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Dynamic stabilization in the PU1-GATA1 circuit using a model with time-dependent kinetic change
Jay L Michaels1, Vincent Naudot, Larry S Liebovitch
1Department of Psychology, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA. jmicha20@fau.edu
Bulletin of Mathematical Biology
|January 7, 2011
Summary
Mathematical modeling of blood stem cell differentiation reveals that dynamic changes in PU.1 and GATA1 gene feedback loops, not constant parameters, create new cellular behaviors and alter gene expression, impacting cell phenotype.
Area of Science:
- Systems Biology
- Molecular Genetics
- Computational Biology
Background:
- The PU.1 and GATA1 genes are crucial for blood stem cell differentiation.
- Their regulation involves complex feedback loops: self-excitation for each gene and cross-inhibition between them.
- Previous models assumed static parameters, limiting understanding of dynamic biological processes.
Purpose of the Study:
- To investigate the impact of time-dependent parameter changes on the PU.1 and GATA1 gene regulatory network.
- To explore novel phenomena arising from dynamic feedback loops in cellular differentiation.
- To understand how perturbations in these feedback loops affect gene expression and cell phenotype.
Main Methods:
- Development of a mathematical model simulating the dynamical interaction between PU.1 and GATA1 genes.
- Incorporation of discrete, time-dependent parameter variations instead of constant, steady-state values.
- Analysis of the model's output to identify emergent behaviors and changes in system dynamics.
Main Results:
- Models with time-dependent parameters exhibited new phenomena, including novel limit cycles and basins of attraction.
- These dynamic behaviors were absent in models utilizing constant parameter values.
- Constant feedback levels favored dominance by either the self-exciting or cross-inhibiting gene.
Conclusions:
- Dynamic changes in the PU.1 and GATA1 feedback loops can significantly alter gene expression and cell phenotype.
- Time-dependent parameter modeling offers new insights into the mechanisms of cellular differentiation.
- This approach may be applicable to understanding other complex gene regulatory systems.
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