# Biomathematics / Computational Biology Colloquium

#### Non-Markovian Enzymatic Turnover Distributions - clues about the inner-workings of enzymes from their output

**Speaker:**
Tal Robin, The Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University

**Location:**
Warren Weaver Hall 1314

**Date:**
Tuesday, September 17, 2019, 12:30 p.m.

**Synopsis:**

The Michaelis-Menten reaction scheme is widely used to describe the enzymatic catalysis. However, it is fit to describe many other processes due to it's simplicity and generality. It is best described simply as a renewal process with the option of a restart. That is, after the first initiation step, and before the final completion step, there is a possibility to go back and start from the beginning.

The classical model is described as a three steps Markov chain: initiation(substrate binding) <-> processing(complex) -> completion(catalysis), where the binding step is reversible. This was in fact very successful in describing the bulk behavior of enzymatic catalysis. However, in the last decade or so, single-molecule reactions have opened the door to observing not only averaged rates as can be measured in bulk experiments, but the full distribution of turnover times of single catalytic events. From these experiments came a need to re-examine the classical model as the turnover distributions showed clear divination from expectation.

In this talk, I will discuss a first passage method to describe the catalytic turnover without making the assumption of Markov steps (exponential waiting times), then show what can be learned about the inner workings of an enzyme from looking at the distribution of its output catalysis. Also, limitations of the method will be discussed.