1. Pat Langley School of Computing and Informatics Arizona State University Tempe, Arizona Computational Assistance for Systems Biology of Aging Thanks to D. Bidaye, J. Difzac, J. Furber, S. Kim, S. Racunas, N. Shah, J. Shrager, D. Stracuzzi, and M. Verdicchio for their contributions to this research, which was funded in part by a grant from Science Foundation Arizona.

2. The Complexity of Human Aging Mutation of mitochondrial DNA Accumulation of lipofuscin in lysosomes Protein crosslinking in extra-cellular matrix Cell senescence and cell loss There is mounting evidence that aging involves many interacting mechanisms, including: The daunting complexity of modeling these and other processes suggests the need for computational assistance.

3. Furbers Network Diagram of Aging

4. Challenges for a Systems Biology of Aging Informal, in that the meanings of the diagrams nodes and links are imprecise; Inert, in that it needs a human interpreter to produce model explanations or predictions; and Static, in that the model cannot be updated or revised over time without considerable effort. Furbers diagram offers a good step toward a systems biology of aging, but it remains: In this talk, I present some computational responses to these three challenges that build on Furbers work.

5. An Initial Modeling Environment

6. Formal Representation of Aging Processes Differential equations require functional forms and parameters Boolean networks assume discrete, not continuous, variables Bayesian networks require arbitrary probabilistic parameters We want a notation for models of aging that is precise enough for a digital computer. Computational biology is rife with candidate formalisms, but most are problematic: We need a notation that makes closer contact with biologists ideas about aging mechanisms. Kuipers (1986) qualitative modeling formalism offers many of the features that we desire.

7. Qualitative Causal Models of Aging Places in the cell (e.g., lysosome, cytoplasm) Quantities that are measured in a place For unstable entities (e.g., ROS, Fe) For stable entities (e.g., oxidized proteins, lipofuscin) Causal influences between quantities Increase/decrease of one quantity with another Increase/decrease of quantitys rate of change We can state a model as a set of qualitative elements, each of which specifies: Each influence describes a qualitative causal link between two quantitative variables.

8. A Formal Lysosome Model – + + – + ++ + + Time – LysosomeCytoplasm Fe ROS lytic-enzyme membrane-damage lipofuscin junk-proteinjunk-protein H202 lipofuscin H202 oxidized-protein + + +

9. Examining the Lysosome Model > load lyso.lisp loaded lyso.lisp > show places entities Places: p1: cell p2: lysosome in the cell p3: cytoplasm in the cell Quantities: q0: time q1: junk-protein in the lysosome q2: junk-protein in the cytoplasm q3: fe in the lysosome q4: ros in the lysosome q5: oxidized-protein in the lysosome q6: lipofuscin in the lysosome q7: lipofuscin in the cytoplasm q8: lytic-enzyme in the lysosome q9: damaged-membrane in the lysosome q10: h2o2 in the lysosome q11: h2o2 in the cytoplasm

10. Examining the Lysosome Model > show claims Claims: c1: junk-protein in the lysosome decreases with time c2: junk-protein in the cytoplasm increases with time c3: junk-protein in the lysosome increases with junk-protein in the cytoplasm c4: fe increases with junk-protein in the lysosome [lysosome digestion produces fe from junk proteins] c5: ros increases with fe in the lysosome c6: oxidized-protein increases with ros in the lysosome [ros oxidizes proteins to produce oxidized proteins] c7: lipofuscin increases with oxidized-protein in the lysosome [oxidized-proteins and fe crosslink to form lipofuscin] c8: c2 decreases with lipofuscin in the lysosome [lipofuscin reduces the rate of disassembling junk proteins] c9: lytic-enzyme decreases with lipofuscin in the lysosome c10: ros increases with lipofuscin in the lysosome c11: damaged-membrane increases with ros in the lysosome c12: lipofuscin in the cytoplasm increases with damaged-membrane in the lysosome [damaged lysosomal membranes spill lipofuscin into cytoplasm] c13: h2o2 in the lysosome increases with h2o2 in the cytoplasm

11. Reasoning About Aging Models What biological effects does the model predict? What observations/experiments does the model explain? What portions of the model explain a given phenomenon? How would changes to the model alter its predictions? A systems model of aging is lacking unless one can relate it to observable phenomena. Such an account should let one answer questions like: Model complexity can make these difficult to do manually, but we can provide computational support for such reasoning.

12. Encoding Predictions and Observations A quantity increases with another quantity E.g., lipofuscin in the lysosome increases with time A quantity decreases with another quantity E.g., lytic-enzyme decreases with ROS in the lysosome A quantity does not change with another quantity E.g., H2O2 does not vary with ROS in the lysosome Before one can relate a models predictions to observations, we must represent them both. Our environment uses a notation similar to model claims: Note: These describe phenomena that the model does or should predict; they are not part of the model themselves.

