Evolution of drug resistance; Experimental evolution; Mathematical modeling; SIR models
Liechti J. I., Leventhal G. E., Bonhoeffer S. (2017), Host population structure impedes reversion to drug sensitivity after discontinuation of treatment, in PLoS Comput. Biol.
, 13(8), 1005704-1005704.
Tepekule B., Uecker H., Derungs I., Frenoy A., Bonhoeffer S. (2017), Modeling antibiotic treatment in hospitals: A systematic approach shows benefits of combination therapy over cycling, mixing, and mono-drug therapies, in PLoS Comput. Biol.
, 13(9), 1005745-1005745.
Uecker H., Bonhoeffer S. (2017), Modeling antimicrobial cycling and mixing: Differences arising from an individual-based versus a population-based perspective, in Math Biosci
, 294, 85-91.
Alexander H. K., Mayer S. I., Bonhoeffer S. (2017), Population Heterogeneity in Mutation Rate Increases the Frequency of Higher-Order Mutants and Reduces Long-Term Mutational Load, in Mol. Biol. Evol.
, 34(2), 419-436.
Van Boeckel T. P., Glennon E. E., Chen D., Gilbert M., Robinson T. P., Grenfell B. T., Levin S. A., Bonhoeffer S., Laxminarayan R. (2017), Reducing antimicrobial use in food animals, in Science
, 357(6358), 1350-1352.
Richardson T. O., Liechti J. I., Stroeymeyt N., Bonhoeffer S., Keller L. (2017), Short-term activity cycles impede information transmission in ant colonies, in PLoS Comput. Biol.
, 13(5), 1005527-1005527.
Legros M., Bonhoeffer S. (2016), A combined within-host and between-hosts modelling framework for the evolution of resistance to antimalarial drugs, in J R Soc Interface
, 13(117), NA-NA.
Fingerhuth S. M., Bonhoeffer S., Low N., Althaus C. L. (2016), Antibiotic-Resistant Neisseria gonorrhoeae Spread Faster with More Treatment, Not More Sexual Partners, in PLoS Pathog.
, 12(5), 1005611-1005611.
Polster R., Petropoulos C. J., Bonhoeffer S., Guillaume F. (2016), Epistasis and Pleiotropy Affect the Modularity of the Genotype-Phenotype Map of Cross-Resistance in HIV-1, in Mol. Biol. Evol.
, 33(12), 3213-3225.
du Plessis L., Leventhal G. E., Bonhoeffer S. (2016), How Good Are Statistical Models at Approximating Complex Fitness Landscapes?, in Mol. Biol. Evol.
, 33(9), 2454-2468.
Nagaraja P., Alexander H. K., Bonhoeffer S., Dixit N. M. (2016), Influence of recombination on acquisition and reversion of immune escape and compensatory mutations in HIV-1, in Epidemics
, 14, 11-25.
Mikaberidze A., Mundt C. C., Bonhoeffer S. (2016), Invasiveness of plant pathogens depends on the spatial scale of host distribution, in Ecol Appl
, 26(4), 1238-1248.
Leventhal G. E., Bonhoeffer S. (2016), Potential Pitfalls in Estimating Viral Load Heritability, in Trends Microbiol.
, 24(9), 687-698.
Cadosch D., Abel Zur Wiesch P., Kouyos R., Bonhoeffer S. (2016), The Role of Adherence and Retreatment in De Novo Emergence of MDR-TB, in PLoS Comput. Biol.
, 12(3), 1004749-1004749.
Frost S. D., Pybus O. G., Gog J. R., Viboud C., Bonhoeffer S., Bedford T. (2015), Eight challenges in phylodynamic inference, in Epidemics
, 10, 88-92.
Leventhal G. E., Hill A. L., Nowak M. A., Bonhoeffer S. (2015), Evolution and emergence of infectious diseases in theoretical and real-world networks, in Nat Commun
, 6, 6101-6101.
Bonhoeffer S., Fraser C., Leventhal G. E. (2015), High heritability is compatible with the broad distribution of set point viral load in HIV carriers, in PLoS Pathog.
, 11(2), 1004634-1004634.
Fu F., Nowak M. A., Bonhoeffer S. (2015), Spatial heterogeneity in drug concentrations can facilitate the emergence of resistance to cancer therapy, in PLoS Comput. Biol.
, 11(3), 1004142-1004142.
From agriculture to human health, modern society relies heavily on the availability of drugs to control infectious diseases. For many important diseases, however, the long-term use of drugs has resulted in the evolution of high-level resistance. At the same time the cost of developing new drugs increases while the rate of their discovery decreases. A key question is thus how existing drugs could be deployed in a manner that prolongs their effective lifespan. This is a challenging question as it requires maximising efficiency of disease control while minimising the emergence of resistance. Making progress towards answering this question requires a better understanding of the factors underlying the evolutionary dynamics of resistance. The objective of this proposal is to study the combined effect of population structure and treatment on the evolution of resistance. The proposal is subdivided into three projects: Project A addresses the influence of contact structure on resistance evolution. To this end we will develop network-based epidemiological models that describe treatment and spread of sensitive/resistant strains in populations with heterogeneous contact structure. We address how the dynamics of emergence of resistance and reversion back to sensitivity depend on the contact structure of treated and untreated individuals. Project B is an experimental study addressing the evolution of drug resistance in epidemiologically relevant contexts. Using an automated liquid handling platform we will expose large number of bacterial populations to various antibiotic treatment regimes in a manner that reflects realistic epidemiological dynamics of transmission and treatment in patient populations. The setting allows studying large populations in parallel and implementing treatment strategies that depend dynamically on measurements of resistance. Specifically we will measure the effects of alternative treatment strategies on the evolution and spread of drug resistance in a setting that emulates the transmission dynamics in hospitals. Project C develops a stochastic modeling framework to use in parallel with the experimental studies of project B. The simulation tool has multiple functions. On the one hand it will help explore at low cost and high speed different potential experimental studies. On the other hand it will be used for data analysis and parameter inference.This proposal thus combines experimental and theoretical approaches to shed new light onto how population structure affects the spread of resistant infectious pathogens in response to drug treatment.