“ENISI is unique because it’s specific to the gut, simulating each individual cell rather than creating broad mathematical models,” said researcher Ms. Kate Wendelsdorf.  “Thus, it’s more faithful to a living system and allows us to simulate a million individual cells, more than any other simulator. It’s a powerful tool for understanding interactions between gut pathogens and the mucosal immune system.”

The ENISI software allows researchers to model the immune responses that occur when a pathogen invades the GI tract. Researchers can manipulate cells and immune processes in ENISI to determine if, for example, blocking a specific immune pathway or adding a drug can inhibit pathogen invasion and infection. The computer-generated models created in ENISI can, in turn, help researchers design better experiments to test the simulations in laboratory settings or in live animals. Therefore, it may be possible to test the efficacy of a novel vaccine or immune therapeutic in an ENISI model of disease, confirm the results in an animal model, and then use those results to explore the mechanisms of therapeutic efficacy in additional studies.

ENISI was initially designed to model inflammatory bowel disease (IBD) and the recent upgrade allows investigators to simulate immune responses in a mouse infected with Helicobacter pylori. The MIEP team has plans to expand the ENISI software to simulate infection with enteroaggregative Escherichia coli and other enteric pathogens such as Clostridium difficile and Cryptosporidium parvum.  Future ENISI upgrades will allow users to run simulations via the MIEP website and will eventually be able to visualize in silico cells or lesions forming in real time, rather than only seeing the outcomes of such interactions.

This feature will help immunologists and infectious disease experts immensely in understanding pathology, diagnosis, and treatment.

“ENISI is based on an interaction-based modeling approach that represents individual cells and their interactions with other cells, pathogens, and the environment. The algorithmic/procedural representation of individual agents and their interactions with other agents via an abstract interaction network is central to the modeling process. The use of high-performance computing facilitates scaling to 10⁶ cells; we expect this number to grow 100-fold over the next two years. Such a representation yields a fundamentally different approach to understanding novel immunological processes,” said Dr. Madhav Marathe, MIEP’s modeling lead.

“ENISI runs on high-performance computers: hundreds or thousands of servers working together to produce an answer. The MIEP program shows the power of trans-disciplinary science, bringing together a team of software developers, computer scientists, immunologists, and physicists to solve problems that they wouldn’t have been able to tackle on their own,” said Dr. Keith Bisset, a MIEP modeling expert and a key developer of the ENISI software.

A MIEP team paper entitled Enteric Immunity Simulator: A tool for in silico study of gut immunopathologies has been accepted in the IEEE Bioinformatics and Biomedicine (BIBM) International conference proceedings. Preliminary ENISI modeling results that simulate bacterial-induced colitis will be presented at the IEEE BIBM conference later this year.

“One of our goals in the MIEP program is to develop user-friendly and interactive modeling tools that engage and inform the immunology and infectious disease communities, thereby enabling paradigm-shifting scientific discovery. The release of the upgraded ENISI software by the MIEP team is a major step in allowing powerful computer simulations to uncover novel mechanisms of immunoregulation underlying immune responses to gut pathogens. The ultimate goal of such powerful simulations is to accelerate the discovery of novel drug targets and biomarkers for enteric infectious diseases. The fully integrated computational modeling, bioinformatics and immunology experimentation efforts within the MIEP program enable the generation of mechanistic evidence in silico and efficient validation in vivo,” said Dr. Josep Bassaganya-Riera, MIEP’s principal investigator and Director of the Nutritional Immunology and Molecular Medicine Laboratory.

For more information about ENISI please visit the MIEP Web Portal at www.modelingimmunity.org.

MIEP is funded by the National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health, under NIAID’s Modeling Immunity for Biodefense program.

About the Virginia Bioinformatics Institute
The Virginia Bioinformatics Institute at Virginia Tech is a premier bioinformatics, computational biology, and systems biology research facility that uses transdisciplinary approaches to science, combining information technology, biology, and medicine. These approaches are used to interpret and apply vast amounts of biological data generated from basic research to some of today’s key challenges in the biomedical, environmental, and agricultural sciences. With more than 240 highly trained multidisciplinary, international personnel, research at the institute involves collaboration in diverse disciplines such as mathematics, computer science, biology, plant pathology, biochemistry, systems biology, statistics, economics, synthetic biology, and medicine. The large amounts of data generated by this approach are analyzed and interpreted to create new knowledge that is disseminated to the world’s scientific, governmental, and wider communities.

##

Contact:
Tiffany Trent
540-231-6822
ttrent@vbi.vt.edu

The tool currently captures dynamics of four possible outcomes in response to pathogen exposure in the presence of tolerance-inducing microflora:

1. Complete tolerance that leads to ongoing pathogenic microbe persistence.
2. Hypo-inflammation in which a pathogen is not completely eliminated and persists chronically in the host.
3. Inflammation that eliminates the microbe, but it ceases prior to extensive tissue damage.
4. Hyper-inflammation in which the microbe is eliminated, but at expense of host tissue.

