Award Abstract # 1750183
EAGER: Modular design of multiscale models, with an application to the innate immune response to fungal respiratory pathogens

NSF Org: CBET
Division of Chemical, Bioengineering, Environmental, and Transport Systems
Recipient: UNIVERSITY OF CONNECTICUT HEALTH CENTER
Initial Amendment Date: April 24, 2018
Latest Amendment Date: April 24, 2018
Award Number: 1750183
Award Instrument: Standard Grant
Program Manager: Aleksandr Simonian
asimonia@nsf.gov
 (703)292-2191
CBET
 Division of Chemical, Bioengineering, Environmental, and Transport Systems
ENG
 Directorate for Engineering
Start Date: May 1, 2018
End Date: April 30, 2019 (Estimated)
Total Intended Award Amount: $90,000.00
Total Awarded Amount to Date: $90,000.00
Funds Obligated to Date: FY 2018 = $90,000.00
History of Investigator:
  • Reinhard Laubenbacher (Principal Investigator)
    laubenbacher@uchc.edu
Recipient Sponsored Research Office: University of Connecticut Health Center
263 FARMINGTON AVE
FARMINGTON
CT  US  06030-0001
(860)679-4040
Sponsor Congressional District: 05
Primary Place of Performance: University of Connecticut Health Center
263 Farmington Avenue
Farmington
CT  US  06030-0001
Primary Place of Performance
Congressional District:
05
Unique Entity Identifier (UEI): H6D6JMXJXDE6
Parent UEI:
NSF Program(s): Engineering of Biomed Systems,
Software Institutes
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 026Z, 7916, 8004, 8005
Program Element Code(s): 534500, 800400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

The wealth of biomedical data available today -- often with wide ranging size and time scales -- allows for the validation and calibration of complex computational models that integrate across levels from molecules to whole organisms. For those models to be usable by scientists and clinicians who are experts in the biomedical phenomenon being modeled, it is important that the model features and activity be presented visually to make them understandable. This project seeks to develop a novel, modular computational approach to this challenge. It will use modeling of the immune response to an important respiratory fungal infection as a test bed to demonstrate the feasibility and effectiveness of this approach. This project will advance knowledge in computational modeling of biomedical systems and in the pathophysiology of infection. This particular infection has become increasingly relevant as it occurs most frequently in immuno-compromised individuals, including cancer and transplant patients. The developed model will examine how an individual's immune system interacts with the fungal spores to better understand the progression of this infection. The eventual goal would be to take advantage of this improved understanding to spur the development of new treatments for this fungal infection.

This complexity of multiscale models of biomedical processes poses multiple challenges related to mathematical modeling, software design, validation, reproducibility, and extensibility. The computational goal of this project is to develop a novel modular approach to model architecture, using a recently introduced technology of lightweight virtual machines and a user-friendly open-source platform for the construction and linking of these so-called "Docker containers" to create complex modular models in a transparent fashion. A key benefit of software containers is that they can encompass the entire computational environment of a model, enabling unprecedented reproducibility of computational results. For this project, this computational modeling will be focused on the development of a multiscale model capturing the early stages of invasive aspergillosis. Invasive aspergillosis is one of the most common fungal infections in immunocompromised hosts and carries a poor prognosis. The spores of the causative organism, Aspergillus fumigatus, are ubiquitously distributed in the environment. Healthy hosts clear the inhaled spores without developing disease, but individuals with impaired immunity are susceptible to a life-threatening respiratory infection that can then disseminate to other organs. The increasing use of immunosuppressive therapies in transplantation and cancer has dramatically increased suffering and death from this infection, and this trend is expected to continue. The biomedical focus of the proposed project is the battle over iron between the fungus and the host. The overarching biomedical goal is to develop a simulation tool to explore the role of iron in invasive aspergillosis across biochemical and biophysical conditions.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

Due to the increased availability of data, mathematical modeling of disease processes becomes increasingly possible and important. Many diseases involve features of the human body that span different scales, from molecular mechanisms within individual cells, such as in cancer, to effects at the whole-organism level, such as changes leading to hypertension. And there are many feedback loops between them. All of these need to be captured in appropriate mathematical models that have components at the different scales. This represents major mathematical as well as software design challenges. In particular, models need to be designed so that they can be easily changed, as we improve our understanding of human biology, and that can be easily extended as we include more parts of human biology.

The outcome of this project is the development of a novel software design that enables the transparent design of complex mathematical models, which can be modified over time, whose results can be easily reproduced. Achievement this goal requires the development of novel mathematical approaches to the construction of such computational models, and novel software design methods. This was accomplished in the project.

The biological focus of the project is the immune response to lung infection with fungal pathogens, in particular Aspergillus fumigatus. This infection is common and deadly in immunocompromised patients, such as chemotherapy patients or recipients of organ transplants. Increasingly, the pathogen is becoming resistant to commonly used antifungal drugs, which creates an important need for the development of new therapeutic approaches. The overarching goal of this project is the development of a simulation laboratory for clinicians for this purpose.


Last Modified: 07/02/2019
Modified by: Reinhard Laubenbacher

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page