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, D. Stracuzzi, and M. Verdicchio for their contributions to this research, which was funded in part by a grant from Science Foundation Arizona.

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 a variety of interacting mechanisms, including: The daunting complexity of modeling these and other processes suggests the benefits of computational assistance.

Furber’s Network Diagram of Aging

Challenges for a Systems Biology of Aging   Informal, in that the meanings of the diagram’s nodes and links are imprecise;   Static, in that the model cannot be updated or revised over time without some effort; and   Inert, in that it needs a human interpreter to produce model explanations or predictions. Furber’s 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 Furber’s work.

Formal Representation of Aging Processes   Differential equation models require quantitative knowledge   Boolean networks assume discrete, not continuous, variables   Causal networks are abstract and ignore notion of processes 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. Forbus’ (1984) qualitative process formalism offers many of the features that we desire.

Qualitative Process Models of Aging   Entities or events involved in the process   Conditions for operation of the process   Effects that result from the process   Occurrence of an entity or event   Increase/decrease in entity amount or event rate   Transport of entity or damage to it We can state a model as a set of qualitative processes, each of which specifies: A qualitative process describes a causal relationship, but in a richer manner than many schemes. A model also includes a set of assumptions about the situation.

Sample Aging Processes damage(LY, LMEM)  lysosome(LY), lysomem(LMEM), ros(RS), amount(LY, RS). transport(CYT, LY, LYE)  cytoplasm(CYT), lysosome(LY), lyticenzyme(LYE), lysomem(LMEM), lipofuscin(LF), increase(amount(LY, LF)), damage(LY, LMEM).

Visualizing a Process Model of Aging

Linking Model Components to Literature

Altering a Process Model of Aging   Changing the conditions or effects of a given process;   Specifying and adding an entirely new process;   Removing an existing process; and   Adding or removing a model assumption. Users can alter the current process model interactively in four basic ways: The software’s graphical interface makes it relatively easy to implement such changes. Such changes can alter the model’s implications, which we will discuss shortly.

Visualizing an Altered Model

Reasoning About Aging Processes   What biological effects does the model predict?   What portions of the model explain a given phenomenon?   How would changes to the model alter its predictions?   What therapeutic targets exist for an undesirable effect? A systems model of aging is lacking unless one can relate it to observable phenomena. Such an account should let one answer questions like: The model’s complexity can make this difficult to do manually, but we can provide computational support for such reasoning.

Generating Model Predictions   Given: A set of qualitative processes stated as logical rules;   Given: A set of assumptions about the biological situation;   Find: Zero or more sets of biological results consistent with the rules and assumptions. Our software uses a logical inference engine called Answer Set Prolog (Lipschitz, 2008) to reason about models: The reasoning engine carries out search through the space of possible worlds, with each set mapping onto one such world. This inference mechanism can also determine if a model has internal inconsistencies.

Examining a Model’s Predictions

Tracing an Explanatory Chain   Make R the focus of attention;   Find all process that have R as one of their effects;   For each condition C of each such process,   If C matches an assumption, then collect C and backtrack;   Else make C the focus of attention and apply recursively. We can extract the explanatory chain that accounts for a given result R by a simple recursive method: This scheme walks backward through the model, collecting the rules and assumptions on which the result R depend.

Visualizing an Explanatory Chain

Visualizing Changes in Model Predictions

Status of the Interactive System   Visualize and edit a qualitative model of aging;   Inspect details and references behind model components;   Trace the reasoning chains that produced a result; and   Infer effects of model revisions and visualize them. Our computational assistant hass not yet fulled developed, but the system already lets users: Also, we have initial encodings for both the lysosomal and the mitochondrial portions of Furber’s network diagram. We have much work ahead of us, but we also have the basic machinery and an initial knowledge base in place.

Support for the Aging Research Community   Store shared beliefs about aging phenomena and processes   Exchange new phenomena and propose hypotheses   Update the community aging model 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.

  formalizations of biological knowledge (e.g., EcoCyc, 2003)   Web-supported tools for biological visualization (e.g., KEGG)   Web-based tools for biological processing (e.g., BioBike, 2007)   qualitative reasoning and simulation (e.g., Forbus, 1984)   languages for scientific simulation (e.g., STELLA, P ROMETHEUS ) 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.

Directions for Future Research  improve our encoding of aging events and processes  formalize more content from Furber’s network diagram  add other inference abilities (e.g., therapeutic reasoning)  make the system accessible remotely using the Web  support community-based development of models  evaluate the software’s 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 the aging research community.

Key Contributions  encodes aging entities, events, and processes in logical form;  displays the resulting models in a graphical notation;  draw inferences from a model’s rules and assumptions;  identifies the explanatory chains that lead to each result;  supports the incremental revision of model components;  presents the effects of model revisions on predictions. In summary, we are developing an interactive computer aid that: The system is still immature, but a more advanced version would offer many benefits to the aging community.

End of Presentation