Robustness and Tunability of Biological Oscillations

Abstract

Biological systems must be able to resist and adapt to environmental changes, but in many cases, we lack a complete understanding of the underlying mechanisms that define their response. It is challenging to pinpoint such mechanisms in periodic processes, known as biological oscillations, due to the complex interaction of their constituent parts. This complexity hinders our ability to treat certain diseases and limits our capacity to engineer resilient and responsive biological oscillators. Through a series of collaborative projects, I have researched both experimentally and computationally the mechanisms that allow biological oscillators to function and respond across various conditions. Using cell-free Xenopus laevis frog egg extracts, I explored how cell cycle oscillations react to changes in cytoplasmic density and temperature. In both cases, the oscillatory period was robust to changes close to the physiological range. However, away from physiological conditions, the period increased considerably. We determined the mechanisms driving this response and provided valuable insights into which components are most sensitive to parametric perturbations. In addition, I studied which features of an oscillatory network define its period and amplitude tunability by analyzing thousands of ordinary differential equation models. I investigated how additional feedback loops modify the distribution of functional outputs in an oscillator. The type of feedback added (positive or negative) and its coherence (whether it contains both positive and negative interactions) define the resulting tunability. This knowledge provides a set of principles for engineering oscillatory networks de novo and interpreting mathematical models of biological oscillators. Finally, I studied the role of light on the amplitude of human circadian rhythms by analyzing computational models. I discussed the potential of circadian amplitude as a target for clinical intervention and future experimentation. Overall, the results of this dissertation contribute towards a systems-level understanding of how biological oscillations respond to multi-parameter perturbations and the mechanisms that drive this response.