Welcome to the IPISB conference!

The Information, Probability and Inference in Systems Biology Conference (IPISB2016) will be held from May 18-20, 2016 at IST Austria in Klosterneuburg, Austria.

Conference organizers:

Peter Swain, University of Edinburgh
Gasper Tkacik, IST Austria

The meeting will address the following key areas:

  • New information-theoretic approaches to sensing and signal transduction by cells with an emphasis on dynamic problems, including the quantification and statistical estimation of information in molecular biology.
  • Study of stochastic biochemical networks—new developments in Monte Carlo simulation methods, approximate analytical techniques, and the need for a more developed theory of stochastic chemical reaction networks.
  • Inferential methods for statistical modelling of time series data from single cells, including simulation-based methods, likelihood-free inference, particle filtering techniques and reversible jump MCMC. 
  • Cellular decision-making, both intracellular and intercellular—how to apply mathematics of decision theory to the biological context.
  • The links between control theory, decision-making, and information theory, as applied to systems, evolutionary, and synthetic biology.

A major challenge in biology is to understand how cells sense and process extracellular signals. Cells in a developing organism must respond to positional cues to adopt appropriate cell fates; cancerous cells ignore restraining signals sent from the rest of the body; bacteria sense each other and launch a virulent attack only when their population reaches a critical density, or may switch to a persistent, non-growing mode. Recent experiments have shown that such responses are probabilistic, subject both to random fluctuations in the environment as well as to noise in the inter- and intra-cellular signaling mechanisms. While these biological processes are often explained in terms of “information processing” or “decision-making”, surprisingly little has been done to develop a sound theoretical framework for information processing and decision-making in biology in order to (i) make the relevant intuitions mathematically precise; (ii) develop principled methods to connect such formal frameworks to data.

The aim of our workshop is to bring experts in probability, information theory, and stochastic systems alongside experimental biologists to develop a common understanding of the principles underlying cellular signalling and decision-making and to develop techniques of quantitative analysis to experimentally investigate those principles.

Our workshop is particularly timely because of several recent and concurrent developments. First, experimental progress in single-cell imaging and microfluidic technology makes it possible to record stochastic fluctuations within single cells over time in response to controlled but fluctuating input signals. Second, the study of stochastic processes in biology has advanced to the point where we have mathematical frameworks to quantify noise, separate stochastic from signal components in cellular responses, and have experimental means to dissect the noise sources, with a physics-level of precision, in various regulatory and signaling networks. Third, to capture the consequences of such noise on communication and decision-making, the field has, in the past 5-10 years, witnessed the first successful applications of information theoretic approaches to the relevant cellular problems. Only within the last year or two has there been a series of high impact publications highlighting such applications, and a wider interdisciplinary audience of biologists, physicists, and mathematicians is starting to pay attention to recent developments.