This thesis explores how natural selection acts on organisms such as viruses that have either highly error-prone reproduction or face variable environmental conditions or both. By modeling population dynamics under these conditions, we gain a better understanding of the selective forces at work, both in our simulations and hopefully also in real organisms. With an understanding of the important factors in natural selection we can forecast not only the immediate fate of an existing population but also in what directions such a population might evolve in the future.
We demonstrate that the concept of a quasispecies is relevant to evolution in a neutral fitness landscape. Motivated by RNA viruses such as HIV, we use RNA secondary structure as our model system and find that quasispecies effects arise both rapidly and in realistically small populations. We discover that the evolutionary effects of neutral drift, punctuated equilibrium and the selection for mutational robustness extend to the concept of a quasispecies. In our study of periodic environments, we consider the tradeoffs faced by quasispecies in adapting to environmental change. We develop an analytical model to predict whether evolution favors short-term or long-term adaptation and validate our model through simulation. Our results bear directly on the population dynamics of viruses such as West Nile that alternate between two host species. More generally, we discover that a selective pressure exists under these conditions to fuse or split genes with complementary environmental functions. Lastly, we study the general effects of frequency-dependent selection on two strains competing in a periodic environment. Under very general assumptions, we prove that stable coexistence rather than extinction is the likely outcome. The population dynamics of this system may be as simple as stable equilibrium or as complex as deterministic chaos.
}, address = {1200 East California Boulevard, Pasadena, California 91125}, advisor = {Adami, Christoph Carl}, } @phdthesis{10.7907/X1PD-TN75, author = {Chow, Stephanie Sienyee}, title = {Speciation in Digital Organisms}, school = {California Institute of Technology}, year = {2005}, doi = {10.7907/X1PD-TN75}, url = {https://resolver.caltech.edu/CaltechETD:etd-06062005-171257}, abstract = {Current estimates of the number of species on Earth range from four to forty million total species. Why are there so many species? The answer must include both ecology and evolution. Ecology looks at the interactions between coexisting species, while evolution tracks them through time. Both are required to understand aspects of environments which promote speciation, and which promote species persistence in time.
The explanation for this biodiversity is still not well understood. I argue that resource limitations are a major factor in the evolutionary origin of complex ecosystems with interacting and persistent species. Through experiments with digital organisms in environment with multiple limited resources, I show that these conditions alone can be sufficient to induce differentiation in a population. Moreover, the observed pattern of species number distributions match patterns observed in nature. I develop a simple metric for phenotypic distance for digital organisms, which permits quantitative analysis of similarities within, and differences between species. This enables a clear species concept for digital organisms that may also be applied to biological organisms, thus helping to clarify the biological species concept. Finally, I will use this measurement methodology to predict species and ecosystem stability.
}, address = {1200 East California Boulevard, Pasadena, California 91125}, advisor = {Adami, Christoph Carl}, } @phdthesis{10.7907/08B9-8Q96, author = {Dorn, Evan David}, title = {Universal Biosignatures for the Detection of Life}, school = {California Institute of Technology}, year = {2005}, doi = {10.7907/08B9-8Q96}, url = {https://resolver.caltech.edu/CaltechETD:etd-05272005-071800}, abstract = {My goal is to identify processes of life that leave measurable effects on an organism’s environment, but which are not tied to any particular biochemistry, in order to build a conceptual framework for the search for extraterrestrial life. To this end, I test a pair of phenomena that appear in both terrestrial (biochemical) life and in digital life. Because these two life forms are different and unrelated, any phenomenon measurable in both is suggested to be universal.
The Monomer Abundance Distribution Biosignature (MADB) is any measurement of the relative concentrations of related chemical compounds that cannot be explained by abiotic processes. I observe that living systems synthesize specific chemical compounds at rates that maximize their fitness. As a result, life-bearing environmental samples exhibit compounds in abundance ratios that are clearly not the result of abiotic synthesis because those ratios belie the formation kinetics and thermodynamics that would constrain abiotic synthesis. Often, biotic samples contain high concentrations of specific large, complex molecules that are never seen in abiotic synthesis and cannot be explained unless highly specific catalysts (i.e., enzymes) are present, and energy is expended to drive thermodynamically unfavorable reactions. I catalog this effect as it appears in terrestrial biochemical systems, including amino acids and carboxylic acids, and demonstrate the universality of selection’s action on the monomeric composition of life forms by studying analogous examples in digital life. I suggest how this phenomenon provides a route to the detection of even unusual or unforeseen biochemistries, and give examples of detection methods using pattern-recognition techniques that may allow us to empower an autonomous system with the general ability to detect life forms.
