Biomedical problems are innately complex, and real solutions will require input from many fields. Such input is most effective if it is integrated int an interdisciplinary framework, with a convergence of approaches that truly merges expertise into a more seamless form of collaboration. The foundation of a convergence in interdisciplinary research is a strong modeling framework because modeling is the language that can cross disciplines and levels of biological organization. The Center for Modeling Complex Interactions will create the intellectual, cultural, and physical environment to foster convergence in interdisciplinary biomedical research.
The Specific Aims are: (1) Conduct model-based, interdisciplinary research that leads faculty to transition to independence. The three initial projects will focus on viral co-infection. Co-infection is common in nature and yet understudied in the laboratory. Co-infecting viruses can interact directly or indirectly, and may have nonlinear effects on viral expression and on host outcomes. Project 1 examines disease severity during co- infection in a mouse model system. Project 2 establishes an invertebrate system to examine co-infection at multiple levels of biological organization. Project 3 uses agent-based modeling to examine transmission dynamics at the population level. (2) Establish a collaborative and synergistic Modeling Core to impel interdisciplinary research and to build institutional research capacity. Mathematical modeling is central to interdisciplinary research because it plays a role in every phase of the research, from hypothesis formulation to experimental design, from data analysis to interpretation. Most importantly, it can reveal connections between data from seemingly unrelated areas and across all levels of biological organization, opening new areas of scientific inquiry. Our Modeling Core is a service core that accepts research projects and delivers models, modeling strategies, and connections between projects. (3) Expand participation in interdisciplinary biomedical research. This will be achieved by redirecting vacant and new faculty positions, providing pilot grants to engage current faculty, and outreach to the regional medical community. The plan for long-term sustainability is to first gain credibility withn the university and greater scientific community; next, build research capacity by attracting a diverse group of current and incoming faculty to engage in research in the Center; and, finally, to establish the Center as a research 'entity' within the structure of the University of Idaho.

Public Health Relevance

The Center for Modeling Complex Interactions focuses on biomedical problems that are complex and require too diverse a skill set to be tackled by lone specialists. It brings together empirical scientists and modelers to address problems across all levels of biological organization, from biophysical to ecological. Modeling improves research at all stages - hypothesis formulation, experimental design, analysis, and interpretation - and provides a natural language by which exchange of ideas can highlight commonalities and uncover unforeseen connections between problems.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory Grants (P20)
Project #
5P20GM104420-04
Application #
9414051
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Gao, Hongwei
Project Start
2015-03-15
Project End
2020-01-31
Budget Start
2018-02-01
Budget End
2019-01-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Idaho
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
075746271
City
Moscow
State
ID
Country
United States
Zip Code
83844
Patel, Jagdish Suresh; Ytreberg, F Marty (2018) Fast Calculation of Protein-Protein Binding Free Energies Using Umbrella Sampling with a Coarse-Grained Model. J Chem Theory Comput 14:991-997
Buzbas, Erkan Ozge; Verdu, Paul (2018) Inference on admixture fractions in a mechanistic model of recurrent admixture. Theor Popul Biol 122:149-157
Ferguson, Jake M; Buzbas, Erkan Ozge (2018) Inference from the stationary distribution of allele frequencies in a family of Wright-Fisher models with two levels of genetic variability. Theor Popul Biol 122:78-87
Suchar, Vasile A; Aziz, Noha; Bowe, Amanda et al. (2018) An Exploration of the Spatiotemporal and Demographic Patterns of Ebola Virus Disease Epidemic in West Africa Using Open Access Data Sources. Appl Geogr 90:272-281
Dutta, Rabijit; Xing, Tao; Swanson, Craig et al. (2018) Comparison of flow and gas washout characteristics between pressure control and high-frequency percussive ventilation using a test lung. Physiol Meas 39:035001
Li, Longze; Vakanski, Aleksandar (2018) Generative Adversarial Networks for Generation and Classification of Physical Rehabilitation Movement Episodes. Int J Mach Learn Comput 8:428-436
Rowley, Paul A; Patterson, Kurt; Sandmeyer, Suzanne B et al. (2018) Control of yeast retrotransposons mediated through nucleoporin evolution. PLoS Genet 14:e1007325
Vakanski, Aleksandar; Jun, Hyung-Pil; Paul, David et al. (2018) A Data Set of Human Body Movements for Physical Rehabilitation Exercises. Data (Basel) 3:
Damase, Tulsi Ram; Miura, Tanya A; Parent, Christine E et al. (2018) Application of the Open qPCR Instrument for the in Vitro Selection of DNA Aptamers against Epidermal Growth Factor Receptor and Drosophila C Virus. ACS Comb Sci 20:45-54
Baumgaertner, Bert; Carlisle, Juliet E; Justwan, Florian (2018) The influence of political ideology and trust on willingness to vaccinate. PLoS One 13:e0191728

Showing the most recent 10 out of 29 publications