A computational model of the effect of gene misexpression on the development of cortical areas

Brain function depends on the specialisation of brain areas. In the murine cerebral cortex, the development of these areas depends on the coordinated expression of several genes in precise spatial patterns in the telencephalon during embryogenesis. Manipulating the expression of these genes during development alters the positions and sizes of cortical areas in the adult. Qualitative data also show that these genes regulate each other's expression during development so that they form a regulatory network with many feedback loops. However, it is currently unknown which regulatory interactions are critical to generating the correct expression patterns to lead to normal cortical development. Here, we formalise the relationships inferred from genetic manipulations into computational models. We simulate many different networks potentially consistent with the experimental data and show that a surprising diversity of networks produce similar results. This demonstrates that existing data cannot uniquely specify the network. We conclude by suggesting experiments necessary to constrain the model and help identify and understand the true structure of this regulatory network

Analysis of the growth cone turning assay for studying axon guidance

The "pipette" or "growth cone turning" assay is widely used for studying how axons respond to diffusible guidance cues in their environment. However, little quantitative analysis has been presented of the gradient shapes produced by this assay, or how they depend on parameters of the assay. Here we used confocal microscopy of fluorescent gradients to characterize these shapes in 3 dimensions. We found that the shape, and more specifically the concentration at the position usually occupied by the growth cone in this assay, varied in sometimes unexpected ways with the molecular weight of the diffusible factor, charge, pulse duration and pulse frequency. These results suggest that direct observation of the gradient of the particular guidance factor under consideration may be necessary to quantitatively determine the signal to which the growth cone is responding

A boolean model of the gene regulatory network underlying mammalian cortical area development

The cerebral cortex is divided into many functionally distinct areas. The emergence of these areas during neural development is dependent on the expression patterns of several genes. Along the anterior-posterior axis, gradients of Fgf8, Emx2, Pax6, Coup-tfi, and Sp8 play a particularly strong role in specifying areal identity. However, our understanding of the regulatory interactions between these genes that lead to their confinement to particular spatial patterns is currently qualitative and incomplete. We therefore used a computational model of the interactions between these five genes to determine which interactions, and combinations of interactions, occur in networks that reproduce the anterior-posterior expression patterns observed experimentally. The model treats expression levels as Boolean, reflecting the qualitative nature of the expression data currently available. We simulated gene expression patterns created by all 1.68 x 10(7) possible networks containing the five genes of interest. We found that only 0.1% of these networks were able to reproduce the experimentally observed expression patterns. These networks all lacked certain interactions and combinations of interactions including auto-regulation and inductive loops. Many higher order combinations of interactions also never appeared in networks that satisfied our criteria for good performance. While there was remarkable diversity in the structure of the networks that perform well, an analysis of the probability of each interaction gave an indication of which interactions are most likely to be present in the gene network regulating cortical area development. We found that in general, repressive interactions are much more likely than inductive ones, but that mutually repressive loops are not critical for correct network functioning. Overall, our model illuminates the design principles of the gene network regulating cortical area development, and makes novel predictions that can be tested experimentally