BioMAS: A Multi-Agent System for Automated Genomic Annotation
Keith DeckerDepartment of Computer and Information Sciences
University of Delaware
Salim Khan, Ravi Makkena, Gang SituComputer & Information Sciences
Dr. Carl Schmidt, Heebal KimAnimal & Food Sciences
Outline General class of problems and MAS
solution approach BioMAS: Automated Genomic Annotation
HVDB: HerpesVirus Database ChickDB: Gallus Gallus Database
GOFigure! CoPrDom Signal Transduction Pathway Discovery
What problems are we addressing? Huge, dynamic “Primary Source” Databases
Highly distributed, overlapping Heterogeneous content, structure, curation
Multitude of analysis algorithms Different interfaces, output formats Create contingent process plans chaining many analyses
together Individual PIs, working on non-model organisms
Learn, then hand-navigate sea of DBs and analysis tools Easily overwhelmed by new sequence and EST data Struggle to make results available usefully to others
Approach: Multi-Agent Information Gathering Software agents for information retrieval, filtering,
integration, analysis, and display Embody heterogeneous database technology (wrappers,
mediators, …) Deal with dynamic data and changing data sources Efficient and robust distributed computation (for both info
retrieval and analysis) Deal with issues of data organization and ownership Natural approach to providing integrated information
To humans via web To other agents via semantic markup [XML/OIL/DAML]
Example: Multi-Agent System for Automated Herpesvirus Annotation Input raw sequence data Output: an annotated database that allows fairly complex
queries BLAST homologs Motifs Protein domains [Prodomain records] PSORT sub-cellular location predictions GO [Gene Ontology] electronic annotation
“Show me all the genes in Marek’s Disease virus with a tyrosine phosphorylation motif and a transmembrane domain value ≥ 2”
How does this help? Automates collection of information from various primary source
databases If the info changes, can be updated automatically. PI can be notified.
Allows various analyses to be done automatically Can encode complex (contingent) sequences of info retrieval and linked
analyses, report interesting results only New data sources, annotation, analyses can be applied as they are
developed, automatically (open system) Made available on internet to others, or private data
Much more sophisticated queries than keyword search Dynamic menu of keys Concept hierarchies (“ontology”) allow more concise queries Query planning (e.g., time, resource usage)
Can search across multiple databases (i.e., from other researchers)
How does it work?Sequence Addition Applet User Query Applet Interface Agents
GenBankInfo Extraction Agent
InformationExtraction Agents
ProDomainInfo Extraction Agent
SwissProt/ProSiteInfo Extraction Agent
Psort AnalysisWrapper
Local KnowledgebaseManagement AgentLocal Knowledgebase
Management AgentLocal KnowledgebaseManagement Agent
AnnotationAgent Task AgentsSequence Source
Processing Agent
Domain-IndependentTask Agents
QueryProcessing Agent
Matchmaker Agent
Agent Name Server Agent
ProxyAgent
RETSINA-styleMulti-Agent Organization
DECAF: A multi-agent system toolkit Focus on programming agents, not
designing internal architecture Programming at the multi-agent level Value-added architecture Support for persistent, flexible, robust
actions
DECAF Focus on programming agents, not
designing internal architecture Avoiding the API approach DECAF as agent “operating system”,
programmers have strictly limited access Communication, planning, scheduling,
[coordination], execution Graphical dataflow plan editor
DECAF Programming at the multi-agent level Standardized, domain-independent, reusable
“middle agents” Agent Name Server (white pages) Matchmaker (yellow pages/directory service) Brokers (managers) Information extraction (learning [STALKER] +
knowledgebase [PARKA]) Proxy (web interfaces) [Agent Management Agent (debugging, demos, external
control)] Note: heterogeneous architectures are OK!
