Monday, June 14 at 03:15pm (PDT)Monday, June 14 at 11:15pm (BST)Tuesday, June 15 07:15am (KST)
SMB2021 FollowMonday (Tuesday) during the "CT01" time block.
Faculty of Mathematics - University of Vienna, Vienna Graduate School of Population Genetics
"A mathematical model for the adaptation of a quantitative trait in a panmictic population"
The genetic architecture of a quantitative trait ranges from selective sweeps at few loci to subtle allele frequency shifts at many loci. By genetic architecture we mean the number of loci responding evolutionarily to adaptation, their mutation rates and the distribution of their mutational effects, their linkage relations, as well as their epistatic interactions. Höllinger et al.  showed for a panmictic population that the population-scaled background mutation rate determines crucially the shape of the mutant allele frequency distribution at the end of the adaptive phase. Remarkably, the strength of selection is not important as long as the locus effects are all the same.We report about results how variation among locus effects alter these findings. For the infinite sites model, we present analytical results for the locus-based distribution of the mutants as well as the phenotypic mean and variance, which are based on a combination of branching process theory (for the initial stochastic phase) and deterministic theory. They are compared with comprehensive computer simulations. I. Höllinger, P.S. Pennings, and J. Hermisson, Polygenic adaptation: From sweeps to subtle frequency shifts, PLOS Genetics, 15: 1–26, 2019.
Alexander B. Brummer
Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, Beckman Research Institute, City of Hope National Medical Center
"Cancer as a model system for testing metabolic scaling theory"
Biological allometries, such as the scaling of metabolism to mass, are hypothesized to result from natural selection to maximize how vascular networks fill space yet minimize internal transport distances and resistance to blood flow. Metabolic scaling theory argues two guiding principles—conservation of fluid flow and space-filling fractal distributions—describe a diversity of biological networks and predict how the geometry of these networks influences organismal metabolism. Yet, absent from these past efforts are studies that independently measure both metabolic function and vascular form. We present simultaneous and consistent measurements of metabolic scaling exponents from clinical lung cancer imaging, and identify potential quantitative imaging biomarkers indicative of tumor growth.We analyze 65 clinical PET-CT scans of patients with non-small cell lung carcinoma. Examination of the scaling of maximum standard uptake value with metabolic tumor volume, and metabolic tumor volume with gross tumor volume, yields metabolic scaling exponents of 0.64 (0.20) and 0.70 (0.17), respectively. We compare these to the value of 0.85 (0.06) derived from the geometric scaling of the tumor-supplying vasculature. These results (1) identify imaging biomarkers in vascular geometry related to blood volume and flow and (2) inform energetic models of growth and development for tumor forecasting.
Oklahoma State University
"How to integrate mechanical treatment and biological control to improve field treatment efficiency on invasions"
Projecting controlling outcomes of different management strategies on invasive populations has broad implications in field management. Different to herbicide usage that may cause environmental pollution and non-target effects on native plants, nonchemical methods, have shown great targeted effectiveness on invasion. However, an interesting and important question remains unclear is that how to decrease the repetition of nonchemical treatments. One possible approach is to integrate nonchemical treatments with biological control agents, which can attack and limit invasion spread after being established in the field. We hypothesize that applying nonchemical methods to remove occurring invasive plant while establishing biological control agents, then using the established biological control agents to limit future regrowth of invasive plant will decrease the use of nonchemical treatments. We developed a spatial modeling framework, including their dispersal processes, to capture population dynamics change under various strategies of control. We found that applying nonchemical treatment in a higher frequency with smaller treated areas per time is a more efficient approach than vice versa. More importantly, we emphasized that a high biological control efficiency can continuously decrease the requirement of repeated treatment of nonchemical methods and maintain the invasive population at a low level.