University of California, Irvine
Randomness and stochasticity are prevalent features of biological phenomena. Usually, experiments are repeated multiple times and the average results are taken to be indicative of the truth. However, I will show that the randomness in the data due to the experimental variation is not “just noise”, but rather a rich source of information. We will focus on retinoic acid signaling in the developing zebrafish hindbrain. There it will be demonstrated using stochastic differential equation models in via DifferentialEquations.jl that the protein crabp2a is able to control the level of biological noise without changing the mean values, a feature we will call mean-independent noise attenuation. We will describe how a very specific level of randomness in the retinoic acid signal is required for hindbrain segmentation to properly occur, and show that mean-independent noise attenuation causes crabp2a to act as a control knob to achieve the appropriate noise levels and allows the rhombomere boundaries to sharpen. Additionally, we will show that this information about the internal biochemical randomness constrains the possible models of hindbrain signaling networks, and use this to mathematically deduce the source of the random variation. Together, the audience should leave with an understanding that randomness may be far more insightful than the average behavior.
I am a mathematician and theoretical biologist at the University of California, Irvine. My programming language of choice is Julia and I am the lead developer of the JuliaDiffEq organization dedicated to solving differential equations (and includes the package DifferentialEquations.jl). My research is in time stepping methods for solving stochastic differential equations (SDEs) and applications to stochastic partial differential equations (SPDEs) which model biological development.
