Literature DB >> 24816612

Cell cycle dynamics: clustering is universal in negative feedback systems.

Nathan Breitsch1, Gregory Moses, Erik Boczko, Todd Young.   

Abstract

We study a model of cell cycle ensemble dynamics with cell-cell feedback in which cells in one fixed phase of the cycle S (Signaling) produce chemical agents that affect the growth and development rate of cells that are in another phase R (Responsive). For this type of system there are special periodic solutions that we call k-cyclic or clustered. Biologically, a k-cyclic solution represents k cohorts of synchronized cells spaced nearly evenly around the cell cycle. We show, under very general nonlinear feedback, that for a fixed k the stability of the k-cyclic solutions can be characterized completely in parameter space, a 2 dimensional triangle T. We show that T is naturally partitioned into k(2) sub-triangles on each of which the k-cyclic solutions all have the same stability type. For negative feedback we observe that while the synchronous solution (k = 1) is unstable, regions of stability of k ≥ 2 clustered solutions seem to occupy all of T. We also observe bi-stability or multi-stability for many parameter values in negative feedback systems. Thus in systems with negative feedback we should expect to observe cyclic solutions for some k. This is in contrast to the case of positive feedback, where we observe that the only asymptotically stable periodic orbit is the synchronous solution.

Mesh:

Year:  2014        PMID: 24816612     DOI: 10.1007/s00285-014-0786-7

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  22 in total

1.  Quantized generation time in mammalian cells as an expression of the cellular clock.

Authors:  R R Klevecz
Journal:  Proc Natl Acad Sci U S A       Date:  1976-11       Impact factor: 11.205

2.  Clustering in cell cycle dynamics with general response/signaling feedback.

Authors:  Todd R Young; Bastien Fernandez; Richard Buckalew; Gregory Moses; Erik M Boczko
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3.  Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes.

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4.  Structure theorems and the dynamics of nitrogen catabolite repression in yeast.

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Journal:  Proc Natl Acad Sci U S A       Date:  2005-04-06       Impact factor: 11.205

5.  Dynamical quorum sensing: Population density encoded in cellular dynamics.

Authors:  Silvia De Monte; Francesco d'Ovidio; Sune Danø; Preben Graae Sørensen
Journal:  Proc Natl Acad Sci U S A       Date:  2007-11-14       Impact factor: 11.205

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Authors:  M Rotenberg
Journal:  J Theor Biol       Date:  1977-05-21       Impact factor: 2.691

7.  Stability of the steady-state size distribution in a model of cell growth and division.

Authors:  K B Hannsgen; J J Tyson
Journal:  J Math Biol       Date:  1985       Impact factor: 2.259

Review 8.  Oscillatory metabolism of Saccharomyces cerevisiae: an overview of mechanisms and models.

Authors:  Pratap R Patnaik
Journal:  Biotechnol Adv       Date:  2003-05       Impact factor: 14.227

9.  Synchronization affector of autonomous short-period-sustained oscillation of Saccharomyces cerevisiae.

Authors:  M Keulers; A D Satroutdinov; T Suzuki; H Kuriyama
Journal:  Yeast       Date:  1996-06-15       Impact factor: 3.239

10.  Real-time luminescence monitoring of cell-cycle and respiratory oscillations in yeast.

Authors:  J Brian Robertson; Chris C Stowers; Erik Boczko; Carl Hirschie Johnson
Journal:  Proc Natl Acad Sci U S A       Date:  2008-11-12       Impact factor: 11.205

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  3 in total

1.  Instability of the steady state solution in cell cycle population structure models with feedback.

Authors:  Balázs Bárány; Gregory Moses; Todd Young
Journal:  J Math Biol       Date:  2018-12-06       Impact factor: 2.259

2.  Noise-induced dispersion and breakup of clusters in cell cycle dynamics.

Authors:  Xue Gong; Gregory Moses; Alexander B Neiman; Todd Young
Journal:  J Theor Biol       Date:  2014-03-30       Impact factor: 2.691

3.  Cell cycle dynamics in a response/signalling feedback system with a gap.

Authors:  Xue Gong; Richard Buckalew; Todd Young; Erik Boczko
Journal:  J Biol Dyn       Date:  2014       Impact factor: 2.179

  3 in total

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