Intelligent data analysis to model and understand live cell time-lapse sequences.
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.


OBJECTIVE
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.
School of Information Systems Computing and Mathematics, Brunel University, West London, UK. allan.tucker@brunel.ac.uk
Allan
A
Paterson
M
M
Ashtari
D
D
Ribé
G
G
Stenbeck
A
A
Tucker
D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D006801:Humans; D008390:Markov Chains; D008490:Medical Informatics / Q000379:methods*; D008853:Microscopy / Q000295:instrumentation / Q000379:methods; D011336:Probability; D059008:Time-Lapse Imaging / Q000295:instrumentation / Q000379:methods*