Building kinetic models for metabolic engineering

被引:40
作者
Foster, Charles J. [1 ]
Wang, Lin [1 ]
Dinh, Hoang, V [1 ,2 ]
Suthers, Patrick F. [1 ,2 ]
Maranas, Costas D. [1 ]
机构
[1] Penn State Univ, Dept Chem Engn, University Pk, PA 16802 USA
[2] Penn State Univ, DOE Ctr Adv Bioenergy & Bioprod Innovat, University Pk, PA 16802 USA
关键词
CENTRAL CARBON METABOLISM; YEAST GLYCOLYSIS;
D O I
10.1016/j.copbio.2020.11.010
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this review we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.
引用
收藏
页码:35 / 41
页数:7
相关论文
共 62 条
[1]   iSCHRUNK - In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks [J].
Andreozzi, Stefano ;
Miskovic, Ljubisa ;
Hatzimanikatis, Vassily .
METABOLIC ENGINEERING, 2016, 33 :158-168
[2]   The Moderately Efficient Enzyme: Evolutionary and Physicochemical Trends Shaping Enzyme Parameters [J].
Bar-Even, Arren ;
Noor, Elad ;
Savir, Yonatan ;
Liebermeister, Wolfram ;
Davidi, Dan ;
Tawfik, Dan S. ;
Milo, Ron .
BIOCHEMISTRY, 2011, 50 (21) :4402-4410
[3]   JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language [J].
Bassen, David M. ;
Vilkhovoy, Michael ;
Minot, Mason ;
Butcher, Jonathan T. ;
Varner, Jeffrey D. .
BMC SYSTEMS BIOLOGY, 2017, 11
[4]  
Berg JM, 2002, ISOZYMES PROVIDE MEA
[5]   COPASI and its applications in biotechnology [J].
Bergmann, Frank T. ;
Hoops, Stefan ;
Klahn, Brian ;
Kummer, Ursula ;
Mendes, Pedro ;
Pahle, Juergen ;
Sahle, Sven .
JOURNAL OF BIOTECHNOLOGY, 2017, 261 :215-220
[6]   Dynamic modeling of the central carbon metabolism of Escherichia coli [J].
Chassagnole, C ;
Noisommit-Rizzi, N ;
Schmid, JW ;
Mauch, K ;
Reuss, M .
BIOTECHNOLOGY AND BIOENGINEERING, 2002, 79 (01) :53-73
[7]   Reserve Flux Capacity in the Pentose Phosphate Pathway Enables Escherichia coli's Rapid Response to Oxidative Stress [J].
Christodoulou, Dimitris ;
Link, Hannes ;
Fuhrer, Tobias ;
Kochanowski, Karl ;
Gerosa, Luca ;
Sauer, Uwe .
CELL SYSTEMS, 2018, 6 (05) :569-+
[8]   KiMoSys: a web-based repository of experimental data for KInetic MOdels of biological SYStems [J].
Costa, Rafael S. ;
Verissimo, Andre ;
Vinga, Susana .
BMC SYSTEMS BIOLOGY, 2014, 8
[9]   Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations [J].
Dash, Satyakam ;
Khodayari, Ali ;
Zhou, Jilai ;
Holwerda, Evert K. ;
Olson, Daniel G. ;
Lynd, Lee R. ;
Maranas, Costas D. .
BIOTECHNOLOGY FOR BIOFUELS, 2017, 10
[10]   Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements [J].
Davidi, Dan ;
Noor, Elad ;
Liebermeister, Wolfram ;
Bar-Even, Arren ;
Flamholz, Avi ;
Tummler, Katja ;
Barenholz, Uri ;
Goldenfeld, Miki ;
Shlomi, Tomer ;
Milo, Ron .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2016, 113 (12) :3401-3406