@article{dobreva_brady-nicholls_larripa_puelz_mehlsen_olufsen_2021, title={A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during an endotoxin challenge}, volume={599}, ISSN={["1469-7793"]}, DOI={10.1113/JP280883}, abstractNote={Inflammation in response to bacterial endotoxin challenge impacts physiological functions, including cardiovascular, thermal and pain dynamics, although the mechanisms are poorly understood. We develop an innovative mathematical model incorporating interaction pathways between inflammation and physiological processes observed in response to an endotoxin challenge. We calibrate the model to individual data from 20 subjects in an experimental study of the human inflammatory and physiological responses to endotoxin, and we validate the model against human data from an independent study. Using the model to simulate patient responses to different treatment modalities reveals that a multimodal treatment combining several therapeutic strategies gives the best recovery outcome.}, number={5}, journal={JOURNAL OF PHYSIOLOGY-LONDON}, author={Dobreva, Atanaska and Brady-Nicholls, Renee and Larripa, Kamila and Puelz, Charles and Mehlsen, Jesper and Olufsen, Mette S.}, year={2021}, month={Mar}, pages={1459–1485} }
 @article{cogan_bao_paus_dobreva_2021, title={Data assimilation of synthetic data as a novel strategy for predicting disease progression in alopecia areata}, volume={38}, ISSN={["1477-8602"]}, DOI={10.1093/imammb/dqab008}, abstractNote={The goal of patient-specific treatment of diseases requires a connection between clinical observations with models that are able to accurately predict the disease progression. Even when realistic models are available, it is very difficult to parameterize them and often parameter estimates that are made using early time course data prove to be highly inaccurate. Inaccuracies can cause different predictions, especially when the progression depends sensitively on the parameters. In this study, we apply a Bayesian data assimilation method, where the data are incorporated sequentially, to a model of the autoimmune disease alopecia areata that is characterized by distinct spatial patterns of hair loss. Using synthetic data as simulated clinical observations, we show that our method is relatively robust with respect to variations in parameter estimates. Moreover, we compare convergence rates for parameters with different sensitivities, varying observational times and varying levels of noise. We find that this method works better for sparse observations, sensitive parameters and noisy observations. Taken together, we find that our data assimilation, in conjunction with our biologically inspired model, provides directions for individualized diagnosis and treatments.}, number={3}, journal={MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA}, author={Cogan, N. G. and Bao, Feng and Paus, Ralf and Dobreva, Atanaska}, year={2021}, month={Sep}, pages={314–332} }
 @article{dobreva_paus_cogan_2020, title={Toward Predicting the Spatio-Temporal Dynamics of Alopecia Areata Lesions Using Partial Differential Equation Analysis}, volume={82}, ISSN={["1522-9602"]}, DOI={10.1007/s11538-020-00707-0}, abstractNote={Hair loss in the autoimmune disease, alopecia areata (AA), is characterized by the appearance of circularly spreading alopecic lesions in seemingly healthy skin. The distinct spatial patterns of AA lesions form because the immune system attacks hair follicle cells that are in the process of producing hair shaft, catapults the mini-organs that produce hair from a state of growth (anagen) into an apoptosis-driven regression state (catagen), and causes major hair follicle dystrophy along with rapid hair shaft shedding. In this paper, we develop a model of partial differential equations (PDEs) to describe the spatio-temporal dynamics of immune system components that clinical and experimental studies show are primarily involved in the disease development. Global linear stability analysis reveals there is a most unstable mode giving rise to a pattern. The most unstable mode indicates a spatial scale consistent with results of the humanized AA mouse model of Gilhar et al. (Autoimmun Rev 15(7):726-735, 2016) for experimentally induced AA lesions. Numerical simulations of the PDE system confirm our analytic findings and illustrate the formation of a pattern that is characteristic of the spatio-temporal AA dynamics. We apply marginal linear stability analysis to examine and predict the pattern propagation.}, number={3}, journal={BULLETIN OF MATHEMATICAL BIOLOGY}, author={Dobreva, Atanaska and Paus, Ralf and Cogan, N. G.}, year={2020}, month={Feb} }