| Popis: |
Part I: A remarkable feature of life on Earth is that despite the apparent observed diversity, the underlying chemistry that powers it is highly conserved. From the level of the nucleobases, through the amino acids and proteins they encode, to the metabolic pathways of chemical reactions catalyzed by these proteins, biology often utilizes identical solutions in vastly disparate organisms. This universality is intriguing as it raises the question of whether these recurring features exist because they represent some truly optimal solution to a given problem in biology, or whether they simply exist by chance, having arisen very early in life's history. In this project we consider the universality of metabolism { the set of chemical reactions providing the energy and building blocks for cells to grow and divide. We develop an algorithm to construct the complete network of all possible biochemically feasible compounds and reactions, including many that could have been utilized by life but never were. Using this network we investigate the most highly conserved piece of metabolism in all of biology, the trunk pathway of glycolysis. We design a method which allows a comparison between the large number of alternatives to this pathway and which takes into account both thermodynamic and biophysical constraints, finding evidence that the existing version of this pathway produces optimal metabolic fluxes under physiologically relevant intracellular conditions. We then extend our method to include an evolutionary simulation so as to more fully explore the biochemical space. Part II: Studies of population dynamics have a long history and have been used to understand the properties of complex networks of ecological interactions, extinction events, biological diversity and the transmission of infectious disease. One aspect of these models that is known to be of great importance, but one which nonetheless is often neglected, is spatial structure. Various classes of models have been proposed with each allowing different insights into the role space plays. Here we use a lattice-based approach. Motivated by gene transfer and parasite dynamics, we extend the well-studied contact process of statistical physics to include multiple levels. Doing so generates a simple model which captures in a general way the most important features of such biological systems: spatial structure and the inclusion of both vertical as well as horizontal transmission. We show that spatial structure can produce a qualitatively new effect: a coupling between the dynamics of the infection and of the underlying host population, even when the infection does not affect the fitness of the host. Extending the model to an arbitrary number of levels, we find a transition between regimes where both a finite and infinite number of parasite levels are sustainable, and conjecture that this transition is related to the roughening transition of related surface growth models. |
