Ronald Taylor, Ph.D.

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The Network Inference Problem and the SEBINI
Platform

• Ronald Taylor, Ph.D.
• Computational Biology Bioinformatics Group
• Computational Sciences Mathematics Division
• Pacific Northwest National Laboratory (PNNL)
• Richland, Washington
• Email ronald.taylor_at_.pnl.gov
• Systems Biology at PNNL http//www.sysbio.org/

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DOEs GenomicsGTL program

• Follow-up to the Human Genome Project. (DOE
  launched the HGP in 1986. GenBank started at a
  DOE lab.)
• Goal to comprehensively understand cellular
  processes in a realistic context, i.e., systems
  biology. To be accomplished using high-throughput
  advanced technologies and computation (petabyte
  scale databases, integrated knowledgebases,
  network modeling)
• Under the direction of the DOE Office of Science
  and its suboffices, the Office of Biological and
  Environmental Research (OBER) and the Office of
  Advanced Scientific Computing Research (OASCR).
• Focused on microbial organisms. (Genomes
  sequenced in the DOE Microbial Genome Program.)

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DOE Genomics GTL Program Goals - The Role of
Living Systems in Energy Production,
Environmental Remediation, and Carbon Cycling and
Sequestration

• Molecular Proteins and multicomponent molecular
  machines that perform most of the cell's work
• Cellular Gene regulatory networks and pathways
  that control cellular processes
• Community Microbial communities in which groups
  of cells carry out complex processes in nature

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Methods used to provide various data for
inferring regulatory networks (I)

• Prediction of transcription factor (TF) binding
  sites e.g., Dr. Lee Ann McCues work at PNNL,
  also MotifMogul software at ISB.
• Public and commercial databases (for example
  TRANSFAC, SCPD), gradually collecting wet lab
  experiments that identify TFs and their binding
  sites, one by one.
• Tiling arrays (expensive).
• Projects to find protein-protein interactions
  e.g., PNNL/ORNL GTL project (expensive).
• Computational algorithms that infer regulatory
  edges based (primarily) on correlations in state
  the algorithms used in SEBINI. Require a large
  amount of array or protein expression data. More
  powerful algorithms/models are needed to infer
  specific gene-to-gene connections than the
  typical statistical techniques used for
  clustering.

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Methods used to provide various data for
inferring regulatory networks (II)

• There are drawbacks to all methods. TFBS
  prediction based on sequence and phylogenetic
  comparison is very hard. Tiling arrays are
  promising, but new (expensive - test one putative
  source TF at a time). Drawbacks to both
  dependence on nearness of TFBS to gene to infer
  target. In eukaryotes, 70 of TFs bind far from
  their targets (A. Aderem). Also neither yields
  regulation type (activator / inhibitor), just
  that there is binding. Also it is common in
  bacteria for a TF to lie between genes
  transcribed in different directions. May regulate
  one or both - which choice is unknown from tiling
  and TFBS prediction.
• As for determining interaction networks using
  mass spec not yet high-throughput, in terms of
  results.

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Conclusion

• Computational algorithms that are based on
  correlations in state will be continue to be
  used, remaining a standard approach for many
  years to come.
• Bonus gathering the large amount of array data
  required for their use provides the raw data for
  investigation of state functions topic for
  future research.

