Speaker
David Lacoste
(ESPCI)
Description
Inferring the directionality of interactions between
cellular processes is a major challenge in systems biology.
Time-lagged correlations allow to discriminate between
alternative models, but they still rely on assumed
underlying interactions. Here, we show that an
information-theoretic quantity, the transfer entropy (TE),
quantifies the directional influence between fluctuating
variables in a model-free way. We present a theoretical
approach to compute the transfer entropy, even when the
noise has an extrinsic component or in the presence of
feedback. We re-analyze the experimental data from Kiviet et
al. (2014) [1], where fluctuations in gene expression of
metabolic enzymes and growth rate have been measured in
single cells of Escherichia coli. We confirm the formerly
detected modes between growth and gene expression, while
prescribing more stringent conditions on the structure of
noise sources [2].
Time permitting, I will also present a different project
related to the kinetics and thermodynamics of reversible
polymerization. More specifically, we are interested in the
relaxation dynamics of information carrying polymers
undergoing reversible exchange reactions [3].
[1] Stochasticity of metabolism and growth at the
single-cell level, D. J. Kiviet et al., Nature, 514, 376 (2014).
[2] Information theoretic analysis of the directional influence
between cellular processes, S. Lahiri et al., PLOS ONE,
under review (2017)
[3] Length and sequence relaxation of copolymers under
recombination reactions, A. Blokhuis and D. Lacoste, J.
Chem. Phys., in press (2017)
