Pat LangleySchool of Computing and Informatics

Arizona State UniversityTempe, Arizona

Computational Assistance forSystems 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.

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.

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;

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.

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.

An Initial Modeling Environment

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.

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 quantity’s 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.

A Formal Lysosome Model

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TimeTime

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LysosomeLysosome CytoplasmCytoplasmFeFe

ROSROS

lytic-enzymelytic-enzyme

membrane-damagemembrane-damage

lipofuscinlipofuscin

junk-proteinjunk-protein junk-proteinjunk-protein

H202H202

lipofuscinlipofuscin

H202H202

oxidized-proteinoxidized-protein

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Examining the Lysosome Model

> load “lyso.lisp” loaded “lyso.lisp”> show places entitiesPlaces: p1: cell p2: lysosome in the cell p3: cytoplasm in the cellQuantities: 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

Examining the Lysosome Model

> show claimsClaims: 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

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.

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 model’s 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.

Examining Facts, Queries, and Predictions

> show factsFacts: 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

Examining Facts, Queries, and Predictions

> show factsFacts: 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

Examining Facts, Queries, and Predictions

> show factsFacts: 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.

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.

Conflicting Model Pathways

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TimeTime

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LysosomeLysosome CytoplasmCytoplasmFeFe

ROSROS

lytic-enzymelytic-enzyme

membrane-damagemembrane-damage

lipofuscinlipofuscin

H202H202

lipofuscinlipofuscin

H202H202

oxidized-proteinoxidized-protein

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junk-proteinjunk-protein junk-proteinjunk-protein

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 model’s implications and its ability to explain observed results.

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.

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]

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.

Status of the Interactive System

Load, examine, and alter a qualitative model of aging

Answer queries about how one quantity affects another

Compare the model’s 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 Furber’s network diagram.

We have much work ahead of us, but we also have the basic machinery and a partial knowledge base in place.

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.

formalizations of biological knowledge (e.g., EcoCyc, 2003)

qualitative reasoning and simulation (e.g., Kuipers, 1986)

languages for scientific simulation (e.g., STELLA, PROMETHEUS)

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.

Directions for Future Research

expand our encoding of aging events and processes

formalize more content from Furber’s 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 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 aging researchers.

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.

End of Presentation