Julian Lewis, "Autoinhibition with transcriptional delay: a simple mechanism for the zebrafish somitogenesis oscillator", Current Biology 13:1398-408 2003. PubMed
We study the systems biology of signal transduction in eukaryotic (metazoan) cells.
Cells use transmembrane receptors and channels to sense their external environment and somehow transduce these signals into decisions about whether to grow, move, divide, die, hunt, differentiate, ... . While much is now known about the molecular components involved in this information processing, we lack an understanding of how the system of interacting components produces the phenotypes seen in nature.
What we mean by understanding is something like what engineers mean when they say they understand how a radio works†: why the various components and sub-systems are there; what happens when the system is modified in some way; how to fix it when it breaks. This is a preliminary definition which is good enough to get us started. However, biological systems are not just mechanisms; their organisation and function reflects their evolvability. We do not yet understand how to integrate evolution into such a mechanistic framework‡.
†Yuri Lazebnik, "Can a biologist fix a radio? Or, what I learned when studying apoptosis", Cancer Cell 2:179-82 2002. PubMed
‡Marc Kirschner & John Gerhart, The Plausibility of Life: Resolving Darwin's Dilemma, Yale University Press 2005. Amazon
We approach cellular decision making from several directions. We undertake experiments to quantify and characterise phenotypic behaviour, particularly dynamical behaviour in response to signals. Data at single cell resolution is essential to relate phenotypic responses to molecular mechanisms. However, single cells, even those which arise clonally from a single precursor, can exhibit substantial cell-to-cell variability and we also seek to take this phenotypic diversity into account.
The data and insights from experiments help us build mathematical models of signal transduction systems. We believe this is crucial to understanding them in the ways described above. Even simple systems can exhibit non-obvious behaviours, which may be difficult to unravel using informal, intuitive reasoning§. By disentangling the behaviours mathematically, we develop a better intuition for how the system might behave and this guides the experiments we do to understand how it does behave.
§Dietrich Braess, "Über ein Paradoxon aus der Verkehrsplanung", Unternehmensforschung 12:258-68 1968.
Julian Lewis, "Autoinhibition with transcriptional delay: a simple mechanism for the zebrafish somitogenesis oscillator", Current Biology 13:1398-408 2003. PubMed
FCS & scaffolds We are developing Fluorescence Correlation Spectroscopy (FCS) and its modern variants (Fluorescence Cross Correlation Spectroscopy) as a general methodology for determining protein numbers and protein-protein associations in single living cells. Fluctuations in molecular fluorescence from small volumes (~0.2 fL) appear noisy but conceal information on the processes causing the fluctuations: small molecular numbers, diffusion, chemical reactions, photophysics, etc. Correlation methods extract signals from the noise. Scaffold proteins usually have no enzymatic function but serve to bind other proteins. KSR (Kinase Suppressor of RAS) binds members of the metazoan MAP kinase cascade, downstream of growth factor receptors like EGFR. Why is the EGF pathway organised this way? What role do scaffolds play? We are using FCS to study the molecular organisation and behaviour of KSR. |
FCS curve of tdimer2 in the cytoplasm of a stably transfected Hela cell, showing a tdimer2 concentration of 0.3 μM and a diffusion coefficient of 3.3 x 10-7 cm2/sec. 120 measurements each of 5 seconds were taken at a laser power of 1 kW/cm2. |
Quantifying input-output responses We use immunostaining against multiple pathway components and high-throughput cell screening to get an overall quantitative picture of the signalling phenotype. While the dynamics of what happens in a single cell has to be inferred from snapshots of different cell populations at different times, this method gives a synoptic view of pathway behaviour and cell-to-cell variation.
Multisite phosphorylation Many proteins are phosphorylated, some heavily, and the information thus encoded is critical to cellular informtion processing. A single substrate molecule with n sites has 2n phosphorylation states. Although we know increasingly more about which sites are phosphorylated, we have little data about the proportions of phosphorylation states in the substrate population. These are regulated by the dynamical tug-of-war between kinases and phosphatases. We are studying this theoretically& and also developing new experimental methods for assaying phosphorylation patterns. &Jeremy Gunawardena, "Multisite protein phosphorylation makes a good threshold but can be a poor switch", PNAS 102:14617-22 2005. PDF |
Steady state response of maximally phosphorylated phospho-form, as a function of the ratio, u, of free kinase, [E], to free phosphatase, [F], for different numbers, n, of phosphorylation sites. |
Modular models in little b We have designed a computational infrastructure that allows models to be built in an incremental and modular fashion. We think something like this will be essential if model building is to evolve from an activity carried out by computational biologists to an everyday practice among biologists. The infrastructure consists of a programming language, little b, written in LISP, together with a library of descriptions of biological entities. We use little b to help us incrementally build models of the systems we study experimentally.
modularity and little b
examples of what can be achieved with little b (coming soon)
www.littleb.org description of little b and its semantics (under construction)