andreavs

• Ellingsrud, Ada Johanne; Solbrå, Andreas Våvang; Einevoll, Gaute; Halnes, Geir & Rognes, Marie Elisabeth (2020). Finite element simulation of ionic electrodiffusion in cellular geometries. Frontiers in Neuroinformatics.  ISSN 1662-5196.  14, s 1- 18 . doi: 10.3389/fninf.2020.00011 Fulltekst i vitenarkiv.
• Hansson, Kenth-Arne; Solbrå, Andreas Våvang; Gundersen, Kristian & Bruusgaard, Jo C. (2020). Computational assessment of transport distances in living skeletal muscle fibers studied in situ. Biophysical Journal.  ISSN 0006-3495.  119(11), s 2166- 2178 . doi: 10.1016/j.bpj.2020.10.016
• Sætra, Marte Julie; Solbrå, Andreas Våvang; Devor, Anna; Sakadžić, Sava; Dale, Anders M. & Einevoll, Gaute (2020). Spatially Resolved Estimation of Metabolic Oxygen Consumption From Optical Measurements in Cortex. Neurophotonics.  ISSN 2329-423X.  7(3) . doi: 10.1117/1.NPh.7.3.035005 Fulltekst i vitenarkiv.
• Solbrå, Andreas Våvang; Bergersen, Aslak Wigdahl; van den Brink, Jonas; Malthe-Sørenssen, Anders; Einevoll, Gaute & Halnes, Geir (2018). A Kirchhoff-Nernst-Planck framework for modeling large scale extracellular electrodiffusion surrounding morphologically detailed neurons. PLoS Computational Biology.  ISSN 1553-734X.  14(10), s 1- 26 . doi: 10.1371/journal.pcbi.1006510 Fulltekst i vitenarkiv.
• Winje, Ivan; Sheng, Xia; Hansson, Kenth-Arne; Solbrå, Andreas Våvang; Tennøe, Simen; Saatcioglu, Fahri; Bruusgaard, Jo C. & Gundersen, Kristian (2018). Cachexia does not induce loss of myonuclei or muscle fibres during xenografted prostate cancer in mice. Acta Physiologica.  ISSN 1748-1708.  s 1- 9 . doi: 10.1111/apha.13204
• Dragly, Svenn-Arne; Mobarhan, Milad; Solbrå, Andreas Våvang; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute (2017). Neuronify: An educational simulator for neural circuits. eNeuro.  ISSN 2373-2822.  4(2:e0022-17.2017), s 1- 13 . doi: 10.1523/ENEURO.0022-17.2017 Fulltekst i vitenarkiv.
• Omairi, Saleh; Matsakas, Antonios; Degens, Hans; Kretz, Oliver; Hansson, Kenth-Arne; Solbrå, Andreas Våvang; Bruusgaard, Jo C.; Joch, Barbara; Sartori, Roberta; Giallourou, Natasa; Mitchell, Robert; Collins-Hooper, Henry; Foster, Keith; Pasternack, Arja; Ritvos, Olli; Sandri, Marco; Narkar, Vihang; Swann, Jonathan R.; Huber, Tobias & Patel, Ketan (2016). Enhanced exercise and regenerative capacity in a mouse model that violates size constraints of oxidative muscle fibres. eLIFE.  ISSN 2050-084X.  5(AUGUST) . doi: 10.7554/eLife.16940

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• Solbrå, Andreas Våvang (2019). Modeling electrical and diffusive transports in neural tissue. Series of dissertations submitted to the Faculty of Mathematics and Natural Sciences, University of Oslo.. 2129.
• Solbrå, Andreas Våvang; Bergersen, Aslak Wigdahl; van den Brink, Jonas; Malthe-Sørenssen, Anders; Einevoll, Gaute & Halnes, Geir (2018). Modeling electrodiffusion in the extracellular space around a morphologically detailed neuron.
• Dragly, Svenn-Arne; Mobarhan, Milad; Solbrå, Andreas Våvang; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute (2017). Neuronify: An Educational Simulator for Neural Circuits. Vis sammendrag

