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Period Robustness Analysis of Minimal Models for Biochemical Oscillators

Caicedo-Casso, Angelica G.
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427980229

Abstract Details

2015, PhD, University of Cincinnati, Arts and Sciences: Mathematical Sciences.
Mathematical modeling is a sophisticated method that facilitates the analysis and understanding of complex biological systems, for example, oscillatory systems. In biology, oscillatory systems are found in numerous species ranging from calcium oscillations to circadian rhythms. These oscillators, of autonomous nature, contain complex feedback mechanisms ruled by molecular interactions that determine the physiology and enable spontaneous oscillations. Although previous results using both experimental and theoretical approaches have revealed core molecular network topologies and conditions that allow autonomous oscillations, many important questions remain elusive. In this thesis, we investigate five different minimal models that may underlie fundamental molecular processes in biological oscillatory systems. We focus on dynamical differences and period maintenance of the models. In particular, we introduce two types of noises into these minimal models and perform numerical bifurcation and period sensitivity analyses. Our results demonstrate that small wiring modifications lead to substantial dynamical changes and the oscillatory domain is enlarged in models that involve a positive feedback mechanism including a reversible reaction. Additionally, the outcomes from sensitivity analysis in the presence of external or internal noises reveal different rankings in the hierarchy of period robustness of the five minimal models. Ranking of the models is determined by the number of highly sensitive parameters, network topology and/or noise strength. Finally, we demonstrate that minimal models including positive feedback via autocatalysis are more robust than those where positive feedback is exerted through inhibition of degradation regardless of the noise type. This investigation will be useful to analyze fundamental molecular mechanisms of different biological oscillators. We admit that characteristics of minimal models may be too simple for modeling a detailed behavior of complex biological oscillators. However, our data indicate that these minimal models may be used as building blocks for biochemical oscillators of greater complexity using synthetic biology.
Sookkyung Lim, Ph.D. (Committee Chair)
H Dumas, Ph.D. (Committee Member)
Donald French, Ph.D. (Committee Member)
Christian Hong, Ph.D. (Committee Member)
Benjamin Vaughan, Ph.D. (Committee Member)
131 p.
Applied Mathematics
Robustness; Sensitivity Analysis; Period; Minimal models; Stochasticity; Bifurcation analysis

Recommended Citations

Citations

  • Caicedo-Casso, A. G. (2015). Period Robustness Analysis of Minimal Models for Biochemical Oscillators [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427980229

    APA Style (7th edition)

  • Caicedo-Casso, Angelica. Period Robustness Analysis of Minimal Models for Biochemical Oscillators. 2015. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427980229.

    MLA Style (8th edition)

  • Caicedo-Casso, Angelica. "Period Robustness Analysis of Minimal Models for Biochemical Oscillators." Doctoral dissertation, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427980229

    Chicago Manual of Style (17th edition)

ucin1427980229
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© 2015, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.