A Boolean Model of the Gene Regulatory Network Underlying Mammalian Cortical Area Development

Abstract

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 possible networks containing the five genes of interest. We found that only 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.

Pallio-Pallial Tangential Migrations and Growth Signaling: New Scenario for Cortical Evolution?

“…In recent years it has been shown by gene mapping studies at early stages of development that mouse pallial regionalization is preceded by mutually opposed crisscrossing pairs of gradiental transcription factor expression domains oriented in orthogonal spatial directions. Genes such as Emx2, Pax6, Sp8, and CoupTF1 , active during the critical early patterning stages, show gradiental patterns of expression oriented either rostrocaudally or mediolaterally across the whole pallium, with Emx2 predominating rostrally, Pax6 caudally, Sp8 dorsally, and CoupTF1 ventrally [Sur and Rubenstein, 2005;Mallamaci and Stoykova, 2006;Rash and Grove, 2006;O'Leary and Sahara, 2008;Borello and Pierani, 2010;Giacomantonio and Goodhill, 2010]. Additional gradients and secondary differential regional gene expression patterns are no doubt present as well [Bedogni et al, 2010].…”

Finding and Analyzing the Minimum Set of Driver Nodes in Control of Boolean Networks

“…Gene regulatory networks are models of sets of interactions among genes, the analysis of which is one of the key topics in systems biology and theoretical biology. BNs have been widely used to model the regulatory networks in mammalian cortical area [31], T cell large granular lymphocyte leukemia [75], mammalian cell cycle [26], and to reproduce the wild-type gene expression patterns [8]. Of course, BNs cannot model all aspects of biological systems.…”

Inferring Gene Regulatory Network from Bayesian Network Model Based on Re-sampling

“…The main methods of constructing gene regulatory networks include: temporal Boolean network model [5], the mutual information [6] 216 mutual information associated with the model are coarser, rarely used alone. The linear combination model, the weighted matrix model, the neural network model and differential equations model of processing power to noise is relatively weak.…”