Biology subjects

Voit, E.

Publications and source records attributed to Voit, E..

4 recordsLinked to original sources

bioRxiv2021

Emergence and Enhancement of Ultrasensitivity through Posttranslational Modulation of Protein Stability

Signal amplification converts a linear input to a steeply sigmoid output and is central to cellular functions. One canonical signal amplifying motif is zero-order ultrasensitivity through the posttranslational modification (PTM) cycle signaling proteins. The functionality of this signaling motif has been examined conventionally by supposing that the total amount of the protein substrates remains constant. However, covalent modification of signaling proteins often results in changes in their stability, which affects the abundance of the protein substrates. Here we use a mathematical model to explore the signal amplification properties in such scenarios. Our simulations indicate that PTM-induced protein stabilization brings the enzymes closer to saturation, and as a result, ultrasensitivity may emerge or is greatly enhanced, with a steeper sigmoidal response of higher magnitude and generally longer response time. In cases where PTM destabilizes the protein, ultrasensitivity can be regained through changes in the activities of the involved enzymes or from increased protein synthesis. Interestingly, ultrasensitivity is not limited to modified or unmodified protein substrates; the total protein substrate can also exhibit ultrasensitivity. It is conceivable that cells use inducible protein stabilization as a way to boost signal amplification while saving energy by keeping the protein substrate at low basal conditions.

systems biology↗

bioRxiv2021

Inference and Validation of the Structure of Lotka-Volterra Models

For close to a century, Lotka-Volterra (LV) models have been used to investigate interactions among populations of different species. For a few species, these investigations are straightforward. However, with the arrival of large and complex microbiomes, unprecedently rich data have become available and await analysis. In particular, these data require us to ask which microbial populations of a mixed community affect other populations, whether these influences are activating or inhibiting and how the interactions change over time. Here we present two new inference strategies for interaction parameters that are based on a new algebraic LV inference (ALVI) method. One strategy uses different survivor profiles of communities grown under similar conditions, while the other pertains to time series data. In addition, we address the question of whether observation data are compliant with the LV structure or require a richer modeling format. The code and data used in this manuscript are available at "https://github.com/LBSA-VoitLab/Inference_and_Validation_of_the_Structure_of_Lotka_Volterra_Models".

systems biology↗

bioRxiv2021

Antibiotics drive expansion of rare pathogens in a chronic infection microbiome model

Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment, and are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model, and benchmark on clinical data. We show that, in the absence of antibiotics, the microbiome structure in a synthetic sputum medium is highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path towards microbiome-informed conditional treatment strategies.

microbiology↗

bioRxiv2017

A Mathematical Model of the Phosphoinositide Pathway

Phosphoinositides are signaling lipids that constitute a complex network regulating many cellular processes. We propose a computational model that accounts for all known species of phosphoinositides in the plasma membrane of mammalian cells. The model replicates the steady-state of the phosphoinositide pathway and most known dynamic phenomena. Furthermore, sensitivity analysis demonstrates model robustness to moderate alterations in any of the parameters. Model analysis suggest that the greatest contributor to PI(4,5)P2 production is a flux representing the direct transformation of PI into PI(4,5)P2 and is also responsible for the maintenance of this pool when PI(4)P is decreased. PI(5)P is also shown to be a significant source for PI(4,5)P2 production. The model was validated with data from siRNA screens that knocked down the expression of several enzymes in the pathway. The screen monitored the activity of the epithelium sodium channel, ENaC, which is activated by PI(4,5)P2. Moderating ENaC activity can have a therapeutic effect in Cystic Fibrosis (CF) patients. Our model suggests control strategies where the activities of the enzyme PIP5KI or the PI4K+PIP5KI+DVL protein complex are decreased and cause an efficacious reduction in PI(4,5)P2 levels while avoiding undesirable alterations in other phosphoinositide pools.\n\nAbbreviations

systems biology↗