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