogenase]--> a-ketoglutarate + NADH + H+ + Co2 a-ketoglutarate + NAD+ + CoA ---[a-ketogluterate dehydrogenase complex]--> succinyl CoA + NADH + H+ + Co2 succinyl CoA + GDP + phosphatate ---[Succinyl CoA synthase]--> succinate + GTP + CoA succinate + FAD ---[Succinate dehydrogenase]--> fumarate + FADH2 fumarate + H2O ---[Fumerase]--> malate

44. Step 4. Simulate Natural or Experimental Knockouts glucose + ATP ---[Hexokinase]--> glucose 6-phosphate + ADP glucose 6-phosphate ---[Phosphoglucomutase]--> fructose 6-phosphate fructose 6-phosphate + ATP ---[Phosphofructokinase]--> fructose 1,6 bisphosphate + ADP fructose 1,6 bisphosphate ---[Aldolase]--> dihydrozyacetone phosphate + glyceraldehyde 3-phosphate phosphatate + NAD+ + glyceraldehyde 3-phosphate ---[Triose phosphate dehydrogenase]--> 1,3-bisphosphoglycerate 1,3-bisphosphoglycerate + ADP ---[Phosphoglycerate kinase]--> 3-phosphoglycerate + ATP 3-phosphoglycerate ---[Phosphoglyceromutase]--> 2-phosphoglycerate 2-phosphoglycerate ---[Enolase]--> phosphoenolpyruvate + H2O phosphoenolpyruvate + ATP ---[Pyruvate kinase]--> pyruvate + ADP malate + NAD+ ---[Malate dehydrogenase]--> oxaloacetate + NADH + H+ pyruvate + NAD+ + CoA ---[NIL]--> NADH + H+ + Co2 + acetyl CoA acetyl CoA + oxaloacetate ---[Citrate synthase]--> citrate + CoA citrate ---[Aconitase]--> isocitrate isocitrate + NAD+ ---[Isocitrate dehydrogenase]--> a-ketoglutarate + NADH + H+ + Co2 a-ketoglutarate + NAD+ + CoA ---[a-ketogluterate dehydrogenase complex]--> succinyl CoA + NADH + H+ + Co2 succinyl CoA + GDP + phosphatate ---[Succinyl CoA synthase]--> succinate + GTP + CoA succinate + FAD ---[Succinate dehydrogenase]--> fumarate + FADH2 fumarate + H2O ---[Fumerase]--> malate 1,3-bisphosphoglycerate + ADP ---[Phosphoglycerate kinase]--> 3-phosphoglycerate + ATP Knockout:

45. Step 5. Propose Bridging Reactions Abstract Chemicial Knowledge + ATP 6 Carbons 0 Phosphates 6 Carbons 1 Phosphate 3 Phosphates2 Phosphates glucose + ATP ---[Hexokinase]--> glucose 6-phosphate + ADP ADP Abstract Balance Constrained Search

46. 25 plausible (single) bridging reactions are proposed: 3-phosphoglycerate> phosphoenolpyruvate + 3-phosphoglycerate> 2-phosphoglycerate + 3-phosphoglycerate> 3-phosphoglycerate + 3-phosphoglycerate> ADP + 1,3-bisphosphoglycerate + 3-phosphoglycerate> glyceraldehyde 3-phosphate + 3-phosphoglycerate> dihydrozyacetone phosphate + 3-phosphoglycerate> ATP + Co2 + acetyl + 3-phosphoglycerate> <CYTOSOLIC:ADP + 1,3-bisphosphoglycerate ---[]--> ATP + 3-phosphoglycerate> ATP + pyruvate + 3-phosphoglycerate> ATP + glycerate + 3-phosphoglycerate> ATP + glyceraldehyde + 3-phosphoglycerate> ATP + dihydroxyacetone + 3-phosphoglycerate> ADP + phosphoenolpyruvate + 3-phosphoglycerate> ADP + 2-phosphoglycerate + 3-phosphoglycerate> ADP + 3-phosphoglycerate + 3-phosphoglycerate> ADP + glyceraldehyde 3-phosphate + 3-phosphoglycerate> ADP + dihydrozyacetone phosphate + 3-phosphoglycerate> Co2 + acetyl + 3-phosphoglycerate> pyruvate + 3-phosphoglycerate> glycerate + 3-phosphoglycerate> glyceraldehyde + 3-phosphoglycerate> dihydroxyacetone + 3-phosphoglycerate> 1,3-bisphosphoglycerate + 3-phosphoglycerate> 1,3-bisphosphoglycerate + ADP ---[Phosphoglycerate kinase]--> 3-phosphoglycerate + ATP Knockout: Step 5. Propose Bridging Reactions

47. www.bio.davidson.edu/Biology Step 6. Order Bridging Reactions by Likelihood Homology of hexokinase across species: We also measure similarity in structure between each bridging reaction and the knocked out reaction.

48. Microarray Data on Photosynthetic Regulation