Neural tube patterning: From a minimal model for rostrocaudal patterning toward an integrated 3D model
Max Brambach,
Ariane Ernst,
Sara Nolbrant,
Janelle Drouin-Ouellet,
Agnete Kirkeby,
Malin Parmar,
Victor Olariu
Affiliations
Max Brambach
Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, 223 63, Sweden; Corresponding author
Ariane Ernst
Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, 223 63, Sweden
Sara Nolbrant
Departments of Experimental Medical Science and Clinical Sciences, Wallenberg Neuroscience Center, and Lund Stem Cell Center, Lund University, 221 84 Lund, Sweden
Janelle Drouin-Ouellet
Faculté de Pharmacie, Université de Montréal, Montréal, QC, H3T 1J4, Canada
Agnete Kirkeby
Departments of Experimental Medical Science and Clinical Sciences, Wallenberg Neuroscience Center, and Lund Stem Cell Center, Lund University, 221 84 Lund, Sweden
Malin Parmar
Departments of Experimental Medical Science and Clinical Sciences, Wallenberg Neuroscience Center, and Lund Stem Cell Center, Lund University, 221 84 Lund, Sweden
Victor Olariu
Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, 223 63, Sweden; Corresponding author
Summary: Rostrocaudal patterning of the neural tube is a defining event in vertebrate brain development. This process is driven by morphogen gradients which specify the fate of neural progenitor cells, leading to the partitioning of the tube. Although this is extensively studied experimentally, an integrated view of the genetic circuitry is lacking. Here, we present a minimal gene regulatory model for rostrocaudal patterning, whose tristable topology was determined in a data-driven way. Using this model, we identified the repression of hindbrain fate as promising strategy for the improvement of current protocols for the generation of dopaminergic neurons. Furthermore, we combined our model with an established minimal model for dorsoventral patterning on a realistic 3D neural tube and found that key features of neural tube patterning could be recapitulated. Doing so, we demonstrate how data and models from different sources can be combined to simulate complex in vivo processes.