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Software Environment for BIological Network
Inference (SEBINI) - Introduction
SEBINI has been created to provide an interactive
environment for the evaluation and deployment of
algorithms used in the reconstruction of the
structure of biological regulatory networks.
SEBINI compares and trains network inference
methods on artificial networks and simulated gene
expression perturbation data. It also allows the
analysis within the same framework of
experimental high-throughput expression data
using the suite of (trained) inference methods.
Hence SEBINI should be useful both to software
developers wishing to evaluate, compare, refine,
or combine inference techniques, and to
bioinformaticians (or biologists) analyzing
experimental data. SEBINI provides a platform
that aids in more accurate reconstruction of
regulatory and interaction networks, with much
less effort, in less time.
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Software Environment for BIological Network
Inference (SEBINI)
PNNLs Bioinformatics Resource Manager (BRM)
Input Module High-throughput experimental data
Builder Module Simulated high-throughput expressio
n data for artificial networks
Text files (flat files)
Visualization of inferred networks via Cytoscape
PNNLs PRISM database system
Human-readable reports on inferred networks
SEBINI Central relational database (PostgreSQL)
User interface web site operated by Java
servlets
Machine-readable network structure files for
dynamic modeling programs
Topological statistics, network annotation,
post-inference processing scoring error
analysis (on artificial data sets)
Collection of network inference algorithms. User
selects algorithm and data set, runs alg to infer
a network (a set of edges). Mutual
information-based and Bayesian network structure
learning algorithms provided for learning
regulatory networks. Also PNNL/ORNL algorithm
for learning protein-protein interaction networks
from PNNL/ORNL bait-prey experiment mass spec
data sets. Inferred networks permanently stored
back into database.
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SEBINI architecture implementation (I)

• 100 Java programs (classes) and growing
  rapidly.
• All inter-servlet communication is routed through
  a CentralControl class. Algorithm handlers are
  called directly from the Java servlets for the
  corresponding web pages. This environment is is
  NOT a spiderweb there is a control chokepoint.
• 30 PostgreSQL database tables. Slowly growing
  at present quite stable. One major database
  change coming.
• Data security project based. Upon login, the
  user is assigned a 32 digit hex digit JSessionID,
  which is checked before display of every web
  page.

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SEBINI architecture implementation (II)

• While the SEBINI was originally designed to
  infer directed (regulatory) networks, the code
  now allows undirected networks, so algorithms
  that infer interaction networks can be used and
  such networks (e.g., protein-protein interaction
  networks) permanently stored and analyzed.
• Design issues interface for user navigation
  among huge data sets, database design to map
  inferred networks and inferred edges back to
  original network and expression data IDs must
  be carried forward.
• expression data ? one-to-many via binning alg
  choice ? binned exp data ? one-to-many via
  inference alg choice ? inferred_network

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SEBINI architecture implementation (III)

• Design issues (continued) multi-threaded job
  monitoring web pages to view all data points,
  working towards transparency of all data in all
  tables (security access permitting) in the
  database via display through the web site.
• Permanent storage of binned/ pre-processed data
  sets. Novel. Important for efficiency,
  transparency, speed of response, analysis of
  results.
• Jobs times recorded to millisec. Algorithms can
  be compared on efficiency vs relative power.
• A Java handler class is created for each new
  algorithm, to wrap it, ie, to handle
  communication with the database and web site.

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SEBINI architecture implementation (IV)

• SEBINI was initially implemented on a Dell
  desktop running Red Hat Linux, using Java ver.
  1.4, PostgreSQL ver. 7.4, and Tomcat 4.1.
  Cytoscape is used for network visualization,
  invoked through Java Web Start.
• SEBINI has also been installed on a Windows web
  server that will soon be accessible from outside
  the PNNL, using Java ver. 1.5, Tomcat ver. 5, and
  PostgreSQL 8.1. Jakarta Commons Java libraries
  are used for data file uploads Jakarta.
• Machine-specific parameters are stored in an
  easily changed properties text file.
• development site http//asimov.emsl.pnl.gov8080/
  NIT/NIT.html
• public demo site https//www.emsl.pnl.gov/NIT/NIT
  .html

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Possible sources of inference algorithms

• Probabilistic graphical models (structure
  learning Bayesian networks, among others)
• Information theory (mutual information based, and
  CMI)
• Classical statistics, analysis of correlation -
  e.g., Pearson correlation
• Machine learning decision trees (C4.5, ID3),
  supervised and unsupervised
• Data mining association rule mining (really
  want to try this)
• Pattern classification
• Deductive reasoning
• Neural networks
• Fuzzy logic?

