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 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 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.

Gene knockout identification for metabolite production improvement using a hybrid of genetic ant colony optimization and flux balance analysis

2015 ◽  
Vol 20 (4) ◽  
pp. 685-693 ◽  
Author(s):  
Abdul Hakim Mohamed Salleh ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Sigeru Omatu ◽  
Florentino Fdez-Riverola ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Flux Balance Analysis ◽  
Gene Knockout ◽  
Ant Colony ◽  
Metabolite Production ◽  
Flux Balance ◽  
Production Improvement ◽  
Balance Analysis

Identifying a gene knockout strategy using a hybrid of the bat algorithm and flux balance analysis to enhance the production of succinate and lactate in Escherichia coli

2015 ◽  
Vol 20 (2) ◽  
pp. 349-357 ◽  
Author(s):  
Pooi San Chua ◽  
Abdul Hakim Mohamed Salleh ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Sigeru Omatu ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Flux Balance Analysis ◽  
Gene Knockout ◽  
Bat Algorithm ◽  
Flux Balance ◽  
Balance Analysis

scholarly journals Minimizing the number of optimizations for efficient community dynamic flux balance analysis

2020 ◽  
Author(s):  
James D. Brunner ◽  
Nicholas Chia
Keyword(s):  
Flux Balance Analysis ◽  
Metabolic Activity ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Computational Time ◽  
Time Interval ◽  
Time Step ◽  
Dynamic Flux Balance Analysis ◽  
Flux Balance ◽  
Balance Analysis

AbstractDynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism’s metabolic activity at each time-step of a dynamic simulation, using the well-know technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps, instead of the full optimization problem. Using our method, we can use this solution as long as the resulting metabolic activity remains within the optimization problem’s constraints (i.e. the solution remains feasible). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We show using an eight species community that this is an efficient and robust method for computing dynamic flux balance analysis simulations, and so is capable of simulating communities of organisms. We demonstrate that the method gives an approximately 85% speed-up per organism over the standard and widely used method. Our method has been implemented in the Python language and source code is available at https://github.com/jdbrunner/surfin_fba and in the Python Package Index (PyPI) as surfinFBA.Author summaryThe standard method in the field for dynamic flux balance analysis carries a prohibitively high computational cost because it requires solving a linear optimization problem at each time-step. We have developed a novel method for producing solutions to this dynamical system which greatly reduces the number of optimization problems that must be solved. We prove mathematically that we can solve the optimization problem once and simulate the system forward as an ordinary differential equation for some time interval, and solutions to this ODE provide solutions to the optimization problem. Eventually, the system reaches an easily checkable condition which implies that another optimization problem must be solved. We compare our method with the classical method to validate that it provides equivalent solutions in much lower computational time.

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