Evolutionary Theory of Disease

Thursday, June 17 at 11:30am (PDT)
Thursday, June 17 at 07:30pm (BST)
Friday, June 18 03:30am (KST)

SMB2021 SMB2021 Follow Thursday (Friday) during the "MS20" time block.
Note: this minisymposia has multiple sessions. The second session is MS19-EVOP (click here).

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Jesse Kreger (University of California, Irvine, United States), Natalia Komarova (University of California, Irvine, United States)


The use of fundamental principles of evolutionary biology can provide important insight into disease dynamics, both within and between hosts. For example, evolutionary mathematical models have been used to better understand disease progression, immune escape mechanisms/drug resistance, and optimal treatment regimens in many prominent diseases (such as virus infection and cancer). Our mini-symposium focuses on the “Evolutionary Theory of Disease”, and in particular, on how evolutionary ideas in tandem with mathematical techniques and models can be used to understand disease dynamics as well as how to best combat the disease. In our mini-symposium, researchers will present their exciting and impactful recent research, including new mathematical and computational models, with a focus on understanding the evolutionary dynamics of the disease. Our list of speakers includes both accomplished/senior researchers as well as junior mathematicians at the postdoctoral and graduate student levels. Their research spans different diseases (from infectious diseases to cancer) and mathematical approaches (including differential equations, agent-based models, network models, etc), and should stimulate broad interest in the mathematical biology community. We look forward to an exciting exchange of ideas, as well as great networking opportunities for researchers at all career stages.

Chadi M. Saad-Roy

(Princeton University, United States)
"The evolution of an asymptomatic infectious stage: analysis of a simple evolutionary-epidemiological model"
Pathogens exhibit numerous life-history strategies. An important pathogen characteristic is the degree of symptoms exhibited by hosts at the onset of infectiousness. Additionally, mediated by host immunity, a pathogen may elicit reduced (or no) symptoms in the first stage leading to simultaneously slower progression and lower transmission. In this talk, we examine the evolutionary implications of these trade-offs. We couple a simple epidemiological model with evolutionary analyses, and we find that numerous evolutionary outcomes are possible. For simple trade-off formulations, these include a fully symptomatic or asymptomatic first infectious stage, a subsymptomatic first stage, or bistability between a fully symptomatic and asymptomatic first stage. Then, we discuss the ensuing implications for disease mitigation measures.

Jasmine Foo

(University of Minnesota, United States)
"Power law transitions in site frequency spectra of neutrally evolving tumors"
The site frequency spectrum (SFS) is a popular genomic summary statistic that tracks the frequencies of mutations in a population sample. In the context of cancer, the site frequency spectrum of tumor samples are commonly used to gain insights into tumor evolutionary processes. However, recent analyses of the SFS in tumor population models have generally dealt with special or limiting cases, such as considering only cells with an infinite line of descent, assuming deterministic tumor growth, or taking large time/population limits. I will discuss recent work in which we derive exact expressions for the mean SFS in a neutrally evolving tumor. We find that the rates of cell birth and death change the shape of the SFS at the small frequency end, inducing a transition between power laws as cell viability decreases. We demonstrate how, in principle, this insight may be used to estimate the ratio between cell birth and cell death rates, as well as the mutation rate, using SFS data alone.

Mohammad Kohandel

(University of Waterloo, Canada)
"Predicting mutability of the genomic segments of a pathogen"
There is an essential need to better understand how a pathogenesis, like SARS-CoV-2, is affected by mutations and to determine the conserved segments in the genome that can serve as stable targets for novel therapeutics. We introduce a text-mining method to estimate the mutability of genomic segments directly from a reference (ancestral) whole genome sequence. The method relies on calculating the importance of genomic segments based on their spatial distribution and frequency over the whole genome. To validate our approach, we perform a large-scale analysis of the viral mutations in nearly 80,000 publicly-available SARS-CoV-2 predecessor whole genome sequences and show that these results are highly correlated with the segments predicted by the text-mining method. Importantly, these correlations are found to hold at the codon and gene levels, as well as for gene coding regions.

Alison Hill

(Johns Hopkins University, Institute for Computational Medicine, United States)
"Selection for SARS-CoV-2 variants at the within-host and population scale"
The evolution of novel variants has become an increasing concern as the COVID-19 pandemic has progressed into 2021. In this talk we will discuss modeling work to understand the factors driving SARS-CoV-2 evolution within individual hosts and across populations. We examine and compare mutations that increase transmission and those that evade adaptive immunity. We build models to include and analyze the roll of within-host clinical course, heterogeneities in transmission, population structure, and the nature of acquired and vaccine-induced immunity on the fate of variants emerging at different times and places throughout the pandemic. We find that several unique features of COVID-19, including the timing of peak infectiousness vs the onset of adaptive immune responses in infected individuals, and the asynchronous spatiotemporal nature of epidemics around the world, contribute to patterns observed in the evolution of new variants.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.