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SEBINI flow of control, for experimental data

• Log into a project, or create a new project
• Create a network set (container) for the one
  experimental network
• Upload the experimental expression data file
• Select a binning algorithm and bin the data
• Select an inference algorithm, select the alg
  parameters, and infer a network.
• Visualize the inferred network, now in the
  database, using Cytoscape
• View topological statistics
• View node and edge annotations
• Generate a human-readable report
• Export the topology in a format suitable for
  input into dynamic simulations

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SEBINI flow of control, for synthetic data

• Log into a project, or create a new project
• Select a topology build algorithm, enter param
  values, and create a synthetic network set
• Select an expression set build algorithm, enter
  param values, and create synthetic expression
  sets
• Select a binning algorithm and bin the data
• Select an inference algorithm, enter param
  values, and infer network
• Visualize and compare the real and inferred
  network(s), using Cytoscape
• View topological statistics
• View precision, recall, F-measure statistics for
  precise measure of how well the inference alg
  performed against the gold standard, the known
  synthetic network.

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Some goals for SEBINI

• Make network inference a starting point, not an
  end point (currently an end point that is usually
  not even reached) Simple deployment of
  state-of-the-art algs not previously available to
  a biology lab, available over the web or via
  local SEBINI install.
• Advance the field by improving the algorithms.
  Possibility of combining alg output (as done in
  GRAIL), now that alg results are stored in same
  database. Develop expertise on how much data is
  needed, appropriate cutoffs, species-specific
  post-processing, the weaknesses of a given
  method, what background information on a genome
  is most useful to supplement the primary
  expression data.
• Network biology is only in its infancy (2004,
  Barabasi). Nobody knows what inference
  algorithm(s) will perform best - theoretical
  guidance is lacking. But SEBINI will position us
  to empirically test new algorithms, easily modify
  or combine algs - possibly with species specific
  information.

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DOE Science Undergraduate Laboratory Internship
(SULI) program

• DOE Science Undergraduate Lab Internship (SULI)
  program. Year-round. Duration 10 wks summer,
  12-16 wks in fall or spring. 400/week housing
  http//science-ed.pnl.gov/undergrad/erulf.stm.
• Manager Karen Wieda kj.wieda_at_pnl.gov, (509)
  375-3811.
• Of 200 applications specifying PNNL as 1st round
  choice, 50 offers made last year. Summer term is
  the most competitive. DOE pays most of cost.
• PNNL Science Engineering Education - Fellowship
  Services. Undergrad, grad, visiting scientists,
  sabbaticals. 400-750/week, 5 months max.
  http//science-ed.pnl.gov/studentops.stm
• Manager Rebecca Janosky rebecca.janosky_at_pnl.gov
  , [phone].
• About 900-1000 applications/yr, of which 250 get
  an offer. Completed applications posted to
  central site. Mentor/host pays the cost, plus 18
  overhead.

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Take-home SULI information

• Apply by early January 2007 for summer 2007. PNNL
  sees students who selected it as first choice on
  Feb 1.
• SULI manager Ms. Karen Wieda. karen.wieda_at_pnl.gov
  Phone [phone]
• Ronald Taylor (for the SEBINI project)
  ronald.taylor_at_pnl.gov
• PNNL science education web site
  http//science-ed.pnl.gov/ students/
• Other background web sites www.pnl.gov,
  www.sysbio.org, genomicsgtl.energy.gov/compbio/

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Acknowledgements

• For work on SEBINI Anuj Shah for work on
  Cytoscape, MI alg translation, statistics),
  Meridith Blevins (SULI), Charles Treatman (SULI)
• Funding from the US Dept of Energy, through the
  PNNL Biomolecular Systems Initiative, the EMSL
  Membrane Grand Challenge at PNNL, and the joint
  PNNL/ORNL protein-protein interaction network
  mapping GTL project