Diabetes

Building an understanding of beta-cell resilience by integrating theory and experiment. Sir Ronald Ross’s pioneering work on Malaria led to the discovery of the parasite’s vector.  He also is credited with the development of the first mathematical model to study its transmission (cite).   In the 1950’s, future Nobel Prize winners, Alan Hodgkin and Andrew Huxley developed a mathematical model to explain the ionic mechanisms underlying the initiation and propagation of the action potentials in the axon of a giant squid. This work started the fields of mathematical neuroscience and biological rhythms. The 1980’s brought the development of the PCR providing a much-needed tool for experimentalists to amplify DNA. Today, new laboratory technologies are being developed to assist scientists in answering biology’s most complex questions. These new methods also remind us of the enormously complex nature of biology.  We are now in an era of need for emerging quantitative technologies such as Theoretical, Computational and System Biology models (TCSB). Emerging technologies that not only focus on certain key aspects of biology, but also easily available and usable, just like PCR, for experimentalists to have at hand to tackle Diabetes most challenging questions.

Research AIM: Modeling dynamic changes in T1DM; from a minimal model of T1DM prediction to a TCSB model that will study the hidden dynamics that may lead to the cause and eventually the cure of the disease.  Our goal is to bridge statistics, mathematics, systems biology, pancreatic physiology, immunology and medicine through the development of TCSB models that account for islet marker antibodies as key parameters to predict the onset of disease and to determine correlations between the appearance of marker antibodies and b-cell resilience.  With Dr. Pietropaolo’s success at identifying islet marker antibodies in patients in the pre-diabetic state, we can now apply the tools of modeling to try to predict not just the timing to disease onset but how can one better treat the disease.

Expected Outcome 1: The presence of islet maker antibodies, like IAA, GAD65, and ICA, is a sign that one’s chances of developing T1DM are significant. We will build upon our multiple linear regression models, which are quite successful at correlating the onset of disease to the given levels of islet marker antibodies and develop a predictive TCSB model that will predict the onset of diabetes based on the level of the islet marker antibodies and more importantly, allow us to test variations in our results, based on certain key kinetic parameters.

Expected Outcome 2: We have predicted that the combination of novel biomarkers detecting antibodies directed at IA-2, ZnT8 and GAD65 specific epitopes would further enhance sensitivity and the predictive value of T1DM progression as compared to conventional islet autoantibody markers.  Combining our experimental results with sophisticated TCSB modeling, we will be able to develop analytical tools to help predict the onset of the disease using these key parameters.

The regression work is critical for predicting disease onset, but is insufficient for determining causality.  We will continue to advance this work to discover causality of changes in b-cell resilience.

Our TCSB model will study in-silico the following:
  

• What are specific differences in beta-cell physiology and resilience that can be predicted between patients with fragmented and full length IA-2 epitopes?
• What is triggering the immune response to behave differently in these patients?
• Is it a difference in the MHC II complex of Macrophages?
• Is it an error in the folding of proteins in the endoplasmic reticulum?
• Is it catalyzed by certain childhood viral infections?

Our goal is not to simply build a model that reproduces information that is already well known.  This is a flaw in many researchers’ existing models.  Applied Mathematics was developed in the 17th and 18th centuries to predict ideas in Astronomy and Physics. Biology is quite different then Physics.  Our goal is to rethink mathematics in terms of Diabetes.  What if math would have been developed to predict reasons for the cause of T1D?  That is our goal!

Our TCSB model will test predictions about b-cell resilience.  With success, we can then begin to adapt our model to create an emerging technology that researchers study Diabetes can easily use in their labs. This strategy will depend significantly on the controlling features in the pancreas, such as the immune system’s dynamics, the b-cell dynamics, and the islet marker antibodies.

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