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How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex.

Setty Y, Chen CC, Secrier M, Skoblov N, Kalamatianos D, Emmott S - BMC Syst Biol (2011)

Bottom Line: Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program.Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies.This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Science Laboratory, Microsoft Research, Cambridge, CB3 0FB, UK. yaki.setty@gmail.com

ABSTRACT

Background: Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process.

Results: The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration.

Conclusions: We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

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Cellular and population dynamics in neural migration simulations. A. Distribution of multipolar (blue) and bipolar (red) neurons over time (averaged over 10 different simulations). B. Zone occupancy as function of time (VZ/SVZ blue), IZ (green) and CP (red) (averaged over 10 different simulations). C: Migration stage over time as determined by the path formed by neuronal movement (Solid line: mathematical analysis of the migration data using in-vivo parameters (from [24]) multipolar population (blue) bipolar population (red). Circles: mathematical analysis of the migration data using in-silico parameters: multipolar population (green) and bipolar population (purple). D: Migration stage as a function of velocity in X and Y directions, multipolar (blue) and bipolar (red).
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Figure 3: Cellular and population dynamics in neural migration simulations. A. Distribution of multipolar (blue) and bipolar (red) neurons over time (averaged over 10 different simulations). B. Zone occupancy as function of time (VZ/SVZ blue), IZ (green) and CP (red) (averaged over 10 different simulations). C: Migration stage over time as determined by the path formed by neuronal movement (Solid line: mathematical analysis of the migration data using in-vivo parameters (from [24]) multipolar population (blue) bipolar population (red). Circles: mathematical analysis of the migration data using in-silico parameters: multipolar population (green) and bipolar population (purple). D: Migration stage as a function of velocity in X and Y directions, multipolar (blue) and bipolar (red).

Mentions: Simulation results and experimental observations of neuronal population distribution were in general agreement. The model, consistent with histology (Figure 2B), generated a narrow band of progenitor glial cells at the VZ/SVZ boundary, a sparse occupancy of neurons in the IZ and neuron accumulation at the cortical surface (CP). The multipolar population dominated the earlier stages of migration (blue) and gradually adopted a bipolar fate (red) (Figure 3A). Similarly, the occupancy of neurons in the different zones of the simulated and experimental cortex cross-section showed a gradual migration over time. At the early developmental stages the majority of neurons positioned at the VZ/SVZ (blue). As the simulation advances the neurons rapidly move to the IZ (green) and then accumulate at the CP (red) (Figure 3B). At the end of the simulation 77% of the neuronal population was bipolar, 14% multipolar and 9% radial glial cells. The cortex occupancy in the simulated cross-section showed 9% of cells occupying the VZ/SVZ zones, 11% the IZ and 80% the CP. Experimental observations report a similar population distribution in the mouse cortex [24]: 75% bipolar neurons, 10% multipolar and 10% glial cell (5% unclassified), with zone occupancies of 7%, 15% and 78% at the ZV/SVZ, IZ and CP zones respectively.


How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex.

Setty Y, Chen CC, Secrier M, Skoblov N, Kalamatianos D, Emmott S - BMC Syst Biol (2011)

Cellular and population dynamics in neural migration simulations. A. Distribution of multipolar (blue) and bipolar (red) neurons over time (averaged over 10 different simulations). B. Zone occupancy as function of time (VZ/SVZ blue), IZ (green) and CP (red) (averaged over 10 different simulations). C: Migration stage over time as determined by the path formed by neuronal movement (Solid line: mathematical analysis of the migration data using in-vivo parameters (from [24]) multipolar population (blue) bipolar population (red). Circles: mathematical analysis of the migration data using in-silico parameters: multipolar population (green) and bipolar population (purple). D: Migration stage as a function of velocity in X and Y directions, multipolar (blue) and bipolar (red).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3198702&req=5

Figure 3: Cellular and population dynamics in neural migration simulations. A. Distribution of multipolar (blue) and bipolar (red) neurons over time (averaged over 10 different simulations). B. Zone occupancy as function of time (VZ/SVZ blue), IZ (green) and CP (red) (averaged over 10 different simulations). C: Migration stage over time as determined by the path formed by neuronal movement (Solid line: mathematical analysis of the migration data using in-vivo parameters (from [24]) multipolar population (blue) bipolar population (red). Circles: mathematical analysis of the migration data using in-silico parameters: multipolar population (green) and bipolar population (purple). D: Migration stage as a function of velocity in X and Y directions, multipolar (blue) and bipolar (red).
Mentions: Simulation results and experimental observations of neuronal population distribution were in general agreement. The model, consistent with histology (Figure 2B), generated a narrow band of progenitor glial cells at the VZ/SVZ boundary, a sparse occupancy of neurons in the IZ and neuron accumulation at the cortical surface (CP). The multipolar population dominated the earlier stages of migration (blue) and gradually adopted a bipolar fate (red) (Figure 3A). Similarly, the occupancy of neurons in the different zones of the simulated and experimental cortex cross-section showed a gradual migration over time. At the early developmental stages the majority of neurons positioned at the VZ/SVZ (blue). As the simulation advances the neurons rapidly move to the IZ (green) and then accumulate at the CP (red) (Figure 3B). At the end of the simulation 77% of the neuronal population was bipolar, 14% multipolar and 9% radial glial cells. The cortex occupancy in the simulated cross-section showed 9% of cells occupying the VZ/SVZ zones, 11% the IZ and 80% the CP. Experimental observations report a similar population distribution in the mouse cortex [24]: 75% bipolar neurons, 10% multipolar and 10% glial cell (5% unclassified), with zone occupancies of 7%, 15% and 78% at the ZV/SVZ, IZ and CP zones respectively.

Bottom Line: Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program.Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies.This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Science Laboratory, Microsoft Research, Cambridge, CB3 0FB, UK. yaki.setty@gmail.com

ABSTRACT

Background: Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process.

Results: The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration.

Conclusions: We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

Show MeSH
Related in: MedlinePlus