Monday, June 14 at 10:30pm (PDT)Tuesday, June 15 at 06:30am (BST)Tuesday, June 15 02:30pm (KST)
SMB2021 FollowMonday (Tuesday) during the "CT02" time block.
Technical University of Munich
"An immuno-epidemiological model linking within-host and between-host dynamics of cholera"
Cholera is a severe diarrheal infection caused by the Vibrio cholerae bacterium. It affects millions of people globally with an estimated 2.9 million cases reported annually. In this study, we formulate a multi-scale model linking the between-host and within-host dynamics of cholera to gain new perspectives on the spread of the infection. We conduct a time-scale analysis for the within-host system, where the dynamics of the immune response and the pathogen are differentiated using time scales. The approach used allows for the elimination of the pathogen after a defined time, which is contrary to other within-host models. We use the within-host system as a basis for the formulation of the epidemic model which takes into account direct human-to-human transmission as well as transmission via the environment. The epidemic model is a physiologically structured model based on the immune status, which is a function derived from the within-host immune response. The basic reproduction number is derived and the steady states analysed. Analysis of the endemic equilibrium reveals conditions that may lead to its stability as well as its destabilisation through the occurrence of a Hopf bifurcation. Without loss of immunity, the environmental transmission route is necessary for periodic orbits to occur.
"Modelling Therapy Scheduling Based on the Collateral Susceptibility of Drugs"
The development of drug resistance remains a major challenge in the treatment of chronic infections as there are little to no new drugs being discovered and mortality due to such infections are on the increase. To address this, the concept of collateral sensitivity cycling was proposed as a plausible therapy scheduling approach whereby drugs are sequentially used based on their collateral susceptibility profiles. Using control engineering approaches, we develop strategies aimed at minimizing the appearance of drug-resistant pathogens within the host whilst considering their collateral susceptibilities. With a generalized mathematical model based on bacteria population, we develop switching drug strategies which can be used to ensure the stability of the eradication equilibrium. Our numerical simulations compare different switching drug strategies and validate their use for mitigation against bacterial resistance.
Institut for Bioinformatics, University Medicine Greifswald
"Mathematical modeling of 1-MT-induced production of the ant-inflammatory metabolite KYNA in pigs"
Treatment with the drug 1-methyltryptophan (1-MT) has been shown to modulate immune responses by targeting several immunologically relevant pathways. One potential mode of action of 1-MT is a shift towards production of the tryptophan (TRP) metabolite kynurenic acid (KYNA), which mediates crucial immunomodulatory effects under inflammatory conditions. It is still unknown whether 1-MT is metabolized to KYNA directly or via intermediate metabolites such as TRP or kynurenine (KYN). To answer this question, this study employs mathematical modeling to evaluate six different hypothetical mechanisms. We developed models based on system of ordinary differential equations, and compare simulations to data measured in an experiment pig model investigating in vivo effects of 1-MT. We in silico evaluate the feasibility of assumptions made by comparing model dynamics with kinetic experimental data, and are thus able to direct further experimental work to the most promising explanatory mechanisms, facilitating further experimental verification. Based on analysis of the computational model, we conclude that a direct degradation of 1-MT to KYNA is the most probable metabolic pathway which best explains the experimentally observed kinetics.
University of Warwick
"SARS-CoV-2 variants and potential escape from vaccine-derived and pre-existing immunity"
The heterogeneity in vaccine coverage both locally and worldwide represents a large potential for SARS-CoV-2 variants that escape existing immunity both from vaccines and prior infections. As countries with high vaccine coverage prepare to relax other control measures, such variants could have a devastating effect. We demonstrate that, even when the variant is less transmissible than the locally dominant variant, reduced immunity can lead to a significant wave of infection. We use an SEIR ODE model of infection with two variants and three potential vaccines, and assume asymmetric immunity granted by prior infection between the two variants. We apply our model to the context in the UK, which had given first doses to 57% of its population by 29th March, with a combination of AstraZeneca and Pfizer vaccines. Initial doses focussed on older age groups and doses were given 12 weeks apart. As of 29th March, cases were dominated by the B.1.1.7 (UK) variant and the primary concern was importations of the B.1.351 (South African) variant. We show that if the B.1.351 variant significantly evades vaccine-derived and prior immunity then planned relaxations could lead to a large wave of B.1.351 infections.