In its current stage, ENISI is capable of testing hypotheses to predict the net immune response to a pathogen given the complex interplay between both regulatory and inflammatory pathways. In addition, ENISI can be used to identify aspects of competing immune pathways that could likely be exploited to inhibit pathogen invasion, infection, and evolution.

Accurately predicting these outcomes in specific individual backgrounds sets the stage for modeling determinants of a successful infection (host susceptibility), the capacity of the host as an infectious carrier, and pathogen phenotype selection. These are all relevant to devising effective therapeutic approaches that intervene in microbial infection cycles and pathogenic functions. The MIEP team is planning to release the first version of the ENISI tool in the MIEP website by September of 2011.

ENISI Documentation

A full technical report on ENISI is available ENISI0.9_TR .

An archived technical report containing information on the first ENISI model is available ENISI0.8_TR.

Dr. Stefan Hoops, a Computational Systems Biologist at VBI and the Bioinformatics Lead for MIEP, noted “The CellPublisher code we started with was exceptionally well-documented, so we acclimated to it very quickly. We would like to thank the original authors for the great tool they provided us.”

The Bioinformatics Team at the Nutritional Immunology and Molecular Medicine Laboratory and MIEP, including Dr. Hoops and Dr. Yongguo Mei, a Research Software Engineer at VBI’s Nutritional Immunology and Molecular Medicine Laboratory, incorporated three major enhancements to CellPublisher:

• One improvement allows annotating reactions in addition to species, thereby allowing scientists to convey more information about the interactions in their models.
• Google map markers are only shown for objects which have been annotated in the MIEP models. It is immediately visible to the user where information is available. This behavior is similar to the road maps one sees in Google.
• Finally, the capability to display 3D protein structures through JMol has been enhanced by making it compatible with all major Web browsers on common operating systems.

For the purposes of clarity the MIEP/NIMML Bioinformatics team also made a minor change and reduced the size of annotation markers so that they do not interfere with the network information conveyed through the graphic representation of the model.

“Working with the open source community is a fun and enjoyable journey. As a member of the MIEP Bioinformatics team, I am very pleased to make contributions back to the software development community. Because the MIEP team is developing analyses, visualization and modeling tools, the benefits of these collaborative efforts are not just for programmers, but for the broader research community as well,” Dr. Mei said.

Possibly the biggest advantage of open source software is that everybody has the right to modify the source code. This means that the code can be implemented in other pieces of software and adapted to changing environments. The MIEP bioinformatics team has utilized these advantages to the benefit of the immunology and infectious disease research community, contributing our modifications back to the original source code so that other scientists have the same opportunity.

“The MIEP Bioinformatics Team has made substantial progress in the implementation of a fully integrated experimental environment for studying the mechanisms of immunoregulation underlying immune responses to enteric pathogens. The development of user-friendly and interactive network models of CD4+ T cell differentiation and mucosal immunity to Helicobacter pylori will facilitate dissemination of MIEP’s modeling efforts. In addition, MIEP’s open framework approaches to software development, including CellPublisher, facilitate efficient and well-modulated collaborations,” said Dr. Bassaganya-Riera, an Associate Professor at VBI, Principal Investigator of MIEP, and Director of the Nutritional Immunology and Molecular Medicine Laboratory.

Please visit the MIEP Web Portal at www.modelingimmunity.org.

MIEP is funded by the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, under Contract No. HHSN272201000056C to VBI.

About the Nutritional Immunology and Molecular Medicine Laboratory
The Nutritional Immunology and Molecular Medicine Laboratory (www.vbi.vt.edu/nimm) conducts translational research aimed at developing novel therapeutic and prophylactic approaches for modulating immune and inflammatory responses. The Laboratory combines computational modeling, bioinformatics approaches, immunology experimentation, and pre-clinical and clinical studies to better understand the mechanisms of immune regulation at mucosal surfaces and ultimately accelerate the development of novel treatments for inflammatory, infectious and immune-mediated diseases.

About the Virginia Bioinformatics Institute
The Virginia Bioinformatics Institute at Virginia Tech is a premier bioinformatics, computational biology, and systems biology research facility that uses transdisciplinary approaches to science, combining information technology, biology, and medicine. These approaches are used to interpret and apply vast amounts of biological data generated from basic research to some of today’s key challenges in the biomedical, environmental, and agricultural sciences. With more than 240 highly trained multidisciplinary, international personnel, research at the institute involves collaboration in diverse disciplines such as mathematics, computer science, biology, plant pathology, biochemistry, systems biology, statistics, economics, synthetic biology, and medicine. The large amounts of data generated by this approach are analyzed and interpreted to create new knowledge that is disseminated to the world’s scientific, governmental, and wider communities.

##
Contact:
Tiffany Trent
540-231-6822
ttrent@vbi.vt.edu