Dennis Vitkup

The paper uses neural networks to predict protein evolutionary rates by integrating molecular and cellular features across multiple scales, showing that protein-level and site-level properties contribute additively to evolutionary rate variance.

The paper shows macroecological laws emerge from chaotic species interactions in spatially heterogeneous communities without external noise.

This study identifies brain circuits and cell types affected by ASD mutations using a computational approach integrating genetic data with spatial transcriptomics and connectome data.

The paper identifies CGE-derived GABAergic interneurons as a key target of rare mutations linked to cognitive deficits in ASD and SCZ using human and mouse models.

The paper investigates the generation time effect on protein substitution rates across mammals and develops an integrated model of protein evolution.

The paper identifies a percolation-like transition in bacterial metabolic networks that correlates with lifestyle differences between obligate symbionts and free-living generalists.

This study examines how functional properties of bacterial species and communities influence the scaling of temporal abundance fluctuations in gut microbiota, finding strong associations with enzyme availability and metabolic capabilities.

The paper shows that protein evolution rates are inversely related to functional optimality and expression levels across multiple organisms and tissues.

The paper shows that truncating mutations affecting the same exon in autism lead to similar intellectual phenotypes, suggesting exons, not genes, are units of phenotypic impact.

The paper develops and validates a computational model showing mammalian cells use receptor abundance to encode past stimuli for relative sensing in signaling networks.

The paper introduces MERIDIAN, a Maximum Entropy framework to infer signaling network heterogeneity from cell-to-cell variability in network species abundances, and applies it to EGF-induced Akt signaling.

The paper introduces DIVERS, a method to quantify spatiotemporal dynamics and noise in microbiota abundances using replicate sampling and spike-in sequencing, applied to the human gut microbiome.