Kyle R. Klicker

Supporting Computational Visual Theories in Biology

At the Pacific Northwest National Laboratory, the visual modeling environment for biology (VMEB) is being developed to allow biologists to construct visual representations of scientific concepts and theories. Unlike existing scientific visual modeling environments, VMEB captures and manages visual concepts and diagrams in a computational form that may be transferred across sessions, shared among collaborators, linked to external data sources, and searched against to identify relevant or matching information

A symbolic logic strategy for mapping biological concepts in the HERBE prototype

More powerful database approaches are needed to support biologists in the efficient use of complex, large scale, and rapidly changing systems biology data. We are engaged in research to advance database strategies for capturing, managing, computing, and searching biological concepts in ways that are presently not possible. The Heuristic Entity Relationship Building Environment (HERBE) project is developing mechanisms to automate the translation of concepts into a computable form. HERBE features an open visual environment to collect diverse, complex data and, construct models graphically allowing users to apply their own symbolic logic. As data are entered, HERBE captures the data, then integrates and heuristically interprets underlying concepts into a computable form using a multitiered data management infrastructure.

PQuad—a visual analysis platform for proteomic data exploration of microbial organisms

UNLABELLED The visual Platform for Proteomics Peptide and Protein data exploration (PQuad) is a multi-resolution environment that visually integrates genomic and proteomic data for prokaryotic systems, overlays categorical annotation and compares differential expression experiments. PQuad requires Java 1.5 and has been tested to run across different operating systems. AVAILABILITY http://ncrr.pnl.gov/software.

Problem solving environment approach to integrating diverse biological data sources

Scientists face an ever-increasing challenge in investigating biological systems with high throughput experimental methods such as mass spectrometry and gene arrays because of the scale and complexity of the data and the need to integrate results broadly with heterogeneous other types of information. Many analyses require merging the experimental results with datasets returned from public databases, such as those hosted by the National Center for Biotechnology (NCBI), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein interaction databases such as the Biomolecular Interaction Network Database (BIND). Because data sources such as these are constantly evolving the researcher is faced with hurdles to manually gather, integrate and manage the data into cohesive datasets. To overcome these technical problems, we have been building a three-tier software system that includes a client-side graphical user interface for rich interaction with the data, an application server that hides the messy technical details of data collection, integration, and management tasks from the researcher, and a flexible database schema that efficiently manages mixed data source content. The software is being developed using Java for portability and Open Source technology so that it can one day be freely distributed. This problem-solving environment is called the Computational Cell Environment (CCE) and is designed to provide scalable and agile connectivity to diverse data stores and eventually provide data retrieval, management, and analysis through all aspects of biological study.

Enabling Systems Biology: A Scientific Problem-Solving Environment

Biologists today are striving to solve multidisciplinary, complex systems biology questions. To successfully address these questions, software tools must be created to allow scientists to capture data and information, to share this information, and to analyze the data as elements of a complete system. At Pacific Northwest National Laboratory, we are creating the Computational Cell Environment, a biology-centered collaborative problem-solving environment with the goal of providing data retrieval, management, and analysis through all aspects of biological study. A horizontal prototype called SysBioPSE, demonstrates this vision. Our initial work is centered on developing the Distributed Data Management and Analysis subsystem, which is a specific tool for retrieving data from multiple heterogeneous data stores, providing storage facilities that support pedigree tracking and data and information analysis under a common user interface. With time, many such individual subsystems will be developed and integrated to fulfill the Computational Cell Environment vision.

High-throughput visual analytics biological sciences: turning data into knowledge

For the SC|06 analytics challenge, we demonstrate an end-to-end solution for processing data produced by high-throughput mass spectrometry (MS)-based proteomics so biological hypotheses can be explored. This approach is based on a tool called the Bioinformatics Resource Manager (BRM) which will interact with high-performance architecture and experimental data sources to provide high-throughput analytics to a specific experimental dataset. Peptide identification was achieved by a high-performance code, Polygraph, which has been shown to scale well beyond 1000 processors. Visual analytics applications such as PQuad, Cytoscape, or others may be used to visualize protein identities in the context of pathways using data from public repositories such as Kyoto Encyclopedia of Genes and Genomes (KEGG). The end result was that a user can go from experimental spectra to pathway data in a single workflow reducing time-to-solution for analyzing biological data from weeks to minutes.

Bioinformatics Approach for Exploring MS/MS Proteomics Data

The use of computer tools and technologies is unavoidable when it comes to conducting mass spectrometry (MS) research at any significant level. This is mainly due to the large volume of MS data and the processing rates required. Most of the existing tools focus on one particular task: be it storing and maintaining the data or visualizing the dataset to draw inferences from the data. But for the researcher the problem of manually retrieving a large dataset from a datasource and customizing it for a particular visualization application is a daunting task in itself. This paper describes the Computational Cell Environment (CCE) which is a problem-solving environment for systems biology that provides uniform and integrated access to distributed, heterogeneous biological data sources and analysis applications, through a multi-tiered architecture. This paper also illustrates the necessity for such a tool by discussing its usage in proteomics research being performed on the diseased and normal states of Deinococcus Radiodurans along with corresponding curated data collected from community resources.

Exploring genomic context patterns for Rhodobacter sphaeroides in the HERBE knowledge discovery environment

Sophisticated information strategies are increasingly essential for biologists. We have built a powerful knowledge discovery resource using novel genomic context methods, interactive visualization strategies, and computational environment technologies. The Heuristic Entity Relationship Building Environment (HERBE) is a research platform for advanced database technologies that fuses data management solutions with knowledge management components to support the dynamic capture of concepts and observations as biologists explore large-scale data. The Similarity Box visualization software supports the ability of biologists to interact with large-scale computational results and evaluate relationships based on natural reasoning processes. We have applied these knowledge methods in the exploration of complex microbial genome relationships. We extracted a complete set of gene neighbor patterns for Rhodobacter sphaeroides using HERBE to map data structures for chromosomal contiguity against sequence similarity. We then organized these gene neighbor patterns by their phylogenetic profiles using Similarity Box to enable biologists to explore the results.