FN Archimer Export Format
PT J
TI Twelve quick tips for designing sound dynamical models for bioprocesses
BT 
AF Mairet, Francis
   Bernard, Olivier
AS 1:1;2:2,3,4;
FF 1:PDG-RBE-BRM-LPBA;2:;
C1 Ifremer, Physiology and Biotechnology of Algae laboratory, Nantes, France
   Cote d’Azur University, INRIA, BIOCORE, Sophia-Antipolis Cedex, France
   Sorbonne University, CNRS, LOV, Villefranche sur mer, France
   ENERSENSE, Department of Energy and Process Engineering, NTNU, Trondheim, Norway
C2 IFREMER, FRANCE
   INRIA, FRANCE
   UNIV PARIS 06, FRANCE
   UNIV SCI & TECHNOL NORWEGIAN, NORWAY
SI NANTES
SE PDG-RBE-BRM-LPBA
IN WOS Ifremer UPR
   DOAJ
   copubli-france
   copubli-europe
   copubli-univ-france
IF 4.829
TC 8
UR https://archimer.ifremer.fr/doc/00512/62336/66596.pdf
LA English
DT Article
AB Because of the inherent complexity of bioprocesses, mathematical models are more and more used for process design, control, optimization, etc. These models are generally based on a set of biochemical reactions. Model equations are then derived from mass balance, coupled with empirical kinetics. Biological models are nonlinear and represent processes, which by essence are dynamic and adaptive. The temptation to embed most of the biology is high, with the risk that calibration would not be significant anymore. The most important task for a modeler is thus to ensure a balance between model complexity and ease of use. Since a model should be tailored to the objectives, which will depend on applications and environment, a universal model representing any possible situation is probably not the best option. 
PY 2019
PD AUG
SO Plos Computational Biology
SN 1553-7358
PU Public Library of Science (PLoS)
VL 15
IS 8
UT 000489735000024
DI 10.1371/journal.pcbi.1007222
ID 62336
ER

EF