Abstract
In silico methods and models in the pathology of the blood-brain barrier (BBB) or also called BBB "computational pathology", are based on using mathematical approaches together with complex, high-dimensional experimental data to evaluate and predict disease-related impacts on the CNS. These computational methods and tools have been designed to deal with BBB-linked neuropathology at the molecular, cellular, tissue, and organ levels. The molecular and cellular levels mainly include molecular docking and molecular dynamics simulations (atomistic and coarse-grain) of mutated or misfolded tight junction proteins, receptors, and various BBB transporters. The tissue and organ levels encompass the mechanistic and pharmacokinetic models as well as finite-element method and pathway analyses enriched with multiple sources of raw data (e.g., in vitro and in vivo, histopathological records, "-omics", and imaging data). Overall, this review discusses comprehensive computational techniques and strategies at different levels of complexity, providing new insights and future directions for diagnosis, treatment improvement, and a deeper understanding of BBB-related neuropathological events.
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(Reprinted from Ref. Goliaei et al. (2015). Copyright 2015, ACS Chemical Neuroscience). (The graphics program ChemDraw® was used to create the artwork)
(Reprinted from Ref. Bearer et al. (2009). Copyright 2009, Cancer Research). (The graphics program ChemDraw® was used to create the artwork)
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Acknowledgements
The authors are also grateful to BMBF (Bundesministerium für Bildung und Forschung), European Commission, and German Research Foundation (DFG) for their support of this work by providing the LIPOTRANS 13N11803, NEUROBID Call HEALTH-2009-2.2.1-4, and DFG Fo315/4-1 grants to Carola Förster. This publication was also funded by the University of Würzburg in the funding program Open Access Publishing.
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Shityakov, S., Förster, C.Y. Computational simulation and modeling of the blood–brain barrier pathology. Histochem Cell Biol 149, 451–459 (2018). https://doi.org/10.1007/s00418-018-1665-x
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DOI: https://doi.org/10.1007/s00418-018-1665-x