13. Examining Facts, Queries, and Predictions > show facts Facts: f1: lipofuscin in the lysosome increases with time f2: lipofuscin in the cytoplasm increases with time f3: lytic-enzyme decreases with ros in the lysosome f4: h2o2 does-not-change with ros in the lysosome

14. Examining Facts, Queries, and Predictions > show facts Facts: f1: lipofuscin in the lysosome increases with time f2: lipofuscin in the cytoplasm increases with time f3: lytic-enzyme decreases with ros in the lysosome f4: h2o2 does-not-change with ros in the lysosome > does lytic-enzyme change with ros in the lysosome ? p1: lytic-enzyme decreases with ros in the lysosome > does oxidized-protein change with h2o2 in the lysosome ? p2: oxidized-protein does-not-change with h2o2 in the lysosome

15. Examining Facts, Queries, and Predictions > show facts Facts: f1: lipofuscin in the lysosome increases with time f2: lipofuscin in the cytoplasm increases with time f3: lytic-enzyme decreases with ros in the lysosome f4: h2o2 does-not-change with ros in the lysosome > does lytic-enzyme change with ros in the lysosome ? p1: lytic-enzyme decreases with ros in the lysosome > does oxidized-protein change with h2o2 in the lysosome ? p2: oxidized-protein does-not-change with h2o2 in the lysosome > predict f1 f2 f3 f4 f1: lipofuscin in the lysosome increases with time the model makes ambiguous predictions. f2: lipofuscin in the cytoplasm increases with time the model makes ambiguous predictions. f3: lytic-enzyme decreases with ros in the lysosome p3: the model predicts the same relation. f4: h2o2 does-not-change with ros in the lysosome p4: the model predicts the same relation.

16. Generating Model Predictions Chains backward from dependent term D to find all paths that connect it with independent term I. If no paths exist, then it predicts no empirical relationship. If a path has an even number of decreases links, it predicts D increases with I, else that D decreases with I. Provides a definite relationship if all path predictions agree. Notes an ambiguous relationship if path predictions disagree. Given a qualitative query about the relation between two model quantities, the environment: The same form of qualitative reasoning predicts both changes over time and responses to experimental manipulation.

17. Conflicting Model Pathways – + + – + ++ + + Time – LysosomeCytoplasm Fe ROS lytic-enzyme membrane-damage lipofuscin H202 lipofuscin H202 oxidized-protein + + + junk-proteinjunk-protein

18. Altering a Qualitative Model of Aging Adding a new place, quantity, claim, or fact Adding a note (e.g., a URL) to a claim or fact Removing a place, quantity, claim, or fact Disabling or enabling a claim or fact Saving revisions to a file that can be loaded later Biologists should also be able to extend and revise models of aging easily and efficiently. Our environment lets users alter the current model interactively in five basic ways: Some changes can alter the models implications and its ability to explain observed results.

19. Examining the Effects of Model Revision > predict f1: lipofuscin in the lysosome increases with time the model makes ambiguous predictions. f2: lipofuscin in the cytoplasm increases with time the model makes ambiguous predictions. f3: lytic-enzyme decreases with ros in the lysosome p3: the model predicts the same relation. f4: h2o2 does-not-change with ros in the lysosome p4: the model predicts the same relation.

20. Examining the Effects of Model Revision > predict f1: lipofuscin in the lysosome increases with time the model makes ambiguous predictions. f2: lipofuscin in the cytoplasm increases with time the model makes ambiguous predictions. f3: lytic-enzyme decreases with ros in the lysosome p3: the model predicts the same relation. f4: h2o2 does-not-change with ros in the lysosome p4: the model predicts the same relation. > disable c1 [ junk-protein in the lysosome decreases with time]

21. Examining the Effects of Model Revision > predict f1: lipofuscin in the lysosome increases with time the model makes ambiguous predictions. f2: lipofuscin in the cytoplasm increases with time the model makes ambiguous predictions. f3: lytic-enzyme decreases with ros in the lysosome p3: the model predicts the same relation. f4: h2o2 does-not-change with ros in the lysosome p4: the model predicts the same relation. > disable c1 [ junk-protein in the lysosome decreases with time] > predict f1: lipofuscin in the lysosome increases with time p5: the model predicts the same relation. f2: lipofuscin in the cytoplasm increases with time p6: the model predicts the same relation. f3: lytic-enzyme decreases with ros in the lysosome p7: the model predicts the same relation. f4: h2o2 does-not-change with ros in the lysosome p8: the model predicts the same relation.

22. Status of the Interactive System Load, examine, and alter a qualitative model of aging Answer queries about how one quantity affects another Compare the models predictions to observed facts Determine effects of model revisions on predictions Our computational assistant is still under development, but the system already lets users: Also, we have an initial encoding for the lysosomal portion of Furbers network diagram. We have much work ahead of us, but we also have the basic machinery and a partial knowledge base in place.

23. Support for the Aging Research Community Store shared beliefs about aging phenomena and processes Exchange new observational facts and propose hypotheses Update the community model of aging incrementally Initial users of our interactive modeling system are likely to be individual biologists or laboratories. However, a Web-based version would benefit the distributed aging research community, which could use it to: The system would serve as a graphical wiki that organizes knowledge, directs discussion, and grows over time. However, content would consist of formally stated models and phenomena, rather than documents.

24. formalizations of biological knowledge (e.g., EcoCyc, 2003) qualitative reasoning and simulation (e.g., Kuipers, 1986) languages for scientific simulation (e.g., STELLA, P ROMETHEUS ) Web-supported tools for biological visualization (e.g., KEGG) Web-based tools for biological processing (e.g., BioBike, 2007) Intellectual Influences Our approach to computational biological aides incorporates ideas from many traditions: However, it combines these ideas in novel ways to assist in the construction of system-level models of aging.

25. Directions for Future Research expand our encoding of aging events and processes formalize more content from Furbers network diagram provide a graphical interface for viewing and using models make the system accessible remotely using the Web support community-based development of models evaluate the softwares actual usability for biologists Our effort is still in its early stages, and we need further work to: Together, these changes should make our interactive system a powerful computational aid for aging researchers.

26. Key Contributions In summary, we are developing an interactive computational aid for systems biology of aging that supports: The system is still immature, but a more advanced version would offer many benefits to the aging community. Formal models of aging that specify clear relationships among known biological quantities; Interpretable models that let a computer predict and explain known phenomena; and Revisable models that users can evaluate, update, and revise with little training and effort.

27. End of Presentation