@article {25515,
	title = {Interstitial Solute Transport in 3D Reconstructed Neuropil Occurs by Diffusion Rather than Bulk Flow},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {114},
	year = {2017},
	pages = {9894-9899},
	publisher = {National Academy og Sciences},
	doi = {10.1073/pnas.1706942114},
	author = {Karl Erik {Holter} and Kehlet, Benjamin and Devor, Anna and Terrence J. {Sejnowski} and Anders M {Dale} and Stig W. {Omholt} and Ole P. {Ottesen} and Erlend A. {Nagelhus} and Mardal, Kent-Andre and Klas H. {Pettersen}}
}
@article {25500,
	title = {Roadmap for cardiovascular circulation model},
	journal = {American Journal of Physiology},
	volume = {594},
	number = {23},
	year = {2016},
	pages = {6909-6928},
	publisher = {The Physiological Society},
	abstract = {Computational models of many aspects of the mammalian cardiovascular circulation have been developed. Indeed, along with orthopaedics, this area of physiology is one that has attracted much interest from engineers, presumably because the equations governing blood flow in the vascular system are well understood and can be solved with well-established numerical techniques. Unfortunately, there have been only a few attempts to create a comprehensive public domain resource for cardiovascular researchers. In this paper we propose a roadmap for developing an open source cardiovascular circulation model. The model should be registered to the musculo-skeletal system. The computational infrastructure for the cardiovascular model should provide for near real-time computation of blood flow and pressure in all parts of the body. The model should deal with vascular beds in all tissues, and the computational infrastructure for the model should provide links into CellML models of cell function and tissue function. In this work we review the literature associated with 1D blood flow modelling in the cardiovascular system, discuss model encoding standards, software and a model repository. We then describe the coordinate systems used to define the vascular geometry, derive the equations and discuss the implementation of these coupled equations in the open source computational software OpenCMISS. Finally, some preliminary results are presented and plans outlined for the next steps in the development of the model, the computational software and the graphical user interface for accessing the model.},
	doi = {10.1113/JP272660},
	author = {Safei, S and C P {Bradley} and Suresh, V and Mithraratne, K and Muller, A and Ho, H and Ladd, D and Leif Rune {Hellevik} and Stig W. {Omholt} and J G {Chase} and L O {M{\"u}ller} and S M {Watanabe} and P J {Blanco} and de Bono, B and Peter J. {Hunter}}
}
@article {23371,
	title = {A computational pipeline for quantification of mouse myocardial stiffness parameters},
	journal = {Computers in Biology and Medicine},
	volume = {53},
	year = {2014},
	pages = {65{\textendash}75},
	publisher = {Comput. Biol. Med.},
	abstract = {The mouse is an important model for theoretical-experimental cardiac research, and biophysically based whole organ models of the mouse heart are now within reach. However, the passive material properties of mouse myocardium have not been much studied. We present an experimental setup and associated computational pipeline to quantify these stiffness properties. A mouse heart was excised and the left ventricle experimentally inflated from 0 to 1.44kPa in eleven steps, and the resulting deformation was estimated by echocardiography and speckle tracking. An in silico counterpart to this experiment was built using finite element methods and data on ventricular tissue microstructure from diffusion tensor {MRI}. This model assumed a hyperelastic, transversely isotropic material law to describe the force-deformation relationship, and was simulated for many parameter scenarios, covering the relevant range of parameter space. To identify well-fitting parameter scenarios, we compared experimental and simulated outcomes across the whole range of pressures, based partly on gross phenotypes (volume, elastic energy, and short- and long-axis diameter), and partly on node positions in the geometrical mesh. This identified a narrow region of experimentally compatible values of the material parameters. Estimation turned out to be more precise when based on changes in gross phenotypes, compared to the prevailing practice of using displacements of the material points. We conclude that the presented experimental setup and computational pipeline is a viable method that deserves wider application.},
	issn = {1879-0534},
	doi = {10.1016/j.compbiomed.2014.07.013},
	author = {Nordb{\o}, Oyvind and Lamata, Pablo and Land, Sander and Steven A {Niederer} and Jan M. {Aronsen} and William E. {Louch} and Sjaastad, Ivar and Martens, Harald and Arne B. {Gjuvsland} and T{\o}ndel, Kristin and Torp, Hans and Lohezic, Maelene and Jurgen E. {Schneider} and Espen W. {Remme} and Nicolas P. {Smith} and Stig W. {Omholt}}
}
@article {Simula.simula.2684,
	title = {Hierarchical Multivariate Regression-Based Sensitivity Analysis Reveals Complex Parameter Interaction Patterns in Dynamic Models},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	volume = {120},
	number = {1},
	year = {2013},
	pages = {25-41},
	publisher = {Elsevier},
	doi = {10.1016/j.chemolab.2012.10.006},
	author = {T{\o}ndel, Kristin and Jon Olav {Vik} and Martens, Harald and Ulf G. {Indahl} and Nicolas P. {Smith} and Stig W. {Omholt}}
}
@inbook {Simula.simula.2686,
	title = {PLS-Based Multivariate Metamodeling of Dynamic Systems},
	booktitle = {New Perspectives in Partial Least Squares and Related Methods},
	volume = {56},
	year = {2013},
	pages = {3-30},
	publisher = {Springer},
	organization = {Springer},
	chapter = {1},
	address = {New York},
	author = {Martens, Harald and T{\o}ndel, Kristin and Tafintseva, Valeriya and Kohler, Achim and Plathe, Erik and Jon Olav {Vik} and Arne B. {Gjuvsland} and Stig W. {Omholt}},
	editor = {Abdi, H. and W. W. {Chin} and V. E. {Vinzi} and Russolillo, G. and Trinchera, L.}
}
@article {karticlereference.2014-04-03.9225258604,
	title = {Multi-Way Metamodelling Facilitates Insight Into the Complex Input-Output Maps of Nonlinear Dynamic Models},
	journal = {BMC Systems Biology},
	volume = {6},
	number = {1},
	year = {2012},
	pages = {88},
	publisher = {BioMedCentral},
	author = {T{\o}ndel, Kristin and Ulf G. {Indahl} and Arne B. {Gjuvsland} and Stig W. {Omholt} and Martens, Harald}
}
@article {Simula.simula.2680,
	title = {Genotype-Phenotype Map Characteristics of an in Silico Heart Cell},
	journal = {Frontiers in Physiology},
	volume = {2},
	number = {1},
	year = {2011},
	pages = {106},
	publisher = {NCBI},
	author = {Jon Olav {Vik} and Arne B. {Gjuvsland} and Li, Liren and T{\o}ndel, Kristin and Steven A {Niederer} and Nicolas P. {Smith} and Peter J. {Hunter} and Stig W. {Omholt}}
}
@article {Simula.simula.2682,
	title = {Hierarchical Cluster-Based Partial Least Squares Regression Is an Efficient Tool for Metamodelling of Nonlinear Dynamic Models},
	journal = {BMC Systems Biology},
	volume = {5},
	number = {1},
	year = {2011},
	pages = {90},
	publisher = {BioMedCentral},
	doi = {10.1186/1752-0509-5-90},
	author = {T{\o}ndel, Kristin and Ulf G. {Indahl} and Arne B. {Gjuvsland} and Jon Olav {Vik} and Peter J. {Hunter} and Stig W. {Omholt} and Martens, Harald}
}
