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Rognes, M. E.

Publications and source records attributed to Rognes, M. E..

20 records · Page 2Linked to original sources

Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields

BackgroundInflux and clearance of substances in the brain parenchyma occur by a combination of diffusion and convection, but the relative importance of thiese mechanisms is unclear. Accurate modeling of tracer distributions in the brain relies on parameters that are partially unknown and with literature values varying up to 7 orders of magnitude. In this work, we rigorously quantified the variability of tracer enhancement in the brain resulting from uncertainty in diffusion and convection model parameters.\n\nMethodsIn a mesh of a human brain, using the convection-diffusion-reaction equation, we simulated tracer enhancement in the brain parenchyma after intrathecal injection. Several models were tested to assess the uncertainty both in type of diffusion and velocity fields and also the importance of their magnitude. Our results were compared with experimental MRI results of tracer enhancement.\n\nResultsIn models of pure diffusion, the expected amount of tracer in the gray matter reached peak value after 15 hours, while the white matter does not reach peak within 24 hours with high likelihood. Models of the glymphatic system behave qualitatively similar as the models of pure diffusion with respect to expected time to peak but display less variability. However, the expected time to peak was reduced to 11 hours when an additional directionality was prescribed for the glymphatic circulation. In a model including drainage directly from the brain parenchyma, time to peak occured after 6-8 hours for the gray matter.\n\nConclusionEven when uncertainties are taken into account, we find that diffusion alone is not sufficient to explain transport of tracer deep into the white matter as seen in experimental data. A glymphatic velocity field may increase transport if a directional structure is included in the glymphatic circulation.

neuroscience↗

Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data

Mathematical modeling and simulation have emerged as a potentially powerful, time and cost effective approach to personalized cancer treatment. The usefulness of mechanistic models to disentangle complex multi-scale cancer processes such as treatment response has been widely acknowledged. However, a major barrier for multi-scale models to predict the outcomes of therapeutic regimens in a particular patient lies in their initialization and parameterization which need to reflect individual cancer characteristics accurately. In this study we use multi-type routinely acquired measurements on a single breast tumor, including histopathology, magnetic resonance imaging, and molecular profiling to personalize parts of a complex multi-scale model of breast cancer treated with chemotherapeutic and anti-angiogenic agents. We model the dynamics of drugs in tissue (pharmacokinetics) and the corresponding effects on their targets (pharmacodynamics). We developed a open-source computer program that simulates cross-sections of tumors under 12-week therapy regimes and use it to individually reproduce and elucidate treatment outcomes of four patients. For two of the tumors that did not respond to therapy, we used model simulations to suggest alternative regimes, depending on their individual characteristics, with improved outcomes. We found that more frequent doses of chemothereapy reduce tumor burden in a low proliferative tumor while lower doses of anti-angiogenic agents improve drug penetration in a poorly perfused tumor. In addition to bridge multi-type clinical data to shed light on individual treatment outcomes, our approach identified a few tumor-related aspects that need to be clinically portraited better to allow for future model-driven personalized cancer therapy.

cancer biology↗