Sebastian Bonhoeffer

Last Name

Bonhoeffer

First Name

Sebastian

ORCID

0000-0001-8052-3925

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03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian

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Publications 1 - 10 of 166

orcAI: A Machine Learning Tool to Detect and Classify Acoustic Signals of Killer Whales in Audio Recordings
Bonhoeffer, Sebastian; Selbmann , Anna; Angst, Daniel C.; et al. (2025)
Cancer-induced immunosuppression can enable effectiveness of immunotherapy through bistability generation: A mathematical and computational examination
Garcia, Victor; Bonhoeffer, Sebastian; Fu, Feng (2020)
Journal of Theoretical Biology
Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these interactions are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can plausibly reproduce biological behavior that is consistent with the notion of an immunological barrier. This behavior is also in accord with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability, where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into a cancer-free state. Additionally, in combination with the administration of immune effector cells, modifications in cancer cell killing may be harnessed for immunotherapy without the need for resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers.
High Heritability Is Compatible with the Broad Distribution of Set Point Viral Load in HIV Carriers
Bonhoeffer, Sebastian; Fraser, Christophe; Leventhal, Gabriel E. (2015)
PLoS Pathogens
Set point viral load in HIV patients ranges over several orders of magnitude and is a key determinant of disease progression in HIV. A number of recent studies have reported high heritability of set point viral load implying that viral genetic factors contribute substantially to the overall variation in viral load. The high heritability is surprising given the diversity of host factors associated with controlling viral infection. Here we develop an analytical model that describes the temporal changes of the distribution of set point viral load as a function of heritability. This model shows that high heritability is the most parsimonious explanation for the observed variance of set point viral load. Our results thus not only reinforce the credibility of previous estimates of heritability but also shed new light onto mechanisms of viral pathogenesis.
How Good Are Statistical Models at Approximating Complex Fitness Landscapes?
Du Plessis, Louis; Leventhal, Gabriel E.; Bonhoeffer, Sebastian (2016)
Molecular Biology and Evolution
Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.
Molecular Epidemiology Reveals Long-Term Changes in HIV Type 1 Subtype B Transmission in Switzerland
Kouyos, Roger D.; Wyl, Viktor von; Yerly, Sabine; et al. (2010)
The Journal of Infectious Diseases
The effect of combining antibiotics on resistance: A systematic review and meta-analysis
Siedentop, Berit Ada; Kachalov, Viacheslav N.; Witzany, Christopher; et al. (2024)
eLife
Background: Under which conditions antibiotic combination therapy decelerates rather than accelerates resistance evolution is not well understood. We examined the effect of combining antibiotics on within-patient resistance development across various bacterial pathogens and antibiotics. Methods: We searched CENTRAL, EMBASE, and PubMed for (quasi)-randomised controlled trials (RCTs) published from database inception to 24 November 2022. Trials comparing antibiotic treatments with different numbers of antibiotics were included. Patients were considered to have acquired resistance if, at the follow-up culture, a resistant bacterium (as defined by the study authors) was detected that had not been present in the baseline culture. We combined results using a random effects model and performed meta-regression and stratified analyses. The trials’ risk of bias was assessed with the Cochrane tool. Results: 42 trials were eligible and 29, including 5054 patients, qualified for statistical analysis. In most trials, resistance development was not the primary outcome and studies lacked power. The combined odds ratio for the acquisition of resistance comparing the group with the higher number of antibiotics with the comparison group was 1.23 (95% CI 0.68–2.25), with substantial between-study heterogeneity (I2=77%). We identified tentative evidence for potential beneficial or detrimental effects of antibiotic combination therapy for specific pathogens or medical conditions. Conclusions: The evidence for combining a higher number of antibiotics compared to fewer from RCTs is scarce and overall compatible with both benefit or harm. Trials powered to detect differences in resistance development or well-designed observational studies are required to clarify the impact of combination therapy on resistance. Funding: Support from the Swiss National Science Foundation (grant 310030B_176401 (SB, BS, CW), grant 32FP30-174281 (ME), grant 324730_207957 (RDK)) and from the National Institute of Allergy and Infectious Diseases (NIAID, cooperative agreement AI069924 (ME)) is gratefully acknowledged.
Dynamic variation in cycling of hematopoietic stem cells in steady state and inflammation
Takizawa, Hitoshi; Regoes, Roland R.; Boddupalli, Chandra S.; et al. (2011)
Journal of Experimental Medicine
