scholarly journals Vitreoscilla Haemoglobin: A Tool to Reduce Overflow Metabolism

2021 ◽  
Vol 10 (1) ◽  
pp. 43
Author(s):  
Hilal Taymaz-Nikerel ◽  
Alvaro R. Lara
Keyword(s):  
Large Scale ◽  
Overflow Metabolism ◽  
Acetate Production ◽  
E Coli ◽  
Flux Balance ◽  
Uptake Rates ◽  
Reported Effect ◽  
Balance Analysis ◽  
Underlying Mechanisms ◽  
Potential Tool

Overflow metabolism is a phenomenon extended in nature, ranging from microbial to cancer cells. Accumulation of overflow metabolites pose a challenge for large-scale bioprocesses. Yet, the causes of overflow metabolism are not fully clarified. In this work, the underlying mechanisms, reasons and consequences of overflow metabolism in different organisms have been summarized. The reported effect of aerobic expression of Vitreoscilla haemoglobin (VHb) in different organisms are revised. The use of VHb to reduce overflow metabolism is proposed and studied through flux balance analysis in E. coli at a fixed maximum substrate and oxygen uptake rates. Simulations showed that the presence of VHb increases the growth rate, while decreasing acetate production, in line with the experimental measurements. Therefore, aerobic VHb expression is considered a potential tool to reduce overflow metabolism in cells.

scholarly journals FastMM: an efficient toolbox for personalized constraint-based metabolic modeling

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Gong-Hua Li ◽  
Shaoxing Dai ◽  
Feifei Han ◽  
Wenxing Li ◽  
Jingfei Huang ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Large Scale ◽  
Computing Time ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Flux Balance ◽  
Mcmc Sampling ◽  
Genome Wide ◽  
Balance Analysis ◽  
User Friendly

Abstract Background Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. Results Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). Conclusion FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

scholarly journals Flux Balance Analysis of Ammonia Assimilation Network in E. coli Predicts Preferred Regulation Point

PLoS ONE ◽  
2011 ◽  
Vol 6 (1) ◽  
pp. e16362 ◽  
Author(s):  
Lu Wang ◽  
Luhua Lai ◽  
Qi Ouyang ◽  
Chao Tang
Keyword(s):  
Flux Balance Analysis ◽  
Ammonia Assimilation ◽  
E Coli ◽  
Flux Balance ◽  
Balance Analysis

scholarly journals Effective Dynamic Models of Metabolic Networks

2016 ◽  
Author(s):  
Michael Vilkhovoy ◽  
Mason Minot ◽  
Jeffrey D. Varner
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Dynamic Models ◽  
Biochemical Networks ◽  
Promising Alternative ◽  
E Coli ◽  
Flux Balance ◽  
Comparable Performance ◽  
Elementary Modes ◽  
Balance Analysis

AbstractMathematical models of biochemical networks are useful tools to understand and ultimately predict how cells utilize nutrients to produce valuable products. Hybrid cybernetic models in combination with elementary modes (HCM) is a tool to model cellular metabolism. However, HCM is limited to reduced metabolic networks because of the computational burden of calculating elementary modes. In this study, we developed the hybrid cybernetic modeling with flux balance analysis or HCM-FBA technique which uses flux balance solutions instead of elementary modes to dynamically model metabolism. We show HCM-FBA has comparable performance to HCM for a proof of concept metabolic network and for a reduced anaerobicE. colinetwork. Next, HCM-FBA was applied to a larger metabolic network of aerobicE. colimetabolism which was infeasible for HCM (29 FBA modes versus more than 153,000 elementary modes). Global sensitivity analysis further reduced the number of FBA modes required to describe the aerobicE. colidata, while maintaining model fit. Thus, HCM-FBA is a promising alternative to HCM for large networks where the generation of elementary modes is infeasible.

Immunogold labeling of CMP-KDO synthetase produced by recombinant E. Coli cells

1987 ◽  
Vol 45 ◽  
pp. 924-925
Author(s):  
F. A. Durum ◽  
R. G. Goldman ◽  
T. J. Bolling ◽  
M. F. Miller
Keyword(s):  
Inclusion Bodies ◽  
Large Scale ◽  
Recombinant Dna ◽  
Test System ◽  
Cell Protein ◽  
Gram Negative Bacteria ◽  
Cytoplasmic Inclusions ◽  
Isolation And Purification ◽  
E Coli ◽  
Low Concentrations

CMP-KDO synthetase (CKS) is an enzyme which plays a key role in the synthesis of LPS, an outer membrane component unique to gram negative bacteria. CKS activates KDO to CMP-KDO for incorporation into LPS. The enzyme is normally present in low concentrations (0.02% of total cell protein) which makes it difficult to perform large scale isolation and purification. Recently, the gene for CKS from E. coli was cloned and various recombinant DNA constructs overproducing CKS several thousandfold (unpublished data) were derived. Interestingly, no cytoplasmic inclusions of overproduced CKS were observed by EM (Fig. 1) which is in contrast to other reports of large proteinaceous inclusion bodies in various overproducing recombinant strains. The present immunocytochemical study was undertaken to localize CKS in these cells.Immune labeling conditions were first optimized using a previously described cell-free test system. Briefly, this involves soaking small blocks of polymerized bovine serum albumin in purified CKS antigen and subjecting them to various fixation, embedding and immunochemical conditions.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

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