ABSTRACT
Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE's utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations.
Subject(s)
Clostridioides difficile/genetics , Host-Pathogen Interactions/genetics , Microbiota/genetics , Models, Theoretical , Algorithms , Animals , Clostridioides difficile/growth & development , Clostridioides difficile/pathogenicity , MiceABSTRACT
We used selective adaptation to identify the neural mechanisms responsible for 3-D shape perception from orientation flows in retinal images [Li, A., & Zaidi, Q. (2000). Perception of three-dimensional shape from texture is based on patterns of oriented energy. Vision Research 40 (2), 217-242)]. Three-dimensional shape adaptation could involve stages from photoreceptors to non-oriented retinal cells, oriented cells in striate cortex, and extra-striate cells that respond to 3-D slants. To psychophysically isolate the relevant stage, we used 3-D adapting stimuli created from real and illusory orientations, and test stimuli different from the adapting stimuli in phases and frequencies. The results showed that mechanisms that adapt to 3-D shapes combine real and illusory 2-D orientation information over a range of spatial frequencies.
