Matthew Bedder

Papers

Z. Zhang, M. Bedder, S.L. Smith, D. Walker, S. Shabir, J. Southgate Automated Motion Analysis of Adherent Cells in Monolayer Culture Proceedings of the 10th International Conference on Information Processing in Cells and Tissues (2015)
This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP), acquired over a 20 hour period. Subsequent analysis, comprising feature extraction, demonstrated the ability of the technique to successfully separate the modulated classes of cell.
@incollection{
	zhang2015Automated,
	title = {Automated Motion Analysis of Adherent Cells in Monolayer Culture},
	author = {Zhang, Zhen and Bedder, Matthew and Smith, Stephen L. and Walker, Dawn and Shabir, Saqib and Southgate, Jennifer},
	booktitle = {Proceedings of the 10th International Conference on Information Processing in Cells and Tissues},
	publisher = {Springer International Publishing},
	year = {2015},
	editor = {Lones, Michael and Tyrrell, Andy and Smith, Stephen and Fogel, Gary},
	pages = {185-194},
	series = {Lecture Notes in Computer Science},
	volume = {9303},
	doi = {10.1007/978-3-319-23108-2_16}
}
	


Journals

Z. Zhang, M. Bedder, S.L. Smith, D. Walker, S. Shabir, J. Southgate Characterization and Classification of Adherent Cells in Monolayer Culture using Automated Tracking and Evolutionary Algorithms BioSystems (2016)
This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS), acquired over a 24 hour period. Subsequent analysis following feature extraction demonstrated the ability of the technique to successfully separate the modulated classes of cell using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the separation. Our approach not only provides non-biased and parsimonious insight into modulated class behaviors, but can be extracted as mathematical formulae for the parameterization of computational models.
@article{
	zhang2016characterization,
	title = {Characterization and Classification of Adherent Cells in Monolayer Culture using Automated Tracking and Evolutionary Algorithms},
	author = {Zhang, Zhen and Bedder, Matthew and Smith, Stephen L. and Walker, Dawn and Shabir, Saqib and Southgate, Jennifer},
	journal = {Biosystems},
	year = {2016},
	note = {Accepted Manuscript},
	doi = {doi:10.1016/j.biosystems.2016.05.009}
}
	
S.L. Smith, M.A. Lones, M. Bedder, J.E. Alty, J. Cosgrove, R.J. Maguire, M.E. Pownall, D. Ivanoiu, C. Lyle, A. Cording, C.J.H. Elliott Computational approaches for understanding the diagnosis and treatment of Parkinson’s disease IET Systems Biology (2015)
This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way.
@article{
	smith2015Computational,
	title = {Computational approaches for understanding the diagnosis and treatment of Parkinson's disease},
	author = {Smith, Stephen L. and Lones, Michael A. and Alty, Jane E. and Cosgrove, Jeremy and Maguire, Richard J. and Pownall, Mary Elizabeth and Ivanoiu, Diana and Lyle, Camille and Cording, Amy and Elliott, Christopher J.H.},
	journal = {IET Systems Biology},
	publisher = {Institution of Engineering and Technology},
	year = {2015},
	month = {August},
	doi = {10.1049/iet-syb.2015.0030}
}