Home

Center Info
  Introduction
  Leadership
  Members
  Resources
  Projects
  Events
  Readings
  Feedback

We can only afford to select a few of the many high-impact projects among center leaders and founding members to show what type of research is "systems biology" research and development for future personalized medicine. All the projects listed below are currently ongoing projects, which involve at least two center investigators and two techniques. Collectively, they define future research focus of the center. With a permanent center as a structure, we believe more multi-year multi-centered collaborative program grants can be effectively supported.

Warfighter Cancer Care Engineering (WCCE)

To be Funded by the Department of Defense (2007- 2009) The Major Goals and Objectives of the project is to: 1) create an infrastructure for the collection, annotation, storage, and distribution of blood and tissue samples from colorectal cancer (CRC) patients in the Multiorgan Cancer Clinic at the Indiana University Cancer Center (IUCC) and blood samples from their caregivers (non-cancer controls); 2) perform “OMIC” analyses (SNP (single nucleotide polymorphisms), proteomic, lipidomic and metabolomic) on the samples; 3) create novel mathematic models to integrative the “OMIC” data to classify patients based on their predictive response to treatment; 4) create integrative mathematic models using SNP analysis and dietary records to predict CRC susceptibility; 5) utilize a novel knowledge discovery tool (BioMap) to identify interactive molecular pathways, based on both the integrated “OMICS” analyses and literature mining, that underlie the development of CRC; 6) link the Roudebush Veterans Administration Medical Center (RVAMC) electronic medical record (EMR) data system with the Indianapolis citywide clinical informatics network to optimize cancer care for Indiana Veterans; and 7) create WCCE War Room containing an interactive, integrated, visualization analytics web-based system to rapidly display data across the WCCE system to detect patterns and trends in CRC incidence, treatment and care delivery.

Clinical Proteomic Technology Assessment for Cancer (CPTAC)

Funded by the National Cancer Institute (October 2006- September 2011) Clinical Proteomics refer to the study of all the proteins in a human cell, tissue, or an individual. Because proteins are involved in almost all biological activities, including disease, the proteome is a critical target for understanding how disease arises and how to prevent it. Traditionally, proteomics experiments have been done using two-dimensional gel electrophoresis (2DGE), a process by which large mixtures of proteins are separated by electrical charge and size. As a result of the rapid emergence of mass spectrometry (MS), proteomics data are being collected at a faster pace than the ability of the researchers to validate, interpret, and integrate them with other known data. Despite many claims for the discovery of cancer-related proteins or “biomarkers,” it has proven very difficult to reproduce and validate results across either laboratories/institutions or technology platforms. To that end, a major goal of our Purdue-IU team in this program is to assess thoroughly the various MS platforms to understand their capabilities and limitations in rigorously and reproducibly identifying proteins and peptides relevant to cancer. In addition, new, more robust standard software tools are developed in all areas of data analysis, including data collection, storage, searching, analysis, classification, management, archiving, and retrieval. We will apply our knowledge of assessment outcome to the application of breast cancer biomarker discoveries.

Predictive Lung Cancer Systems Biology Towards Improved Postoperative Survivals

Funded by the IU Simon Cancer Center Lung Cancer Working Group (November 2007- October 2009) Lung cancer accounts for 25-30% of all cancer deaths in the US, primarily due to late stage of diagnostics, lack of effective treatment, and lack of understanding of lung cancer carcinogenesis. There is a comparatively superior survival of Stage I Non-small-cell lung cancer (NSLC) surgical patients, which indicates that a substantial number of patients have the potential to be treated successfully. However, the majority of lung tumors have reached locally advanced stage III (33%) or metastatic stage IV (41%) by the time of diagnosis. The overall poor survival of lung carcinoma patients, therefore, points to a continuing need for improved prevention and treatment measures. The benefits of using global gene expression and protein expression profiles to improve cancer prognostic capability and therefore to alter a clinical treatment decision was unclear until recently. High-resolution proteomics instruments, new panels of plasma markers, and powerful statistical/informatics algorithms are jointly needed to make global protein expression practical for the field. Two practical realities also constrain the computational analysis of global expression of gene/proteins, “curse of dimensionality” and “curse of dataset sparsity”. Therefore, filtering the data through statistical and biological relevance prior to specific disease studies and interpretation of data from holistic view of molecular cell functions—a grand goal of the systems biology approach, bring promises in unraveling computational complexity of problem domains. In this project, we collect plama samples from variable at-risk lung cancer patients after surgery will be collected in collaboration with Dr. Nasser Hanna, along with de-identified clinical data. Standard operation procedures of sample handling will be performed to process through a multiplexed tandem Ion Mobility Separation Mass Spectrometry (IMS-MS) platform. Data from the lung cancer proteomics will be collected as a multi-dimensional array, with each cell containing values for a protein’s identity, quantification, and confidence measure. Data/knowledge gained, including differential global protein expression profiles between different at-risk patient groups, lung cancer proteomics profile maps, lung cancer subnetwork maps, lung cancer specific annotated functional genomics databases, will be invaluable data resources to share among the lung cancer community. The postoperative solutions will point to an interesting direction for more intriguing future questions such as early lung cancer diagnosis, identification of new drug targets, and finding personalized treatment plans.