  Neurons are cells in the brain that are able to rapidly change the electric field across their cell membrane. These changes allow neurons to communicate with each other and is the basis for the complex computations in the brain. Understanding how neurons communicate and the properties of neuronal networks is essential for neuroscience students. Traditionally, students draw networks with pen and paper and qualitatively deduce features of the network by analyzing the static drawings. Here, we present Neuronify, an app that allows students to draw the same networks on a computer or mobile device and run dynamic simulations without programming. The students can test their analysis by running the network and check their predictions against the outcome. Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. Such software is readily available in many areas of natural science such as physics and electrical engineering. However, few educational apps are available for simulation of neural networks. Neuronify allows the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can drag and drop network elements such as neurons, electrical stimulation tools and recording devices. The components can then easily be connected to one another. Building intuition for how neurons and neural networks behave has been a top priority in designing Neuronify. We aim to provide a low entry point to simulation-based neuroscience. Most undergraduate students do not have the computational experience to create their own neural simulator. Neuronify offers them an opportunity to build and experiment with neural networks in a graphical and easy-to-understand interface. By playing around with the networks, the students can develop a good understanding of their properties. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt. It has been downloaded more than 30,000 times since its launch and is available on smart phones (Android, iOS), tablet computers as well personal computers (Windows, Mac, Linux).

• Halnes, Geir; Solbrå, Andreas Våvang; Mäki-Marttunen, Tuomo; Pettersen, Klas; Andreassen, Ole Andreas; Malthe-Sørensen, Anders & Einevoll, Gaute T. (2017). Modelling electrical and chemical dynamics in brain tissue.
• Tennøe, Simen; Mobarhan, Milad; Dragly, Svenn-Arne; Solbrå, Andreas Våvang & Nederbragt, Alexander Johan (2017). Teaching modelling to first-year biology students. Vis sammendrag

  The field of biology relies heavily on computations. This is not well reflected in education and the current undergraduate curriculum has little computational content. This results in a discontinuity between the education received by the students and the problems they face after graduation. The end result is that the students are not equipped to meet the requirements of modern research nor the tasks awaiting them in the industry. To remedy this problem, a new course, BIOS 1100 - Introduction to Modelling in Biology, will be held for the first time at the University of Oslo in fall 2017. Currently, no available book combines biology and programming at an introductory level in a satisfactory way. Most textbooks teach both topics separately or expect the reader to know either biology or programming from before. We have therefore written our own textbook to be used as curriculum in this course. This book aims to teach programming and modelling to first year biology students through examples from biology. The book is based on a philosophy of just-in-time teaching where the programming concepts are introduced just when they are needed to solve the problem in hand. This puts the programming content in an unusual order in comparison to the traditional computer science curriculum while keeping the biology students motivated by the problems they solve. The examples are mainly from the three branches of biology: population dynamics, bioinformatics and evolution. The purpose is to give biology students a practical understanding of programming and mathematical models. This will enable the understanding of mathematical models and encourage critical thinking. Programming allows much more realistic and inspiring problems to be addressed, enabling students to work on current research topics early on. The textbook is written using DocOnce (created by Hans Petter Langtangen), which enables us to compile the book to both LaTeX/PDF, HTML and Jupyter Notebooks.

• Dragly, Svenn-Arne; Mobarhan, Milad; Solbrå, Andreas Våvang & Tennøe, Simen (2016). Neuronify: An educational app for simulation of neural circuits.
• Mobarhan, Milad; Dragly, Svenn-Arne & Solbrå, Andreas Våvang (2016). Neuronify: an educational app for simulation of neural circuits.
• Mobarhan, Milad; Dragly, Svenn-Arne; Tennøe, Simen & Solbrå, Andreas Våvang (2016). Introduction to Computational Biology with Python.
• Mobarhan, Milad; Tennøe, Simen & Solbrå, Andreas Våvang (2016). Neuronify: a new tool for creating simple neural networks.
• Solbrå, Andreas Våvang; Einevoll, Gaute; Malthe-Sørenssen, Anders & Halnes, Geir (2016). A novel electrodiffusive scheme for modeling ion dynamics in neural tissue.
• Solbrå, Andreas Våvang; Malthe-Sørenssen, Anders; Einevoll, Gaute & Halnes, Geir (2016). A novel electrodiffusive scheme for modeling ion dynamics in neural tissue.
• Solbrå, Andreas Våvang & Winje, Ivan (2016). Semi-automatic segmentation of muscle images in MATLAB.
• Tennøe, Simen; Mobarhan, Milad; Dragly, Svenn-Arne; Solbrå, Andreas Våvang & Langtangen, Hans Petter (2015). CSE: Computing in Science Education.

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