MicroMod – an L-systems approach to neuron modelling

Neurons and their support cells have a branching structure caused by their growth processes. The possibility of developing models of such structures can yield great rewards in our understanding of neural growth and function, and our ability to identify different types of neuron. Growth processes in other contexts have been successfully modelled as Lindenmayer systems, but little work has appeared using this approach for neural tissue. In this paper we present a new model of neuron growth using L-systems. MicroMod is a web-based program that allows models to be easily built using a comprehensive set of adjustable parameters. We illustrate some of the techniques facilitated by MicroMod, using the example of microglia. Simulated cells are compared with real cells, and then used to identify clusters that may provide a new classification of cell types. We suggest that the ability to link cluster description back to the parameters of the L-systems model may provide new insights into disease progression.

[1]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[2]  Aristid Lindenmayer,et al.  Mathematical Models for Cellular Interactions in Development , 1968 .

[3]  Walter J. Bock,et al.  Philosophical Foundations of Classical Evolutionary Classification , 1973 .

[4]  C F Tyner,et al.  The naming of neurons: applications of taxonomic theory to the study of cellular populations. , 1975, Brain, behavior and evolution.

[5]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[6]  T. Poggio,et al.  Retinal ganglion cells: a functional interpretation of dendritic morphology. , 1982, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[7]  W. B. Marks,et al.  A fractal analysis of cell images , 1989, Journal of Neuroscience Methods.

[8]  Benoit B. Mandelbrot,et al.  A Fractal’s Lacunarity, and how it can be Tuned and Measured , 1994 .

[9]  Kishore Mulchandani Morphological modeling of neurons , 1995 .

[10]  W. B. Marks,et al.  Fractal methods and results in cellular morphology — dimensions, lacunarity and multifractals , 1996, Journal of Neuroscience Methods.

[11]  L. da Fontoura Costa,et al.  Automatic classification of retinal ganglion cells , 1996, Proceedings II Workshop on Cybernetic Vision.

[12]  T. Velte,et al.  Automatic characterization and classification of ganglion cells from the salamander retina , 1999, The Journal of comparative neurology.

[13]  G. Baltuch,et al.  Microglia as mediators of inflammatory and degenerative diseases. , 1999, Annual review of neuroscience.

[14]  G A Ascoli,et al.  Progress and perspectives in computational neuroanatomy , 1999, The Anatomical record.

[15]  P. Falkai,et al.  Evidence for activation of microglia in patients with psychiatric illnesses , 1999, Neuroscience Letters.

[16]  Luciano da Fontoura Costa,et al.  Shape Analysis and Classification: Theory and Practice , 2000 .

[17]  N. Berman,et al.  Regional distribution of cortical microglia parallels that of neurofibrillary tangles in Alzheimer's disease , 2000, Neuroscience Letters.

[18]  H F Jelinek,et al.  Use of fractal theory in neuroscience: methods, advantages, and potential problems. , 2001, Methods.

[19]  G. Elston,et al.  Dendritic branching patterns of pyramidal cells in the visual cortex of the new world marmoset monkey, with comparative notes on the old world macaque monkey , 2001 .

[20]  Behnam Badie,et al.  Role of microglia in glioma biology , 2001, Microscopy research and technique.

[21]  T N Behar,et al.  Analysis of fractal dimension of O2A glial cells differentiating in vitro. , 2001, Methods.

[22]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.