scholarly journals Quantitative Connection between Cell Size and Growth Rate by Phospholipid Metabolism

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 391 ◽  
Author(s):  
Zhichao Zhang ◽  
Qing Zhang ◽  
Shaohua Guan ◽  
Hualin Shi
Keyword(s):  
Escherichia Coli ◽  
Growth Rate ◽  
Cell Size ◽  
Flux Balance Analysis ◽  
Phospholipid Metabolism ◽  
Cell Length ◽  
Synthesis Rate ◽  
Phospholipid Synthesis ◽  
Flux Balance ◽  
Balance Analysis

The processes involved in cell growth are extremely complicated even for a single cell organism such as Escherichia coli, while the relationship between growth rate and cell size is simple. We aimed to reveal the systematic link between them from the aspect of the genome-scale metabolic network. Since the growth rate reflects metabolic rates of bacteria and the cell size relates to phospholipid synthesis, a part of bacterial metabolic networks, we calculated the cell length from the cardiolipin synthesis rate, where the cardiolipin synthesis reaction is able to represent the phospholipid metabolism of Escherichia coli in the exponential growth phase. Combined with the flux balance analysis, it enables us to predict cell length and to examine the quantitative relationship between cell length and growth rate. By simulating bacteria growing in various nutrient media with the flux balance analysis and calculating the corresponding cell length, we found that the increase of the synthesis rate of phospholipid, the cell width, and the protein fraction in membranes caused the increase of cell length with growth rate. Different tendencies of phospholipid synthesis rate changing with growth rate result in different relationships between cell length and growth rate. The effects of gene deletions on cell size and growth rate are also examined. Knocking out the genes, such as Δ tktA, Δ tktB, Δ yqaB, Δ pgm, and Δ cysQ, affects growth rate largely while affecting cell length slightly. Results of this method are in good agreement with experiments.

Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation

2010 ◽  
Vol 12 (2) ◽  
pp. 150-160 ◽  
Author(s):  
Adam L. Meadows ◽  
Rahi Karnik ◽  
Harry Lam ◽  
Sean Forestell ◽  
Brad Snedecor
Keyword(s):  
Escherichia Coli ◽  
Flux Balance Analysis ◽  
Dynamic Flux Balance Analysis ◽  
Flux Balance ◽  
Balance Analysis

scholarly journals Threshold effect of growth rate on population variability of Escherichia coli cell lengths

2017 ◽  
Vol 4 (2) ◽  
pp. 160417 ◽  
Author(s):  
Manasi S. Gangan ◽  
Chaitanya A. Athale
Keyword(s):  
Escherichia Coli ◽  
Growth Rate ◽  
Cell Size ◽  
Growth Rates ◽  
Threshold Effect ◽  
Cell Length ◽  
Replication Forks ◽  
E Coli ◽  
Cell Size Distributions ◽  
Rate Threshold

A long-standing question in biology is the effect of growth on cell size. Here, we estimate the effect of Escherichia coli growth rate ( r ) on population cell size distributions by estimating the coefficient of variation of cell lengths (CV L ) from image analysis of fixed cells in DIC microscopy. We find that the CV L is constant at growth rates less than one division per hour, whereas above this threshold, CV L increases with an increase in the growth rate. We hypothesize that stochastic inhibition of cell division owing to replication stalling by a RecA-dependent mechanism, combined with the growth rate threshold of multi-fork replication (according to Cooper and Helmstetter), could form the basis of such a threshold effect. We proceed to test our hypothesis by increasing the frequency of stochastic stalling of replication forks with hydroxyurea (HU) treatment and find that cell length variability increases only when the growth rate exceeds this threshold. The population effect is also reproduced in single-cell studies using agar-pad cultures and ‘mother machine’-based experiments to achieve synchrony. To test the role of RecA, critical for the repair of stalled replication forks, we examine the CV L of E. coli ΔrecA cells. We find cell length variability in the mutant to be greater than wild-type, a phenotype that is rescued by plasmid-based RecA expression. Additionally, we find that RecA-GFP protein recruitment to nucleoids is more frequent at growth rates exceeding the growth rate threshold and is further enhanced on HU treatment. Thus, we find growth rates greater than a threshold result in increased E. coli cell lengths in the population, and this effect is, at least in part, mediated by RecA recruitment to the nucleoid and stochastic inhibition of division.

