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Antibiotic Model Systems
Treatments of infections can consist of sequences of pulses of varying drug concentration. Low drug concentrations are said to cause resistance, as they give the bacteria time to adapt to the drug; on the other hand, high concentration of drugs that can affect different types of bacteria can cause undesirable changes in the microbiome. We studied a simple population dynamics model system to investigate when a series of pulses are better at reducing an infection, and when this can cause resistance. If you are excited by this, you might also be interested in checking out the work of our colleagues Edo Kussell, Anne-Flo Bitbol, Kevin Woods, Andrew Read, Rosalind Allen, Bob Austin, Julia Bos and colleagues.
Relevant publications:
M. Bauer, I.R. Graf, V. Ngampruetikorn, G.J. Stephens and E. Frey,
PLoS computational biology 13 (9), e1005747 (2017)
With a simple model for an single infectious population, we asked systematically what type of sequences are best to eliminate an infection. In our theoretical model, a period of lower drug concentration is not always detrimental for treatment efficiency. However, here as with many optimization questions, it is important to remain aware that there are multiple optimization criteria that one can study; for this model system, we present two possibilities, and hope that experimental work will be able to clarify what the most relevant models and optimization goals are.
Microbial Model Populations
There are more than 10000 different species of microbes in a gram of soil; similarly, our gut microbiome is estimated to contain 10-100 trillion species. How the coexistence between so many species can occur is one of the big mysteries of biology; in 1961, when the question arose of how many different species of plankton could coexist despite limited availability of different nutrients, this was called 'The paradox of the plankton'. While one can study these questions by modeling food networks directly, an alternative, more abstract way is to pursue this question in a game-theoretical framework: different bacterial species correspond to agents that play different strategies, and obtain a fitness proportional to the success of their strategy. The prisoner's dilemma is one such game or interaction topology. It is known, however, that real-world modifications, such as having a system where bacteria can cluster in space, or having slower dynamics, can significantly alter which bacteria survive and which die. We studied this old interaction topology in such a spatial system with a time delay, and found that this can significantly alter how long species survive and whether they can coexist.
Relevant Publications:
M. Bauer and E. Frey, Europhysics Letters 122 (6), 68002 (2018).
M. Bauer and E. Frey, Physical Review E 97 (4), 042307 (2018).
M. Bauer and E. Frey, Physical Review Letters 121 (26), 268101 (2018).
Organisms often adapt to new environments with a delay. We showed that in spatially extended systems, this delay can change the survival of populations: when two species interact via a public good, produced by one species at the cost of a lower fitness, this species dies out. Delays in adapting fitness can lead to coexistence of both species. Our work thus provided an intuitive understanding of delay as a contributor to diversity in ecological systems, and stressed the importance of experiments where space is mimicked.
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