Hematopoietic stem cells (HSCs) maintain blood production. How often mouse HSCs divide and whether each HSC contributes simultaneously, sequentially, or repetitively to hematopoiesis remains to be determined. We track division of 5-(and-6)-carboxyfluorescein diacetate succinimidyl ester (CFSE)–labeled HSC in vivo. We found that, in steady-state mice, bone marrow cells capable of reconstituting lifelong hematopoiesis are found within both fast-cycling (undergoing five or more divisions in 7 wk) and quiescent (undergoing zero divisions in 12–14 wk) lineage marker–negative c-Kit+ Sca-1+ populations. The contribution of each population to hematopoiesis can fluctuate with time, and cells with extensive proliferative history are prone to return to quiescence. Furthermore, injection of the bacterial component lipopolysaccharide increased the proliferation and self-renewal capacity of HSCs. These findings suggest a model in which all HSCs undergo dynamic and demand-adapted entry into and exit out of the cell cycle over time. This may facilitate a similar degree of turnover of the entire HSC pool at the end of life.
Reversing resistance: different routes and common themes across pathogens
Allen, Richard C.; Engelstädter, Jan; Bonhoeffer, Sebastian; et al. (2017)
Proceedings of the Royal Society B: Biological Sciences
Resistance spreads rapidly in pathogen or pest populations exposed to bio- cides, such as fungicides and antibiotics, and in many cases new biocides are in short supply. How can resistance be reversed in order to prolong the effec- tiveness of available treatments? Some key parameters affecting reversion of resistance are well known, such as the fitness cost of resistance. However, the population biological processes that actually cause resistance to persist or decline remain poorly characterized, and consequently our ability to manage reversion of resistance is limited. Where do susceptible genotypes that replace resistant lineages come from? What is the epidemiological scale of reversion? What information do we need to predict the mechanisms or likelihood of reversion? Here, we define some of the population biological processes that can drive reversion, using examples from a wide range of taxa and biocides. These processes differ primarily in the origin of revertant genotypes, but also in their sensitivity to factors such as coselection and compensatory evolution that can alter the rate of reversion, and the likelihood that resistance will re-emerge upon re-exposure to biocides. We therefore argue that discriminating among different types of reversion allows for better prediction of where resistance is most likely to persist.
The Role of Recombination for the Coevolutionary Dynamics of HIV and the Immune Response
Mostowy, Rafal; Kouyos, Roger D.; Fouchet, David; et al. (2011)
PLoS ONE
The evolutionary implications of recombination in HIV remain not fully understood. A plausible effect could be an enhancement of immune escape from cytotoxic T lymphocytes (CTLs). In order to test this hypothesis, we constructed a population dynamic model of immune escape in HIV and examined the viral-immune dynamics with and without recombination. Our model shows that recombination (i) increases the genetic diversity of the viral population, (ii) accelerates the emergence of escape mutations with and without compensatory mutations, and (iii) accelerates the acquisition of immune escape mutations in the early stage of viral infection. We see a particularly strong impact of recombination in systems with broad, non-immunodominant CTL responses. Overall, our study argues for the importance of recombination in HIV in allowing the virus to adapt to changing selective pressures as imposed by the immune system and shows that the effect of recombination depends on the immunodominance pattern of effector T cell responses.
Stochastic Gene Expression Influences the Selection of Antibiotic Resistance Mutations
Sun, Lei; Ashcroft, Peter; Ackermann, Martin; et al. (2020)
Molecular Biology and Evolution
Bacteria can resist antibiotics by expressing enzymes that remove or deactivate drug molecules. Here, we study the effects of gene expression stochasticity on efflux and enzymatic resistance. We construct an agent-based model that stochastically simulates multiple biochemical processes in the cell and we observe the growth and survival dynamics of the cell population. Resistance-enhancing mutations are introduced by varying parameters that control the enzyme expression or efficacy. We find that stochastic gene expression can cause complex dynamics in terms of survival and extinction for these mutants. Regulatory mutations, which augment the frequency and duration of resistance gene transcription, can provide limited resistance by increasing mean expression. Structural mutations, which modify the enzyme or efflux efficacy, provide most resistance by improving the binding affinity of the resistance protein to the antibiotic; increasing the enzyme’s catalytic rate alone may contribute to resistance if drug binding is not rate limiting. Overall, we identify conditions where regulatory mutations are selected over structural mutations, and vice versa. Our findings show that stochastic gene expression is a key factor underlying efflux and enzymatic resistances and should be taken into consideration in future antibiotic research.

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Sebastian Bonhoeffer

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