Biology subjects

Engelhardt, D.

Publications and source records attributed to Engelhardt, D..

3 recordsLinked to original sources

bioRxiv2022

evosim: fast and scalable stochastic simulations of evolutionary dynamics

The simulation of clonal dynamics with branching processes can provide valuable insights into disease progression and treatment optimization, but exact simulation of branching processes via the Stochastic Simulation Algorithm (SSA) is computationally prohibitive at the large population sizes associated with therapeutically-relevant scenarios. evosim is a versatile and flexible Python implementation of a fast and unbiased tau-leaping algorithm for the simulation of birth-death-mutation branching processes that is scalable to any population size. Package functionalities support the incorporation and tracking of a sequence of evolutionary changes such as therapeutic interventions as well as the analysis of population diversity. We show that runtimes scale logarithmically with population size, by contrast to the linear scaling of the SSA, and simulations exhibit strong agreement with SSA simulation results. These findings are also supported by mathematical results (Supplementary information). AvailabilityPackage, documentation, and tutorials / usage examples are available on GitHub (https://github.com/daliten/evosim). Mathematical details of the algorithm and the pseudocode are provided in the included Supplementary information.

evolutionary biology↗

bioRxiv2021

Single-cell genetic heterogeneity linked to immune infiltration in glioblastoma.

Glioblastoma (GBM) is the most aggressive brain tumor with a median survival of ~15 months. Targeted approaches have not been successful in this tumor type due to the large extent of intratumor heterogeneity. Mosaic amplification of oncogenes suggests that multiple genetically distinct clones are present in each tumor. To uncover the relationships between genetically diverse subpopulations of GBM cells and their native tumor microenvironment, we employed highly multiplexed spatial protein profiling, coupled with single-cell spatial mapping of fluorescence in situ hybridization (FISH) for EGFR, CDK4, and PDGFRA. Single-cell FISH analysis of a total of 35,843 single nuclei (~2,100 per tumor) revealed that tumors in which amplifications of EGFR and CDK4 more frequently co-occur in the same cell exhibit higher infiltration of CD163+ immunosuppressive macrophages. Our results suggest that high throughput assessment of genomic alterations at the single cell level could provide a measure for predicting the immune state of GBM.

cancer biology↗

bioRxiv2018

Mutation rate variability as a driving force in adaptive evolution

Mutation rate is a key determinant of the pace as well as outcome of evolution, and variability in this rate has been shown in different scenarios to play a key role in evolutionary adaptation and resistance evolution under stress. Here we investigate the dynamics of resistance fixation in a bacterial population with variable mutation rates and show that evolutionary outcomes are most sensitive to mutation rate variations when the population is subject to environmental and demographic conditions that suppress the evolutionary advantage of high-fitness subpopulations. By directly mapping a molecular-level biophysical fitness function to the system-level dynamics of the population we show that both low and very high, but not intermediate, levels of stress result in a disproportionate effect of hypermutation on resistance fixation and that traditional definitions of the selection coefficient are insufficient to account for this effect. We demonstrate how this behavior is directly tied to the extent of genetic hitchhiking in the system, the propagation of high-mutation rate cells through association with high-fitness mutations. Our results indicate a substantial role for mutation rate flexibility in the evolution of antibiotic resistance under conditions that present a weak advantage over wildtype to resistant cells.

evolutionary biology↗