Many families of homologous proteins show a remarkable degree of structural and functional conservation, despite the large variability in their amino acid sequences. We have developed a statistical-mechanics inspired inference approach to link this variability (easy to observe) to structure (hard to observe), i.e. to infer directly co-evolving residue pairs which turn our to form native contacts in the folded protein with high accuracy. The gained information is used to guide tertiary (and quaternary) structure prediction. As a specific example, I will discuss the auto-phosphorylation complex of histidine kinases, which are involved in the majority of signal transduction systems in the bacteria. Only a multidisciplinary approach integrating statistical genomics, biophysical protein simulation, and mutagenesis experiments, allows us to predict and verify the - so far unknown - active kinase structure

Phenotypic states of individual cells in a genetically identical population are heterogeneous and often correlated with the fitness of individual cells.The correlations between phenotypic states such as gene expression levels and fitness introduce complexities to understand the causes of cell population behaviors because the average of phenotypic states of cell population is not identical to the typical state of individual cells.Although this problem was well recognized by the researchers in 1930's-60's and several non-trivial theoretical results have been derived, the issue has not been fully addressed experimentally because of technical difficulties.We are currently working on this problem using modern single-cell measurement tools.In this seminar, I will first present our single-cell study on microbial persistence that eventually led us to the aforementioned problem.Then, I will present the experimental examination on the mathematical relation that links the growth rates of individual cells to that of cell population.I will also explain the newly developed microfluidic…

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I’ll describe our recent efforts to model protein evolution and amino-acid substitutions by (1) designing an easy to use pipeline to estimate amino-acid replacement matrices for specific protein groups, (2) using the knowledge of the protein structure, when available, (3) simplifying mixture models that have been proposed so far.

Originally it was thought that, at least in eukaryotes, gene order is random. However, in all well studied genomes genes of similar expression sit in close proximity to each other. Why is this? In this talk I'll present evidence that gene order is in part selected as it affects the noise level of genes. This explains operons organization in bacteria, clustering of essential genes and gene orientation in yeast

Is protein evolution just about protein function? In this talk I'll show that the processes involved in protein manufacture, rather than end-function, have a major role in determining rates of evolution and choice of both codons and amino acids. I'll examine in detail two issues, splicing and nucleosome occupancy