Whole Body Model of Iron Dynamics
Grey Davis* – University of Tennessee Knoxville, Hope Shevchuk* – Worcester Polytechnic Institute, Jigneshkumar Parmar – UConn Health, Center for Quantitative Medicine, Pedro Mendes – UConn Health, Center for Quantitative Medicine
Iron plays an important role in many processes of the body, most importantly oxygen transport by red blood cells. The goal of this research is to create a predictive model of whole body iron metabolism for humans. As a first step, a whole body model of iron homeostasis was created for mice. This model will be used to gain a better understanding of iron metabolism disorders (i.e. anemia and hemochromatosis). The present mouse model was calibrated to data previously published by the Reich group. All calculations, including parameter estimation, were carried out with the open-source software COPASI.
A Compartmental Model of Positive and Negative Stimulation in T Cell Receptor Signaling
Hannah Rollins* – Clemson University, Alex Galarce* – New College of Florida, Madison Brandon – UConn Health, Center for Quantitative Medicine, Reinhard Laubenbacher – UConn Health, Center for Quantitative Medicine
Unlike traditional cancer treatment, the goal of Cancer Immunotherapy is to help the immune system, specifically the cancer fighting CD8+ T-cell, fight cancer. The idea was first presented in 1893 and gained traction in the 1970s. An initial activation signal from the T-cell Receptor and a secondary, amplifying signal from costimulatory receptors begin a series of signaling processes that lead to the activation of transcription factors important for the proliferation of CD8+ T-cells. Inhibitory receptors are responsible for terminating this signal and deactivating the T-cell. We present a model of the major intracellular signaling of the T-cell after its activation. Our approach utilizes mathematical modeling and simulation tools such as BioNetGen and Copasi and allows us to simulate different initial conditions of T-cell interactions as well as to further understand the intracellular T-cell activation and deactivation signals. Furthermore, we will explore how the T-cell responds to Cancer Immunotherapy treatments which block the inhibitory receptors.
Pipeline to infer brain connectivity networks from fMRI data
Christopher Tseng* – Emory University, Shichao Wang* – University of Pennsylvania, Michael Stevens – Olin Neuropsychiatry Research Center, Institute of Living, Reinhard Laubenbacher – UConn Health, Center for Quantitative Medicine, Paola Vera-Licona – UConn Health, Center for Quantitative Medicine.
The association between functional connectivity networks and neurological diseases has been established in past studies. Accurately identifying such connections from neuroimaging data, though, is a non-trivial matter, requiring the careful selection of network inference methods, a choice that can be better informed through benchmarking. A systematic comparison of neuroscience network inference methods and molecular biology inference methods was performed using in silico fMRI time series based on 200 networks with either, random and scale-free structure. Results were then evaluated with different statistical metrics. The two selected network inference methods from molecular biology performed well, with GENIE  consistently scoring in the top 3 methods. Creating consensus networks from combining the top performing network inference methods also proved effective, with the consensus networks built from the top five methods consistently outperforming the inference of individual methods. A pipeline for inferring brain connectivity networks is then proposed including consensus networks built from the top performing network inference methods.
 Huynh-Thu, V. A., et al., Inferring Regulatory Networks from Expression Data Using Tree-Based Methods, (2010), PLoS ONE 5(9): e12776.