Interactions and tradeoffs between cell recruitment, proliferation, and differentiation affect CNS regeneration.
Regeneration of central nervous system (CNS) lesions requires movement of progenitor cells and production of their differentiated progeny. Although damage to the CNS clearly promotes these two processes, the interplay between these complex events and how it affects a response remains elusive. Here, we use spatial stochastic modeling to show that tradeoffs arise between production and recruitment during regeneration. Proper spatial control of cell cycle timing can mitigate these tradeoffs, maximizing recruitment, improving infiltration into the lesion, and reducing wasteful production outside of it. Feedback regulation of cell lineage dynamics alone however leads to spatial defects in cell recruitment, suggesting a novel, to our knowledge, hypothesis for the aggregation of cells to the periphery of a lesion in multiple sclerosis. Interestingly, stronger chemotaxis does not correct this aggregation and instead, substantial random cell motions near the site of the lesion are required to improve CNS regeneration.
Center for Mathematical and Computational Biology, Center for Complex Biological Systems, Department of Mathematics, University of California, Irvine, California.
William R
WR
Holmes
Center for Mathematical and Computational Biology, Center for Complex Biological Systems, Department of Mathematics, University of California, Irvine, California. Electronic address: qnie@math.uci.edu.
Qing
Q
Nie
D000818:Animals; D002449:Cell Aggregation / Q000502:physiology; D002453:Cell Cycle; D002454:Cell Differentiation / Q000502:physiology; D002465:Cell Movement; D049109:Cell Proliferation; D002490:Central Nervous System / Q000503:physiopathology*; D002633:Chemotaxis / Q000502:physiology; D003198:Computer Simulation; D025461:Feedback, Physiological; D051379:Mice; D008959:Models, Neurological; D009103:Multiple Sclerosis / Q000503:physiopathology; D009416:Nerve Regeneration / Q000502:physiology*; D055495:Neurogenesis; D013269:Stochastic Processes; D013997:Time Factors