1. 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
Systems Biology at PNNL:
11/20/201811/20/2018

2. DOE’s Genomics:GTL program
Follow-up to the Human Genome Project. (DOE launched the HGP in 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.)

3. 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

4. Methods used to provide various data for inferring regulatory networks (I)
Prediction of transcription factor (TF) binding sites – e.g., Dr. Lee Ann McCue’s 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.

5. 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.

6. 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.

7. 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.

8. SEBINI consists of a suite of programs operating on a
centralized relational database. The user interface is web -
based, operated by Java servlets.
A collection of inference algorithms is provided (mutual
information, Bayesian network structure learning,
etc), and
an API has been created to
allow addition of other
inference methods in a well
defined manner.
Briefly,
methods using high
throughput data rely on searc h
ing for
patterns of partial correlation or conditional probabilities
that indicate causal i n
flu
ence. Such patterns of partial
correlations found in the high
throughput data, possibly
combined with other suppl e
mental data on the genes in the
proposed networks or other information on the organism,
are the basis upon which the alg o
rithms in SEBINI’s to
olkit
infer regulatory networks.

9. Direct comparison of network inference methods on common data sets.
Thus, SEBINI allows
Direct comparison of network inference methods on common data
sets.
Artificial data sets (topologies, perturbations, node input functions)
that can be dynamically altered and stored.
Inference results that can be stored, further analyse
d, and visually
displayed.
Dynamic, step -
wise refinement of inference methods, based on
results.
Well
defined addition of new inference algorithms through an API.
Supervised or unsupervised training of inference methods, with
supervised inference results s
cored against the known network
topologies.
Analysis of experimental data within the same framework.
Storage of supplemental information, allowing such information to
be made available to an inference method (e.g., which of the
transcription factors being
produced will bind to which pro
moter
sites,
etc.) SEBINI can
thus show the quantitative effect of
background information –
for example, knowledge of M% of the
promoter sites increases the number of regulatory edges that can be
deduced by N%.

10. Software Environment for BIological Network Inference (SEBINI)
PNNL’s Bioinformatics
Resource Manager (BRM)
Input Module
High-throughput
experimental data
Builder Module
Simulated high-throughput
expression data for
artificial networks
Text files
(flat files)
PNNL’s PRISM
database system
Visualization of inferred networks
via Cytoscape
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.

11. 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.

12. 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

13. 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.

14. 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:
public demo site:

15. 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?

16. 11/20/201811/20/2018

22. 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

23. 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.

24. 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.

25. DOE Science Undergraduate Laboratory Internship (SULI) program
DOE Science Undergraduate Lab Internship (SULI) program. Year-round. Duration: 10 wks summer, wks in fall or spring. $400/week + housing;
Manager: Karen Wieda – (509)
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. $ /week, 5 months max.
Manager: Rebecca Janosky – (509)
About applications/yr, of which 250 get an offer. Completed applications posted to central site. Mentor/host pays the cost, plus 18% overhead.

26. Take-home SULI information
Apply by early January 2007 for summer PNNL sees students who selected it as first choice on Feb 1.
SULI manager: Ms. Karen Wieda. Phone: (509)
Ronald Taylor (for the SEBINI project):
PNNL science education web site: students/
Other background web sites: genomicsgtl.energy.gov/compbio/

27. 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