The Layered Trophic Residue Biosignature (LTRB) is any observation of stratification in solute chemistry that indicates metabolic activity by a sequence of diverse communities. When multiple chemical resources are available, natural selection drives adaptive radiation and the formation of specialist phenotypes. Competition ensures that specialists consume resources in decreasing order of energetic potential when resources diffuse through a medium near a boundary. The result is strata of chemicals appearing in order of redox potential, which is best explained by the presence of life.
}, address = {1200 East California Boulevard, Pasadena, California 91125}, advisor = {Adami, Christoph Carl}, } @phdthesis{10.7907/2z40-1m97, author = {Ofria, Charles A.}, title = {Evolution of genetic codes}, school = {California Institute of Technology}, year = {1999}, doi = {10.7907/2z40-1m97}, url = {https://resolver.caltech.edu/CaltechETD:etd-09042007-091804}, abstract = {In this thesis, I use analytical and computational techniques to study the development of codes in evolutionary systems. We only know of one instance of such a genetic code in the natural world: our own DNA. However, the results from my work are expected to be universally true for all evolving systems. I use mathematical models and conduct experiments with avida, a software-based research platform for the study of evolution in “digital organisms.” This allows me to collect statistically powerful data over evolutionary timescales infeasible in a biological system. In the avida system, Darwinian evolution is implemented on populations of self-replicating computer programs. A typical experiment is seeded with a single ancestor program capable only of reproduction. This ancestor gives rise to an entire population of programs, which adapt to interact with a complex environment, while developing entirely new computational capabilities. I study the process of evolution in this system, taking exact measurements on the underlying genetic codes, and performing tests that would be prohibitively difficult in biological systems. I have focused on the following areas in studying the evolution of genetic codes: Information Theory: I treat the process of reproduction as a noisy channel in which codes are transmitted from the parent’s genome to the child. Unlike most channels, however, evolution actively selects for codes received with a higher information content, even if this increased information was introduced via noise. A genetic code consists of information about the environment surrounding the organism. As a population adapts, this information increases, and can be approximated through measuring the reduction of per-nucleotide entropy - in effect sites freeze in place as they code for useful functionality. In the avida system, we know the sequence of all genomes in the population, and new computational genes can be identified as they are formed. The Evolution of Genetic Organization: Organisms incapable of error correction (such as viruses) develop strong code compaction techniques to minimize their target area for mutations, the most prominent of which is overlapping genes. Higher organisms, however, are capable of reducing their mutational load and will explicitly spread out their code, cleanly segregating their genes. I investigate the pressures behind overlapping or segregation of genes, and demonstrate that overlaps have a side effect of drastically reducing the probability of neutral mutations within a gene, and hence hindering continued adaptation. Further, in a changing environment, overlapping genes have a significantly reduced ability to adapt independently. I compare overlapping and singly expressed sections of code in avida, and show a significant (two-fold) difference in the average per-site variation. I also demonstrate the evolutionary pressure for organisms to segregate their genes in a fluctuating environment to improve their adaptive abilities. Evolving Computer Programs: I explore evolution in digital genetic codes, and isolate some of those features of a programming language that promote continuous adaptation. In the biological world evolution gives rise to complex organisms robust to changing situations in their environment. This increase in complexity and “functionality” of the organisms typically generates more stable systems. On the other hand, as computer programs gain complexity, they only become more fragile. If two programs interact in a way not explicitly designed, the results are neither predictable nor reliable. In fact, computer programs often fail even when put to the use for which they were explicitly intended. Computational organisms, however, have a level of robustness more akin to their biological counterparts, not only performing computations, but often doing so in a manner beyond the efficiency that a human programmer could produce. Finally, all of this work is tied together, and future directions for its continuation are explored.}, address = {1200 East California Boulevard, Pasadena, California 91125}, advisor = {Adami, Christoph Carl}, }