On the Application of Active Learning and Gaussian Processes in Postcryopreservation Cell Membrane Integrity Experiments
Issue No. 03 - May-June (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.155
F. Barry , Nat. Centre for Biomed. Eng. Sci., Nat. Univ. of Ireland Galway, Galway, Ireland
M. J. Murphy , Nat. Centre for Biomed. Eng. Sci., Nat. Univ. of Ireland Galway, Galway, Ireland
D. Fay , Comput. Lab., Univ. of Cambridge, Cambridge, UK
M. Norkus , Electr. & Electron. Eng., Nat. Univ. of Ireland Galway, Galway, Ireland
G. Olaighin , Electr. & Electron. Eng., Nat. Univ. of Ireland Galway, Galway, Ireland
L. Kilmartin , Electr. & Electron. Eng., Nat. Univ. of Ireland Galway, Galway, Ireland
Biological cell cryopreservation permits storage of specimens for future use. Stem cell cryostorage in particular is fast becoming a broadly spread practice due to their potential for use in regenerative medicine. For the optimal cryopreservation process, ultralow temperatures are needed. However, elevated temperatures are often unavoidable in a typical sample handling cycle which in turn negatively affects the postcryopreservation integrity of cells. In this paper, we present an application of active learning using an underlying Gaussian Process (GP) model in an experimental study on postcryopreservation membrane integrity response to a range of elevated temperature conditions. We tailored this technique for the current investigation and developed an algorithm which enabled identification of the sampling locations for the experiments in order to obtain the highest information return about the process from a limited size sample set. We applied this algorithm in the experimental study investigating the effects of severe temperature elevation (ranging from -40 to 20°C) over a short term event (48 hours) on the postcryopreservation membrane integrity of Mesenchymal Stem Cells (MSCs) derived from human bone marrow. The algorithm showed excellent performance by selecting the locations which maximized the reduction of variance of the process response estimate. An approximating GP model developed from this experimental data shows that the elevated temperatures during cryopreservation have an imminent detrimental effect on cell integrity.
physiological models, biomembranes, bone, cellular biophysics, Gaussian processes, learning (artificial intelligence), medical computing, orthopaedics, temperature -40 degC to 20 degC, Gaussian process model, active learning, postcryopreservation cell membrane integrity experiments, biological cell cryopreservation, stem cell cryostorage, regenerative medicine, optimal cryopreservation processing, elevated temperature conditions, mesenchymal Stem Cells, human bone marrow, imminent detrimental effect, cell integrity, Temperature distribution, Kernel, Data models, Biomembranes, Vectors, Stem cells, temperature elevation., Active learning, Gaussian process, postcryopreservation cell integrity, stem cells
F. Barry, M. J. Murphy, D. Fay, M. Norkus, G. Olaighin and L. Kilmartin, "On the Application of Active Learning and Gaussian Processes in Postcryopreservation Cell Membrane Integrity Experiments," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 846-856, 2012.