Skip Nav Destination
Close Modal
Update search
NARROW
Format
TocHeadingTitle
Date
Availability
1-4 of 4
Randal S. Olson
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life23-30, (July 23–27, 2018) 10.1162/isal_a_00012
Abstract
View Paper
PDF
Susceptibility to common human diseases such as cancer is influenced by many genetic and environmental factors that work together in a complex manner. The state-of-the-art is to perform a genome-wide association study (GWAS) that measures millions of single-nucleotide polymorphisms (SNPs) throughout the genome followed by a one-SNP-at-a-time statistical analysis to detect univariate associations. This approach has identified thousands of genetic risk factors for hundreds of diseases. However, the genetic risk factors detected have very small effect sizes and collectively explain very little of the overall heritability of the disease. Nonetheless, it is assumed that the genetic component of risk is due to many independent risk factors that contribute additively. The fact that many genetic risk factors with small effects can be detected is taken as evidence to support this notion. It is our working hypothesis that the genetic architecture of common diseases is partly driven by non-additive interactions. To test this hypothesis, we developed a heuristic simulation-based method for conducting thought experiments about the complexity of genetic architecture. We show that a genetic architecture driven by complex interactions is highly consistent with the magnitude and distribution of univariate effects seen in real data. We compare our results with measures of univariate and interactions effects from two large-scale GWAS studies of sporadic breast cancer and find evidence to support our hypothesis that is consistent with the results of our thought experiment.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems250-257, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch045
Abstract
View Paper
PDF
A common idiom in biology education states, Eyes in the front, the animal hunts. Eyes on the side, the animal hides. In this paper, we explore one possible explanation for why predators tend to have forward-facing, high-acuity visual sys- tems. We do so using an agent-based computational model of evolution, where predators and prey interact and adapt their behavior and morphology to one another over successive generations of evolution. In this model, we observe a coevolutionary cycle between prey swarming behavior and the predators visual system, where the predator and prey continually adapt their visual system and behavior, respectively, over evolutionary time in reaction to one another due to the well-known predator confusion effect. Furthermore, we provide evidence that the predator visual system is what drives this coevolutionary cycle, and suggest that the cycle could be closed if the predator evolves a hybrid visual system capable of narrow, high-acuity vision for tracking prey as well as broad, coarse vision for prey discovery. Thus, the conflicting demands imposed on a predators visual system by the predator confusion effect could have led to the evolution of complex eyes in many predators.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems554-561, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch089
Abstract
View Paper
PDF
Flies that walk in a covered planar arena on straight paths avoid colliding with each other, but which of the two flies stops is not random. High-throughput video observations, coupled with dedicated experiments with controlled robot flies have revealed that flies utilize the type of optic flow on their retina as a determinant of who should stop, a strategy also used by ship captains to determine which of two ships on a collision course should throw engines in reverse. We use digital evolution to test whether this strategy evolves when collision avoidance is the sole selective pressure. We find that the strategy does indeed evolve in a narrow range of cost/benefit ratios, for experiments in which the regressive motion cue is error free. We speculate that these stringent conditions may not be sufficient to evolve the strategy in real flies, pointing perhaps to auxiliary costs and benefits not modeled in our study.
Proceedings Papers
. ecal2015, ECAL 2015: the 13th European Conference on Artificial Life620, (July 20–24, 2015) 10.1162/978-0-262-33027-5-ch107