Information, Probability and Inference in Systems Biology

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Information, Probability and Inference in Systems Biology

 15 - 17 Jul 2013

ICMS, 15 South College Street Edinburgh

Scientific Organisers:

  • Clive Bowsher, University of Bristol
  • Caroline Colijn, Imperial College London
  • Peter Swain, University of Edinburgh

About:

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

Key topics addressed include:

  • New information theoretic approaches to sensing and signal transduction by cells with an emphasis on dynamic problems

  • Study of stochastic biochemical networks

  • Inferential methods for statistical modelling of time series data from single cells

  • Cellular decision-making, both intracellular and intercellular

  • Design of signal transduction networks for use in synthetic biology

Speakers:

  • Clive Bowsher, University of Bristol - The Fidelity of Dynamic Signaling by Noisy Biomolecular Networks

  • Caroline Colijn, Imperial College, London - Pertussis Decisions: Can We Use Metabolic Models to Understand Two Modes of Growth?

  • Robert Endres, Imperial College, London - Precision of Sensing with Memory in Fluctuating Environments

  • Ramon Grima, University of Edinburgh - Beyond the Linear-Noise Approximation of Stochastic Biochemical Networks

  • Des Higham, Strathclyde University - Multi-Level Monte Carlo for Stochastic Simulation

  • Martin Howard, John Innes Centre - Implementation of aAnalogue Arithmetic Division in Food Reserve Utilisation

  • Nick Jones,  Imperial College, London - Mitochondrial Variability

  • Mustafa Khammash, ETH-Zurich - Cybergenetics: Manipulating the Dynamic Behavior of Living Cells Through Feedback Control

  • Tetsuya Kobayashi, University of Tokyo - Dynamics of Bayesian inference and its Application to Cellular Decision-Making

  • Heinz Koeppl, ETH-Zurich - Statistical Inference of Cellular Behavior from Single-Cell Data

  • Markus Kollmann, Universitat Duesseldorf - Scaling up the Regulatory Complexity of the Living Cell

  • Andre Levchenko, Johns Hopkins University

  • Ilya Nemenman, Emory Unviersity - Inferring Phenomenological Models of Cellular Regulation from Data

  • Ted Perkins, Ottawa Hospital Research Institute

  • Hong Qian, University of Washington - Stochastic Dynamics and Cellular Biochemical Systems

  • David Rand, University of Warwick - Design Principles and Dynamics in Clocks, Cell Cycles and Signals

  • Michael Stumpf, Imperial College, London - Analog Versus Digital Information Processing in Cellular Systems

  • Gasper Tkacik, IST Austria - Information Flow and Optimization in Small Genetic Circuits

  • Lev Tsimring, University of California - Synchronization of Synthetic Gene Oscillators

  • Vladislav Vyshemirsky, University of Strathclyde - Modelling Growth Dynamics of Solid Tumours

  • Hans Westerhoff, University of Manchester - The Arrow of Time: How Pointed Can It Be in a Molecular World?

  • Darren Wilkinson, Newcastle University - Bayesian Inference for Markov Process Models of Stochastic Biochemical Network Dynamics

  • Ruth Williams, University of California - Correlation Effects of Intracellular Components due to Limited Processing Resources

  • Carsten Wiuf, University of Copenhagen- Model Reduction in Chemical Reaction Networks

  • Theodore Perkins, Ottawa Hospital Research Institute - What Do Molecules Do When We're Not Looking? State Sequence Analysis for Stochastic Chemical Systems

  • Pieter Rein Ten Wolde, AMOLF - Protein Clustering Improves Signaling Reliability