Preface

Stephanopoulos, Gregory; Aristidou, Aristos; Nielsen, Jens

doi:10.1016/b978-012666260-3/50000-5

Cited by 67 publications

(89 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Applications of genetic engineering or metabolic engineering have increased in both academic and industrial institutions and the area has been reviewed (reviews: Stephanopoulos and Vallino, 1990;Bailey, 1990;Carmeron and Chaplen, 1997;Stephanopoulos et al, 1998;Lee and Papousakis, 1999). Most current metabolic engineering studies have mainly focused on manipulating enzyme levels through the amplification, addition, or deletion of a particular pathway.…”

Section: Introductionmentioning

confidence: 99%

Metabolic Engineering through Cofactor Manipulation and Its Effects on Metabolic Flux Redistribution in Escherichia coli

San

Bennett

Berrı́os-Rivera

et al. 2002

Metabolic Engineering

237

149

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Metabolic Engineering through Cofactor Manipulation and Its Effects on Metabolic Flux Redistribution in Escherichia coli

San

Bennett

Berrı́os-Rivera

et al. 2002

Metabolic Engineering

237

149

View full text Add to dashboard Cite

“…Cell maintenance could be compared with measured protein degradation and resynthesis rates. The ATP consumption associated with protein polymerization has been estimated to be 4.306 ATP equivalents for each amino acid added to the protein chain (Stephanopoulos et al, 1998), whilst the cost of protein degradation by the proteasome has been estimated to be two ATP molecules per peptide bond (Burton et al, 2001;Menon & Goldberg, 1987). In this work, we presume that the ATP-dependent proteasome degrades proteins into peptides with 10 aa and further degradation is carried out by ATP-independent peptidases.…”

mentioning

confidence: 99%

Protein turnover forms one of the highest maintenance costs in Lactococcus lactis

et al. 2014

View full text Add to dashboard Cite

Protein turnover plays an important role in cell metabolism by regulating metabolic fluxes. Furthermore, the energy costs for protein turnover have been estimated to account for up to a third of the total energy production during cell replication and hence may represent a major limiting factor in achieving either higher biomass or production yields. This work aimed to measure the specific growth rate (m)-dependent abundance and turnover rate of individual proteins, estimate the ATP cost for protein production and turnover, and compare this with the total energy balance and other maintenance costs. The lactic acid bacteria model organism Lactococcus lactis was used to measure protein turnover rates at m50.1 and 0.5 h "1 in chemostat experiments.Individual turnover rates were measured for~75 % of the total proteome. On average, protein turnover increased by sevenfold with a fivefold increase in growth rate, whilst biomass yield increased by 35 %. The median turnover rates found were higher than the specific growth rate of the bacterium, which suggests relatively high energy consumption for protein turnover. We found that protein turnover costs alone account for 38 and 47 % of the total energy produced at m50.1 and 0.5 h "1 , respectively, and gene ontology groups Energy metabolism and Translation dominated synthesis costs at both growth rates studied. These results reflect the complexity of metabolic changes that occur in response to changes in environmental conditions, and signify the trade-off between biomass yield and the need to produce ATP for maintenance processes. INTRODUCTIONRecently, protein turnover rates have been recognized as an important factor that provides growth advantage both in terms of higher maximal specific growth rate (m) and biomass yield between different strains (Hong et al., 2012), and in laboratory evolution experiments (González-Ramos et al., 2013). In addition, protein synthesis and degradation are firmly related and therefore represent an important control factor for metabolic regulation (Schwanhäusser et al., 2013). Individual protein turnover rates have been determined in mammalian cells, yeast and bacteria by measuring the incorporation rate of fluorescent tags (Khmelinskii et al., 2012), affinity tags (Belle et al., 2006) or isotopic labels into the proteome (methods reviewed by Hughes & Krijgsveld, 2012; Trötschel et al., 2013). The metabolic incorporation of isotopic labels is currently the most widely used method for cell cultures and conducted using either labelled ammonium (Helbig et al., 2011;Martin et al., 2012), carbon (Cargile et al., 2004) or amino acids (Gerth et al., 2008;Maier et al., 2011; Schwanhäusser et al., 2011). Protein turnover rates are predominantly measured during the logarithmic or stationary phase of batch growth and are assumed to be constant during this phase of growth.Only a few studies report protein degradation rates determined from a steady physiological state (Helbig et al., 2011; Pratt et al., 2002). Although overall protein turnover has been...

show abstract

“…This system of reactions can be modeled as ordinary differential equations, however the reaction rate constants and metabolite concentrations are typically difficult to obtain, thereby limiting their applicability to small well-studied networks. However, since the stoichiometry of metabolic reactions are not organism or context-dependent but is fixed by mass balance, one could apply Constraint Based Modeling (e.g., Flux Balance Analysis, FBA [ 18 ]) to simulate the state of the system without detailed kinetic data, assuming that the flux distributions based on the stoichiometric mass balance are at steady state or pseudo-steady state.…”

Section: Modeling and Simulation Based On Human Metabolic Networkmentioning

confidence: 99%

Human Metabolic Network: Reconstruction, Simulation, and Applications in Systems Biology

Chan

2012

Metabolites

View full text Add to dashboard Cite

Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system.

show abstract

Preface

Cited by 67 publications

References 0 publications

Metabolic Engineering through Cofactor Manipulation and Its Effects on Metabolic Flux Redistribution in Escherichia coli

Metabolic Engineering through Cofactor Manipulation and Its Effects on Metabolic Flux Redistribution in Escherichia coli

Protein turnover forms one of the highest maintenance costs in Lactococcus lactis

Human Metabolic Network: Reconstruction, Simulation, and Applications in Systems Biology

Contact Info

Product

Resources

About