Morphogenesis and Growth Driven by Selection of Dynamical Properties

摘要

Organisms are understood to be complex adaptive systems that evolved to thrive in hostile environments. Though widely studied, the phenomena of organism development and growth, and their relationship to organism dynamics is not well understood. Indeed, the large number of components, their interconnectivity, and complex system interactions all obscure our ability to see, describe, and understand the functioning of biological organisms.;Here we take a synthetic and computational approach to the problem, abstracting the organism as a cellular automaton. Such systems are discrete digital models of real-world environments, making them more accessible and easier to study then their physical world counterparts. In such simplified synthetic models, we find that the structure of the cellular network greatly impacts the dynamics of the organism as a whole. In the physical world, for example, the network property wherein some cells depend on phosphorus produces the cyclical boom-bust dynamics of algae on the surface of a pond. Using techniques of synthetic biology and cellular automata, such local properties can be abstractly specified, and the long-term, system-wide, and dynamical consequences of localized assumptions can be carefully explored.;This thesis explores the potential impacts of Darwinian selection of dynamical properties on long term cellular differentiation and organism growth. The focus here is on the relationship between organism homogeneity (or heterogeneity) and the dynamical properties of robustness, adaptivity, and chromatic symmetry. This dissertation applies an experimental approach to test the following three hypotheses: (1) cellular differentiation increases the expected robustness in an organism's dynamics, (2) cellular differentiation leads to more uniform adaptivity as the organism grows, and (3) for organisms with symmetry, growth by segment elongation is more likely than growth by segment reduplication. To explore these hypotheses, we address several obstacles in the experimental study of dynamical systems, including computational time limits and big data.