FN Archimer Export Format
PT J
TI Dynamical Allocation of Cellular Resources as an Optimal Control Problem: Novel Insights into Microbial Growth Strategies
BT 
AF GIORDANO, Nils
   MAIRET, Francis
   GOUZE, Jean-Luc
   GEISELMANN, Johannes
   DE JONG, Hidde
AS 1:1,2;2:3;3:3;4:1,2;5:2;
FF 1:;2:;3:;4:;5:;
C1 Université Grenoble Alpes, Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), Saint Martin d’Hères, France
   Inria, Grenoble - Rhône-Alpes research centre, Montbonnot, Saint Ismier Cedex, France
   Inria, Sophia-Antipolis Méditerranée research centre, Sophia-Antipolis Cedex, France
C2 UNIV GRENOBLE ALPES, FRANCE
   INRIA, FRANCE
   INRIA, FRANCE
IN DOAJ
IF 4.542
TC 58
UR https://archimer.ifremer.fr/doc/00379/49020/49480.pdf
   https://archimer.ifremer.fr/doc/00379/49020/49481.pdf
   https://archimer.ifremer.fr/doc/00379/49020/49482.pdf
   https://archimer.ifremer.fr/doc/00379/49020/49483.pdf
   https://archimer.ifremer.fr/doc/00379/49020/49484.pdf
   https://archimer.ifremer.fr/doc/00379/49020/49485.pdf
   https://archimer.ifremer.fr/doc/00379/49020/49486.pdf
LA English
DT Article
AB Microbial physiology exhibits growth laws that relate the macromolecular composition of the cell to the growth rate. Recent work has shown that these empirical regularities can be derived from coarse-grained models of resource allocation. While these studies focus on steady-state growth, such conditions are rarely found in natural habitats, where microorganisms are continually challenged by environmental fluctuations. The aim of this paper is to extend the study of microbial growth strategies to dynamical environments, using a self-replicator model. We formulate dynamical growth maximization as an optimal control problem that can be solved using Pontryagin's Maximum Principle. We compare this theoretical gold standard with different possible implementations of growth control in bacterial cells. We find that simple control strategies enabling growth-rate maximization at steady state are suboptimal for transitions from one growth regime to another, for example when shifting bacterial cells to a medium supporting a higher growth rate. A near-optimal control strategy in dynamical conditions is shown to require information on several, rather than a single physiological variable. Interestingly, this strategy has structural analogies with the regulation of ribosomal protein synthesis by ppGpp in the enterobacterium Escherichia coli. It involves sensing a mismatch between precursor and ribosome concentrations, as well as the adjustment of ribosome synthesis in a switch-like manner. Our results show how the capability of regulatory systems to integrate information about several physiological variables is critical for optimizing growth in a changing environment. 
PY 2016
PD MAR
SO Plos Computational Biology
SN 1553-734X
VL 12
IS 3
UT 000376583800014
DI 10.1371/journal.pcbi.1004802
ID 49020
ER

EF