DECAF Value-added architecture Taking care of details (social/individual)
ANS registration/dereg (eventually MM) Standard behaviors (AMA, error, FIPA, libraries) Message dispatching (ontology, conversation) Coordination (GPGP)
Efficient use of computational resources Highly threaded: internally + domain actions Memory efficient (ran systems for weeks, hundreds
of thousands of messages)
DECAF Support for persistent, flexible, robust
actions HTN-style programming
Task alternatives and contingencies RETSINA-style dataflow
Provisions/Parameters determine task activation Multiple outcomes, Loops
TÆMS-style task network annotations Dynamic overall utility: Quality, cost, duration task characteristics Explicit representation of non-local tasks Example: Time/Quality tradeoff
DECAF ArchitecturePlan file Incoming KQML/FIPA messages
Domain Facts and BeliefsOutgoing KQML/FIPA messages
Action ModulesAction ModulesAction ModulesAction ModulesAction Modules
Incoming Message Queue
Objectives Queue
Task Queue
AgendaQueue
Task TemplatesHash Table
PendingAction Queue
Action Results Queue
AgentInitialization Dispatcher Planner Scheduler Executor
[concurrent]
Plan Editor
Expanding the Genomic Annotation System Functional AnnotationApplet
SequenceLKBMA GenBankInfo Extraction AgentMouse Genome DBIEASGD (yeast)IEAFlybaseIEA
ProxyAgent Ontology ReasoningAgent OntologyAgent SNP-Finder ESTLKBMA EST Entry[Chromatograph/FASTA] ProxyAgent ConsensusSequenceChromatographProcessing
User QueryApplet Sequence AdditionApplet
SwissProt/ProSiteIEA PSortIEA ProDomainIEA
ProxyAgent AnnotationAgent Sequence SourceProcessing Agent ProxyAgent Query ProcessingAgentBasicSequenceAnnotationFunctionalAnnotationQueryESTProcessing
Functional Annotation Suborganization
Gene Ontology Consortiumwww.geneontology.org • Biological process • Molecular Function • Cellular Component
Co-present Domain Networks (CoPrDom)
Proteins can be viewed as conserved sets of domains Vertex = domain, edge = co-present in some protein, edge
weight = # of proteins co-present in Network constructed from InterPro domain markup of
proteins in 10 species (human, drosophila, c. elegans, s. cerevisiae among them)
Functional characterization via InterPro to GO mapping Network constructed per organism per functional group, eg:
apoptosis regulation in human
Uses for COPRDOM
Functional characterization of unknown domains Identification of core domains/groups in a
functional group Tracking domain evolution through species
evolution Predicting protein-protein interaction by
identifying evolutionary merging of domain groups
Biological Pathway Discovery thru AI Planning Techniques AI planning is a computational method to develop complex plans
of action using the representation of the initial states, the actions which manipulate these states to achieve the goal states specified.
Initial States: The initial state representation of objects in the "plan world"
Actions: Logical descriptions of preconditions and effects Goals: The end states desired
HTN (Hierarchical Task Network) Planning proceeds by task decompostion of networks, and a successful is one that satisfies a task network.
Uses of the Signal Transduction Planner To produce computer interpretable plans capturing relevant qualitative
information regarding signal transduction pathways.
To produce testable hypotheses regarding gaps in knowledge of the pathway, and drive future signal transduction research in an ordered manner.
To identify key nodes where many pathways are regulated by a node with only 1 functional protein serving as a critical checkpoint.
To perform in silico experiments of hyper expression and deletion mutation.
To enable pathway vizualization tools by providing human- and machine-readable pathway description.
Advantages of Planning Operator schema: Abstracted axiomatic definitions of sub-cellular
processes, understandable to human + computer Task abstraction: Decomposition of complex task into simpler,
interchangeable actions. Reduces search space, conflicts Modeling of pathways at different levels of biochemical detail
Search conducted in Plan Space: Most planners perform bi-directional search (vs. Pathway Tools, Prolog implementations, etc.)
Partial-order Planning: Succinct representation of multiple pathways helps identify key causal relationships
Advantages of Planning (contd.)
Conditional effects can be used to model special cases ("exceptions") when applying operator schema
Resource Utilization can be used to model quantitative aspects such as amplification of a signal, feedback and feed-forward loops
Plan re-use: Old plans can be successfully inserted into new ones (if initial and final conditions are met )without additional computation
(ontologically driven) Operator Schema Example: Transport
(action: transport :parameters (?mol - macromolecule, ?compfrom, ?compto - compartment)
:condition (and (in ?mol ?compfrom) (open ?compfrom ?compto))
:effects (and (in ?mol ?compto) (not (in ?mol ?compfrom)))
RTK-MAPK pathway
Activation of Ras following binding of a hormone (eg. EGF) to a receptor
RTK-MAPK pathway step: O-Plan Output
Phosphorylation of GRB2 at domain Sh2 by the RTK receptor
Summary
Bioinformatics has many features amenable to multi-agent information gathering approach
BioMAS: Automated Analysis: EST processing to functional annotation ontologies
DECAF / RETSINA / TÆMS GOFigure! And electronic GO annotation CoPrDom Co-Present Domain Analysis Signal Transduction Pathway Discovery
BioMAS Future Work Sophisticated queries are possible, but how to make available to
Biologists?? “Show me all glycoproteins in Marek’s Disease virus with a tyrosine phosphorylation
motif and a transmembrane domain value ≥ 2 that are expressed in feather follicles” Robustness, efficiency, scale, data materialization issues Automating and integrating more complex analysis processes
(using existing software!) Estimating physical location of genes by synteny
Integrate new data sources Microarray and other gene expression data And thus, more analyses: QTL mapping, metabolic pathway learning
New off-site organism databases and analysis agents
http://www.cis.udel.edu/~decaf/http://www.cis.udel.edu/~decaf/ http://udgenome.ags.udel.edu/http://udgenome.ags.udel.edu/