The Metabolic Capabilities of Two Cells in Enterobacteria

2012 ◽  
Vol 424-425 ◽  
pp. 420-423
Author(s):  
Qing Hua Zhou ◽  
Xiao Dian Sun ◽  
Yan Li
Keyword(s):  
Escherichia Coli ◽  
Mathematical Models ◽  
Flux Balance Analysis ◽  
Metabolic Pathway ◽  
Buchnera Aphidicola ◽  
Flux Balance ◽  
Analysis Methods ◽  
Balance Analysis

In this paper, we investigate the metabolic capabilities of two kinds cells belong to enterbacteria. Firstly we develop the mathematical models for Escherichia coli and Buchnera aphidicola Cc based on Flux balance analysis methods. Then we study their capacity of producing the important metabolite Ethanol. Finally, the results show that if the metabolic pathway is more complicated, then more the terminal metabolite-AcCoA is produced.

scholarly journals Grid-based computational methods for the design of constraint-based parsimonious chemical reaction networks to simulate metabolite production: GridProd

2017 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Growth Rate ◽  
Metabolic Network ◽  
Flux Balance Analysis ◽  
Efficient Method ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Flux Distribution ◽  
Production Rates ◽  
Flux Balance ◽  
Balance Analysis

AbstractConstraint-based metabolic flux analysis of knockout strategies is an efficient method to simulate the production of useful metabolites in microbes. Owing to the recent development of technologies for artificial DNA synthesis, it may become important in the near future to mathematically design minimum metabolic networks to simulate metabolite production. Accordingly, we have developed a computational method where parsimonious metabolic flux distribution is computed for designated constraints on growth and production rates which are represented by grids. When the growth rate of this obtained parsimonious metabolic network is maximized, higher production rates compared to those noted using existing methods are observed for many target metabolites. The set of reactions used in this parsimonious flux distribution consists of reactions included in the original genome scale model iAF1260. The computational experiments show that the grid size affects the obtained production rates. Under the conditions that the growth rate is maximized and the minimum cases of flux variability analysis are considered, the developed method produced more than 90% of metabolites, while the existing methods produced less than 50%. Mathematical explanations using examples are provided to demonstrate potential reasons for the ability of the proposed algorithm to identify design strategies that the existing methods could not identify. The source code is freely available, and is implemented in MATLAB and COBRA toolbox.Author summaryMetabolic networks represent the relationships between biochemical reactions and compounds in living cells. By computationally modifying a given metabolic network of microbes, we can simulate the effect of knockouts and estimate the production of valuable metabolites. A common mathematical model of metabolic networks is the constraint-based flux model. In constraint-based flux balance analysis, a pseudo-steady state is assumed to predict the metabolic profile where the sum of all incoming fluxes is equal to the sum of all outgoing fluxes for each internal metabolite. Based on these constraints, the biomass objective function, written as a linear combination of fluxes, is maximized. In this study, we developed an efficient method for computing the design of minimum metabolic networks by using constraint-based flux balance analysis to simulate the production of useful metabolites.

scholarly journals Gene Knockout Identification Using an Extension of Bees Hill Flux Balance Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yee Wen Choon ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Chuii Khim Chong ◽  
Sigeru Omatu ◽  
...  
Keyword(s):  
Growth Rate ◽  
Flux Balance Analysis ◽  
Gene Knockout ◽  
Combinatorial Problem ◽  
Production Yield ◽  
Model Organisms ◽  
Computational Time ◽  
Vast Number ◽  
Flux Balance ◽  
Balance Analysis

Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted onEscherichia coli, Bacillus subtilis, andClostridium thermocellumas model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.

scholarly journals Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli

Metabolites ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 198 ◽  
Author(s):  
Yuki Kuriya ◽  
Michihiro Araki
Keyword(s):  
Escherichia Coli ◽  
Flux Balance Analysis ◽  
Acid Production ◽  
Shikimic Acid ◽  
Strain Improvement ◽  
Glucose Consumption ◽  
Production Performance ◽  
Flux Balance ◽  
Balance Analysis ◽  
Shikimic Acid Production

Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.

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