Living organisms throughout evolution have developed desired properties, such as the ability
of maintaining functionality despite changes in the environment or their inner structure, the
formation of functional modules, from metabolic pathways to organs, and most essentially
the capacity to adapt and evolve in a process called natural selection. It can be observed in
the metabolic networks of modern organisms that many key pathways such as the citric acid
cycle, glycolysis, or the biosynthesis of most amino acids are common to all of them.
Understanding the evolutionary mechanisms behind this development of complex biological
systems is an intriguing and important task of current research in biology as well as artificial
life. Several competing hypotheses for the formation of metabolic pathways and the mecha-
nisms that shape metabolic networks have been discussed in the literature, each of which finds
support from comparative analysis of extant genomes. However, while being powerful tools
for the investigation of metabolic evolution, these traditional methods do not allow to look
back in evolution far enough to the time when metabolism had to emerge and evolve to the
form we can observe today. To this end, simulation studies have been introduced to discover
the principles of metabolic evolution and the sources for the emergence of metabolism prop-
erties. These approaches differ considerably in the realism and explicitness of the underlying
models. A difficult trade-off between realism and computational feasibility has to be made
and further modeling decisions on many scales have to be taken into account, requiring the
combination of knowledge from different fields such as chemistry, physics, biology and last
but not least also computer science.
In this thesis, a novel computational model for the in silico evolution of early metabolism
is introduced. It comprises all the components on different scales to resemble a situation of
evolving metabolic protocells in an RNA-world. Therefore, the model contains a minimal
RNA-based genetics and an evolving metabolism of catalytic ribozymes that manipulate a
rich underlying chemistry. To allow the metabolic organization to escape from the confines
of the chemical space set by the initial conditions of the simulation and in general an open-
ended evolution, an evolvable sequence-to-function map is used. At the heart of the metabolic
subsystem is a graph-based artificial chemistry equipped with a built-in thermodynamics. The
generation of the metabolic reaction network is realized as a rule-based stochastic simulation.
The necessary reaction rates are calculated from the chemical graphs of the reactants on
the fly. The selection procedure among the population of protocells is based on the optimal metabolic yield of the protocells, which is computed using flux balance analysis.
The introduced computational model allows for profound investigations of the evolution of
early metabolism and the underlying evolutionary mechanisms. One application in this thesis
is the study of the formation of metabolic pathways. Therefore, four established hypothe-
ses, namely the backwards evolution, forward evolution, patchwork evolution and the shell
hypothesis, are discussed within the realms of this in silico evolution study. The metabolic
pathways of the networks, evolved in various simulation runs, are determined and analyzed
in terms of their evolutionary direction. The simulation results suggest that the seemingly
mutually exclusive hypotheses may well be compatible when considering that different pro-
cesses dominate different phases in the evolution of a metabolic system. Further, it is found
that forward evolution shapes the metabolic network in the very early steps of evolution. In
later and more complex stages, enzyme recruitment supersedes forward evolution, keeping a
core set of pathways from the early phase. Backward evolution can only be observed under
conditions of steady environmental change. Additionally, evolutionary history of enzymes
and metabolites were studied on the network level as well as for single instances, showing a
great variety of evolutionary mechanisms at work.
The second major focus of the in silico evolutionary study is the emergence of complex system
properties, such as robustness and modularity. To this end several techniques to analyze the
metabolic systems were used. The measures for complex properties stem from the fields of
graph theory, steady state analysis and neutral network theory. Some are used in general
network analysis and others were developed specifically for the purpose introduced in this
work. To discover potential sources for the emergence of system properties, three different
evolutionary scenarios were tested and compared. The first two scenarios are the same as
for the first part of the investigation, one scenario of evolution under static conditions and
one incorporating a steady change in the set of ”food” molecules. A third scenario was
added that also simulates a static evolution but with an increased mutation rate and regular
events of horizontal gene transfer between protocells of the population. The comparison of all
three scenarios with real world metabolic networks shows a significant similarity in structure
and properties. Among the three scenarios, the two static evolutions yield the most robust
metabolic networks, however, the networks evolved under environmental change exhibit their
own strategy to a robustness more suited to their conditions. As expected from theory,
horizontal gene transfer and changes in the environment seem to produce higher degrees
of modularity in metabolism. Both scenarios develop rather different kinds of modularity,
while horizontal gene transfer provides for more isolated modules, the modules of the second
scenario are far more interconnected.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:11359 |
| Date | 10 October 2011 |
| Creators | Ullrich, Alexander |
| Contributors | Stadler, Peter, Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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