Proteomics Software Pipeline Development

Funded by the Canary Foundation (May 2007- May 2008) In this project, we perform custom development of Computational Proteomics Analysis System (CPAS), particularly, for proteomics researchers at three university core proteomics facilities throughout the state of Indiana, which include sites at: Purdue University, West Lafayette; Indiana University (IU) – Purdue University, Indianapolis, or IUPUI; and Indiana University, Bloomington. Funding for the CPAS deployment can help us launch CPAS to process mass spectrometry data generated from 6 types of mass spectrometry instruments on a centralized data processing pipeline for hundreds of proteomics researchers and users on the three campuses throughout the state of Indiana. Our project is partially supported and coordinated through existing NCI funded Clinical Proteomics Technology Assessment Consortium (CPTAC, http://proteomics.nci.gov/) program (2006-11). Leveraging on our existing proteomics pipeline development expertise, we plan to focus the development resource in the initial 6 months on local CPAS configuration, customization, testing, and incorporation of experimental meta-data standards. We also plan to add to the current CPAS community our unique high-performance computing capabilities by taking advantage of IU-Purdue Wide Area Network 10Gb campus-interconnections, IU massive data storage capabilities, National Science Foundation TeraGrid, and popular proteomics data management systems such as Tranche, to expand the future outreach of CPAS beyond central Indiana. If successful, we will collaborate with the original CPAS team to seek significant additional funding to further develop the software pipeline for its widespread adoption within the MS-proteomics based biomarker discovery user community.

Human Disease Bibliome Mining

Funded by IUPUI International Development Fund (July 2006- August 2007) A grand challenge in biomedical research is how to keep up-to-date with the vast amount of knowledge in scientific publications. The opportunity for extracting novel knowledge from public large data sets and scientific literature has become huge. The purpose of this project is to initiate collaboration with the brightest computer scientists at Tsinghua University in China and to perform translational bioinformatics research studies. The long-term goal of the team is to develop an externally funded program to text mine the “Human Disease Bibliome” supporting translational biomedical scientific research. The short-term (14 months) specific aim of the team is to 1) integrate text mining and systems biology research software systems from separate labs within the team; 2) develop at least 3 cancer-related molecular pathways based on latest knowledge mined from both biological literature and cancer biology databases; and 3) Evaluate whether our new cancer pathway biology knowledge is effective in helping new hypothesis formulation.

Outcome: Department Associate Chair visited IUPUI in November 2006. She won a multi-million RMB research grant as a result of this international collaboration. She sent an exchange student from the computer science Department of Tshinghua University to visit Center for Systems Biology and Personalized Medicine (co-located with Dr. Chen's lab) and continue the collaboration. The student was an "all-star" , who won consecutive years of "women in computing" international awards and the prestigious HP computing awards in China. Several papers were under review.

Custom Development of an Integrative Proteomics Platform Suitable For Collaborative Efforts in Systems Biology

Funded by IUPUI Roadmap Fund (August 2005- August 2007) Large scale systems biology analyses have emerged in recent years as a way to understand global cellular changes in response to mutational or conditional perturbations. These routinely take the form of microarray analysis which examines changes in mRNA abundance, proteomic analysis which examines protein abundance, and, specifically in the yeast model, genomic phenotyping which directly examines the need for specific genes under defined growth conditions. Software exists that allows one to study the structure and dynamics of biochemical pathways and define standard formats of data exchange between proteomics experiments. However, no software is available for integrative and comparative analysis of proteomic data, including that for protein abundance and protein modification or that for merging of findings across mRNA, protein, and phenotypic data sets. In this project, we developed a proteomics data analysis software platform, based on custom extensions of open-source proteomics software framework. We included existing data sets examining gentamicin toxicity in yeast for which mRNA, protein and phenotypic information have all been collected, and data sets recording protein expression pattern changes caused by the inflammatory cytokines TNFα and IL-1 in mammalian cells. The developed software allowed for cluster and network analysis of proteins and genes. Using this software, we were able to process existing un-organized proteomics data from biochemistry and molecular biology laboratories into the software platform, merge proteomics experimental results from varying experimental and computational conditions, connect to public protein annotation databases including Gene Ontology (GO) and Gene Expression Ominibus (GEO), and make cell stress related systems biology discoveries. These capabilities are essential to creating new hypotheses in systems biology studies.

Outcome: 8 research papers were published with 3 more manuscripts under preparation. A key patent was filed by the PI as an offshoot of this program. 5 graduate students from Informatics, Computer Science, and Biochemistry graduate programs at IUPUI, either at MS or PhD levels, were significantly and positively impacted for their future career choice and were all successfully employed by large companies upon graduation or on their ways onto Academic positions. Successful program funding from the National Cancer Institute was obtained as a result of this collaboration.