Subscribe to RSS
Download PDF
DOI: 10.3414/ME11-02-0041
Intelligent Data Analysis to Model and Understand Live Cell Time-lapse Sequences
Publication History
received:18 November 2011
accepted:27 April 2012
Publication Date:
20 January 2018 (online)
Summary
Background: One important aspect of cellular function, which is at the basis of tissue homeostasis, is the delivery of proteins to their correct destinations. Significant advances in live cell microscopy have allowed tracking of these pathways by following the dynamics of fluorescently labelled proteins in living cells.
Objectives: This paper explores intelligent data analysis techniques to model the dynamic behavior of proteins in living cells as well as to classify different experimental conditions.
Methods: We use a combination of decision tree classification and hidden Markov models. In particular, we introduce a novel approach to “align” hidden Markov models so that hidden states from different models can be cross-compared.
Results: Our models capture the dynamics of two experimental conditions accurately with a stable hidden state for control data and multiple (less stable) states for the experimental data recapitulating the behaviour of particle trajectories within live cell time-lapse data.
Conclusions: In addition to having successfully developed an automated framework for the classification of protein transport dynamics from live cell time-lapse data our model allows us to understand the dynamics of a complex trafficking pathway in living cells in culture.
-
References
- 1 Minoshima S, Cross D. In vivo imaging of axonal transport using MRI: aging and Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2008; 35 (Suppl. 01) S89-S92.
- 2 Meulenbelt I. et al. Clusters of biochemical markers are associated with radiographic subtypes of osteoarthritis (OA) in subject with familial OA at multiple sites. The GARP study. Osteoarthritis Cartilage 2007; 15: 379-385.
- 3 Lippincott-Schwartz J. Dynamics of secretory membrane trafficking. Ann N Y Acad Sci 2004; 1038: 115-124.
- 4 Jaqaman et al Robust single-particle tracking in live-cell time-lapse sequences. Nature Methods 2008; 5: 695-702.
- 5 Sbalzarini IF, Koumoutsakos P. Feature point tracking and trajectory analysis for video imaging in cell biology. Journal of Structural Biology 2005; 151: 182-195.
- 6 Friedman N, Murphy KP, Russell SJ. Proc. of the Annual Conference on Uncertainty in AI 1998; 14: 139-147.
- 7 Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 1989; 77 (02) 257-286.
- 8 Steyer JA, Almers W. Tracking single secretory granules in live chromaffin cells by evanescent-field fluorescence microscopy. Biophysical Journal 1999; 76: 2262-2271.
- 9 Manneville JB. et al. Interaction of the actin cytoskeleton with microtubules regulates secretory organelle movement near the plasma membrane in human endothelial cells. Journal of Cell Science 2003; 116: 3927-3938.
- 10 Bellazzi R, Diomidous M, Sarkar IN, Takabayashi K, Ziegler A, McCray AT. Data Analysis and Data Mining: Current Issues in Biomedical Informatics. Methods Inf Med 2011; 50 (06) 536-544.
- 11 Peek N, Combi C, Tucker A. Biomedical Data Mining. Methods Inf Med 2009; 48 (03) 225-228.
- 12 Tucker A, Vinciotti V, Garway-Heath D, Liu X. A spatio-temporal Bayesian network classifier for understanding visual field deterioration. Artificial Intelligence in Medicine 2005; 34: 163-177.
- 13 Ramoni M, Sebastiani P, Cohen P. Bayesian Clustering by Dynamics. Machine Learning 2002; 47 (01) 91-121.
- 14 North B, Blake A, Isard M, Rittscher J. Learning and Classification of Complex Dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22 (09) 1016-1034.
- 15 Kadous MW. A general architecture for supervised classification of multivariate time series. Technical Report, School of Computer Science & Engineering, University of New South Wales, 1998. Report No.: UNSW-CSE-TR-9806
- 16 Das R. et al. A Hidden Markov Model for Single Particle Tracks Quantifies Dynamic Interactions between LFA-1 and the Actin Cytoskeleton. PLoS ComputBiol 2009; 5 (11) e1000556
- 17 Hamilton JD. Time Series Analysis, Princeton: Princeton University Press 1994.
- 18 Ratnayaka A, Paraoan L, Nelson G, Spiller DG, White MR, Hiscott P. Trafficking of osteonectin by retinal pigment epithelial cells: evidence for basolateral secretion. Int J Biochem Cell Biol 2007; 39: 85-92.
- 19 Steyer JA, Almers W. A real-time view of life within 100 nm of the plasma membrane. Nat Rev Mol Cell Biol 2001; 2: 268-275.
- 20 Kanaan RA, Kanaan LA. Transforming growth factor beta1, bone connection. Med Sci Monit 2006; 12 (08) RA164-169.
- 21 Edlund S. et al. Transforming growth factor-beta-induced mobilization of actin cytoskeleton requires signaling by small GTPases Cdc42 and RhoA. Mol Biol Cell 2002; 13: 902-914.
- 22 Shi Y, Massague J. Mechanisms of TGF-beta signaling from cell membrane to the nucleus. Cell 2003; 113: 685-700.
- 23 Zhang YE. Non-Smad pathways in TGF-beta signaling. Cell Res 2009; 19: 128-139.
- 24 Quinlan JR. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers 1993.
- 25 Jensen FV. Bayesian Networks and Decision Graphs. New York: Springer-Verlag; 2001.
- 26 Qian H. et al. Single particle tracking. Analysis of diffusion and flow in two-dimensional systems. Biophysical Journal 1991; 60: 910-921.
- 27 Ghahramani Z. Learning Dynamic Bayesian Networks, Adaptive Processing of Sequences and Data Structures. In: Giles CL, Gori M. (eds). Lecture Notes in Artificial Intelligence Berlin: Springer-Verlag; 1998: 168-197.
- 28 Gyftodimos E, Flach P. Hierarchical Bayesian Networks: A Probabilistic Reasoning Model for Structured Domains. In: de Jong E, Oates T. (eds) Proceedings of the ICML-2002 Workshop on Development of Representations. July 2002: 23-30.
- 29 Ghahramani Z, Hinton GE. Variational Learning for Switching State-Space Models. Neural Computation 2000; 12 (04) 831-864.
- 30 John GH, Langley P. Estimating continuous distributions in Bayesian classifiers, UAI’95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence. San Francisco: Morgan Kaufmann; 1995.
- 31 Friedman N, Geiger D, Goldszmidt M. Bayesian Network Classifiers. Machine Learning 1997; 29: 131-163.