CMCI Pilot Project: Multi-scale model of interactions between lung and pulmonary ventilation
Project Team: Tao Xing (PI), Gordon Murdoch (Co-PI), Michelle Wiest (Advisor), Loel Fenwick (Collaborator), Rabijit Dutta (Post Doc)
Without adequate respiration, life ceases in as little as three minutes. The failure of effective spontaneous respiration requires immediate intervention to preserve life. However, lungs are far more complicated than simple bags at the ends of tubes. Life support or therapeutic treatment of injured or diseased lungs is frequently needed. However, the current understanding of the most effective reliable and safe pulmonary ventilation methodology is sorely lacking, for either conventional mechanical ventilator (closed-circuit) or the more recent flow ventilation (open-circuit) by Percussionaire Corp. The goal of this project is to develop and validate a multi-scale model for understanding and optimizing the interaction between lungs and pulmonary ventilations, including the key mechanisms impacting the effectiveness of pulmonary ventilation at the organ, tissue, cellular, and molecular scales.
Additionally, the incorporation and integration of hardware can reliably yield empirical experimental data throughout the conducting components of the respiratory anatomy (buccal cavity, trachea, bronchi, bronchioles) as well as the respiratory anatomy responsible for effective gas exchange (inferior bronchioles and alveoli). The model, once validated using the experimental data, will provide a reliable simulation based design toolbox to evaluate comparative profiles of various methods of rescue/supplemental ventilation, which are critical for extrapolating the efficacy of biomedical instrumentation that are designed to reduce mortality associated with respiratory diseases and/or damaged or physiologically compromised lungs. It will provide a virtual three-dimensional laboratory for in silico study of various lung diseases that facilitates a personalized flow ventilation technologies. It will fundamentally change the way current researchers design respiration devices such that simulation-based modeling is applied toward the next generation expert system for the optimization of pulmonary ventilation.
If successful, the proposed innovation would contribute to the saving of lives of those with acute respiratory distress and extend and improve the lives of those with chronic pulmonary conditions. Moreover, it will generate scientific data that can be utilized to provide patient and condition specific therapy. Tackling Critical Issues in the Ebola Epidemic through Modeling Viral Evolution. Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number P20GM104420. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Determining the regulation of chlamydial development through experimental modeling
Project team: Scott Grieshaber (PI), Nicole Grieshaber, Anders Omsland, Christopher Remien (Modeler)
Chlamydia are obligate intracellular bacteria that cause a range of diseases in both man an animals. Chlamydia like bacteria infect just about all eukaryotic organisms on the planet, from man to amoeba. All Chlamydiae, including the human pathogens C. trachomatis, C. pneumoniae, and C. psittaci, share a conserved developmental cycle. Chlamydia infect cells with a specialized infectious non metabolic cell form and, once inside the cell, differentiate into the replicative but non infectious form. At some point late in the infection the replicative forms differentiate back into the infectious form. The molecular controls for this are not yet known. We would like to use mathematical methods to model the most common hypotheses on what controls differentiation. We envision that we can test predicted outcomes of experimental changes based on these models, and will be able to focus on the molecular mechanisms underlying the best fitting model. We plan on submitting an NIH RO1 proposal to investigate gene control during differentiation in 2016. Including a mathematical modeling component would significantly strengthen the proposal as well as help to focus our efforts. Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number P20GM104420. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
CMCI Pilot Project: Modeling variability in persistence induced from within by a toxic metabolite
Project Team: Christopher Marx (PI), Andreas Vasdekis (Co-PI), Chris Remien (Collaborator), Siavash Riazi (Graduate Student), Denis Liyu (Undergraduate Student)
Multidrug antibiotic persistence, which allows some cells that lack genetic resistance to survive antibiotic stresses by becoming dormant, is a major public health concern. This exploratory project will use data from state-of-the-art image cytometry and single-cell analysis in combination with mechanistic mathematical modeling to study the formaldehyde-sensing network that was recently discovered in Methylobacterium by the Marx Lab. The formaldehyde-sensing network in Methylobacterium shares many characteristics with antibiotic persistence, but has the advantage of allowing us to externally manipulate factors governing the transition from growth to stasis, and all the cells in a population go dormant. The research team wishes to develop mathematical models in combination with relevant experimentation 1) to study the ability the biochemical network to allow for distinct cell fate outcomes as a function of key parameters such as protein levels of EfgA 2) to analyze how stochasticity in the form of spontaneous fluctuations in protein levels, which can lead to a potentially toxic pulse of formaldehyde, influences cell transitions between phenotypes such as growth, death, or persistence. This pilot grant will position the researchers to explore fundamental processes associated with antimicrobial resistance, which if eventually manipulated could prevent disease and promote health. Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number P20GM104420. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
CMCI Project: Tackling critical issues in the ebola epidemic through Mmodeling Viral evolution
Project Team: Marty Ytreberg (PI), Celeste Brown (Co-PI), Craig Miller (Co-PI), Holly Wichman (Collaborator), Tanya Miura (Collaborator), Chris Mirabzadeh (Graduate Student), Kyle Martin (Graduate Student)
Ebola surface glycoprotein (blue) bound by antibodies (brown). In dark blue are other known epitopes (antibody binding sites). In orange and red are mutations from the recent epidemic in West Africa.
The goal of this project, which was funded by DEB-1521049 from the National Science Foundation, was to determine the functional implications of ongoing and future Ebola protein evolution. A combination of statistical and molecular modeling techniques were used to determine how evolution modifies the properties of the Ebola glycoprotein and its ability to bind antibodies. The project provided health agencies with a “watch list” of potential mutants that could hamper vaccination efforts and lower population immunity to Ebola. The project also provided a new perspective on the evolutionary history of EBOV, the causative agent of the recent outbreak of Ebola virus disease in several countries in western Africa.
Projects Published Papers
Miller CR, Johnson EL, Burke AZ, Martin KP, Miura TA, Wichman HA, Brown CJ, Ytreberg FM (2015) Initiating a watch list for Ebola virus antibody escape mutations. PeerJ 4:e1674.
Brown CJ, Quates CJ, Mirabzadeh CA, Miller CR, Wichman HA, Miura TA, Ytreberg FM, New perspectives on Ebola virus evolution, PLoS One, 11:e0160410 (2016)
Publications listed here were supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number P20GM104420. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.