A novel computation tool for microbial community modeling predicts the evolution and diversification of E. coli in laboratory evolution experiments and gives insight into the underlying metabolic processes.
A computer model of human cardiomyocyte was produced and validated on independent datasets, overcoming shortcomings of its predecessors, also yielding broadly relevant insights and results on major ionic currents.
A quantitative understanding of molecular tension sensor function enables the production of unique sensors with desired mechanical properties as well as the ability to distinguish between protein force and protein deformation in mechanosensitive processes.
Computational modeling, and empirical behavioral and EEG results show that learning relies not only on comparing current events to past experience, but integrates response-based outcome predictions and confidence.
Mechanical competition is controlled by biophysical parameters that regulate cellular homeostatic density, while biochemical competition is regulated by parameters that influence the organisation of cells in a tissue.
A mathematical model of bias in marker-gene and metagenomic sequencing measurements explains systematic errors in defined mixtures of microbial species, and enables quantitative and reproducible investigation of biological communities.