@article {28301,
	title = {TCRPower: Quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences},
	journal = {Briefings in Bioinformatics},
	volume = {23},
	year = {2022},
	month = {01/2022},
	pages = {bbab566},
	publisher = {Oxford Academic},
	abstract = {T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases, and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to mis-diagnosis if diagnostically-relevant TCRs remain undetected. To address this issue, we developed TCRPower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth, and read cut-off. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones as spike-in TCRs. We sequenced the spike-in TCRs from T-cell clones, together with TCRs from peripheral blood, using a 5{\textquoteright} RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cut-off, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0\%, TCR β-chain 92.4\%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease relevant TCRs for diagnostic applications.\&nbsp;},
	keywords = {Adaptive Immune Receptor Repertoire sequencing, Bulk T-cell receptor sequencing, computational model, Spike-in standards, T-cell receptor, TCRpower},
	doi = {https://doi.org/10.1093/bib/bbab566},
	author = {Dahal-Koirala, Shiva and Balaban, Gabriel and Ralf Stefan {Neumann} and Scheffer, Lonneke and Knut Erik Aslak {Lundin} and Greiff, Victor and Ludvig Magne {Sollid} and Qiao, Shuo-Wang and Sandve, Geir-Kjetil}
}
@article {27783,
	title = {A computational study of the effects of tachycardia-induced remodelling on calcium wave propagation in rabbit atrial myocytes},
	journal = {Frontiers in Physiology},
	volume = {12},
	year = {2021},
	publisher = {Frontiers},
	abstract = {In atrial cardiomyocytes without a well-developed T-tubule system, calcium diffuses from the periphery towards the center creating a centripetal wave pattern. During atrial fibrillation, rapid activation of atrial myocytes induces complex remodelling in diffusion properties that result in failure of calcium to propagate in a fully regenerative manner towards the center; a phenomenon termed\&nbsp; {\textquoteleft}{\textquoteleft}calcium silencing{\textquoteright}{\textquoteright}. This has been observed in rabbit atrial myocytes after exposure to prolonged rapid\&nbsp; pacing. Although experimental studies have pointed to possible mechanisms underlying calcium silencing, their individual effects and relative importance remain largely unknown.In this study we used computational modelling of the rabbit atrial cardiomyocyte to query the individual effects and combined effects of the proposed mechanisms leading to calcium\&nbsp;silencing and abnormal calcium wave propagation. We employed a population of models obtained from a newly developed model of the rabbit atrial myocyte with spatial representation of intracellular calcium handling. We selected parameters in the model that represent experimentally observed cellular remodelling which have been implicated in calcium silencing, and scaled their values in the population to match experimental observations. In particular, we changed the maximum conductances of I_CaL I_NCX, and I_NaK, \&nbsp;RyR open probability, RyR density, Serca2a density, and calcium buffering strength. We incorporated remodelling in a population of 16 models by independently varying parameters that reproduce experimentally observed cellular remodelling, and quantified the resulting alterations in calcium dynamics and wave propagation patterns.The results show a strong effect of\&nbsp; I_CaL in driving calcium silencing, with I_NCX, \&nbsp;I_NaK, and RyR density also resulting in calcium silencing in some models. Calcium alternans was observed in some models where I_NCX and Serca2a density had been changed. Simultaneously incorporating changes in all remodelled parameters resulted in calcium\&nbsp;silencing in all models, indicating the predominant role of decreasing I_CaL in the population phenotype.},
	keywords = {calcium waves, computational model, population of models, rabbit atrial cardiomyocyte, spatial calcium dynamics, tachypacing},
	author = {Vagos, Marcia and Arevalo, Hermenegild and Heijman, Jordi and Schotten, Ulrich and Sundnes, Joakim}
}
@inproceedings {7391416,
	title = {Localization and not extent of fibrofatty infiltratio is the primary factor determining conduction disturbance in a computational model of arrhythmogenic cardiomyopathy},
	journal = {2015 E-Health and Bioengineering Conference (EHB)},
	year = {2015},
	month = {Nov},
	pages = {1-6},
	publisher = {ACM IEEE},
	keywords = {action potential, activation sites, arrhythmogenic cardiomyopathy, bioelectric potentials, biological tissues, biomedical MRI, biomembranes, cardiovascular system, cellular biophysics, computational model, Computational modeling, conduction disturbance, conduction properties, disease progression, diseases, electrophysiology, fibrofatty diseased tissue location, fibrofatty infiltration localization, fibrofatty patterns, fibrofatty region, Geometry, global conduction velocity, Heart, human electrophysiology, intercellular junctions, Junctions, Magnetic resonance imaging, Morphology, nonviable tissue, organ-level model, passive membrane, patient heart geometry, physiological models},
	doi = {10.1109/EHB.2015.7391416},
	author = {Kallhovd, Siri and S. U. {Gjerald} and Wall, Samuel and Saberniak, J. and Haugaa, K. and Maleckar, Molly}
}
