Biology subjects

Gjini, E.

Publications and source records attributed to Gjini, E..

3 recordsLinked to original sources

bioRxiv2019

Co-colonization interactions drive explicit frequency-dependent dynamics among N types

Multi-type spreading processes are ubiquitous in ecology, epidemiology and social systems, but remain hard to model mathematically and to understand on a fundamental level. Here, we describe and study a multi-type susceptible-infected-susceptible (SIS) model that allows for up to two co-infections of a host. Fitness differences between N infectious agents are mediated through altered susceptibilities to secondary infections that depend on colonizer- co-colonizer interactions. By assuming small differences between such pairwise traits (and other infection parameters equal), we derive a model reduction framework using separation of timescales. This quasi-neutrality in strain space yields a fast timescale where all types behave as neutral, and a slow timescale where non-neutral dynamics take place. On the slow timescale, N equations govern strain frequencies and accurately approximate the dynamics of the full system with O(N2) variables. We show that this model reduction coincides with a special case of the replicator equation, which, in our system, emerges in terms of the pairwise invasion fitnesses among strains. This framework allows to build the multi-type community dynamics bottom-up from only pairwise outcomes between constituent members. We find that mean fitness of the multi-strain system, changing with individual frequencies, acts equally upon each type, and is a key indicator of system resistance to invasion. Besides efficient computation and complexity reduction, these results open new perspectives into high-dimensional community ecology, detection of species interactions, and evolution of biodiversity, with applications to other multi-type biological contests. By uncovering the link between an epidemiological system and the replicator equation, we also show our co-infection model relates to Fishers fundamental theorem and to conservative Lotka-Volterra systems.

ecology↗

bioRxiv2019

Mathematical modeling suggests that benefits of short or long antibiotic treatment depend on details of infection

Antibiotics are the major tool for treating bacterial infections. With rising antibiotic resistance in microbes, strategies that limit further evolution and spread of drug resistance are urgently needed, in individuals and populations. While classical recommendations favor longer and aggressive treatments, more recent studies and clinical trials advocate for moderate regimens. In this debate, two axes of aggressive treatment have typically been conflated: treatment intensity and treatment duration, the latter being rarely addressed by mathematical models. Here, by using a simple mathematical model of a generic bacterial infection, controlled by hosts immune response, we investigate the role of treatment timing and antibiotic efficacy in determining optimal duration of treatment. We show that even in such simple mathematical model, it is impossible to select for universally optimal treatment duration. In particular, short (3 day) or long (7 day) treatments may be both beneficial depending on treatment onset, on the criterion used, and on the antibiotic efficacy. This results from the dynamic trade-off between immunity and resistance in acute, self-limiting infections, and uncertainty relating symptoms to the start of infection. We find that treatment timing can shift the trend between resistance selection and length of antibiotic exposure in individual hosts. We propose that major advances in predicting impact of antibiotics on bacterial infections must come from deeper experimental understanding of bacterial infection dynamics in humans. To guide rational therapy, mathematical models need to be constrained by data, including details of pathology and symptom thresholds in patients, and of host immune control of infection.

systems biology↗

bioRxiv2019

Quantifying bacterial fitness in intracellular dynamics

Understanding bacterial infection is challenging because it involves a complex interplay of host, pathogen, and intervention factors. To design successful control measures, mathematical models that quantify such interplay at the level of populations and phenotypes are needed. Here, we study a key aspect of intracellular infection: the interaction dynamics between bacteria and target cells, applicable to pathogens such as Salmonella, E. coli or Listeria monocytogenes. Our mathematical model focuses on the macrophage-bacteria system, implicitly accounting for host immunity, and illustrates three infection scenarios driven by the balance between bacterial growth and death processes. Our analysis reveals critical parameter combinations for the intracellular vs. extracellular fitness advantage of persistent bacteria, and the drivers of overall infection success across acute and persistent regimes. Our results provide quantitative insights on transitions from persistent, to acute, to containment of infection, and suggest biological parameters, such as infected macrophage apoptosis rate and burst size, as suitable intervention targets.

systems biology↗