Natural computation meta-heuristics for the in silico optimization of microbial strains

Background  

One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.     

Metabolic flux analysis of carbon balance in Lactobacillus strains

Metabolic flux analyses were performed based on the carbon balance of six different Lactobacillus strains used in this study. Results confirmed that L. delbrueckii, L. plantarum ATCC 21028, L. plantarum NCIMB 8826 ΔldhL1, L. plantarum NCIMB 8826 ΔldhL1‐pCU‐PxylAB, and L. plantarum NCIMB 8826 ΔldhL1‐pLEM415‐xylAB metabolized glucose via EMP: whereas, L. brevis metabolized glucose via PK pathway. Xylose was metabolized through the PK pathway in L. brevis, L. plantarum NCIMB 8826 ΔldhL1‐pCU‐PxylAB and L. plantarum NCIMB 8826 ΔldhL1‐pLEM415‐xylAB. Operation of both EMP and PK pathways was found in L. brevis, L. plantarum NCIMB 8826 ΔldhL1‐pCU‐PxylAB, and L. plantarum NCIMB 8826 ΔldhL1‐pLEM415‐xylAB when glucose plus xylose were used as carbon source. The information of detailed carbon flow may help the strain and biomass selection in a designed process of lactic acid biosynthesis. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1397–1403, 2016     

A reliable simulator for dynamic flux balance analysis

Dynamic flux balance analysis (DFBA) provides a platform for detailed design, control and optimization of biochemical process technologies. It is a promising modeling framework that combines genome‐scale metabolic network analysis with dynamic simulation of the extracellular environment. Dynamic flux balance analysis assumes that the intracellular species concentrations are in equilibrium with the extracellular environment. The resulting underdetermined stoichiometric model is solved under the assumption of a biochemical objective such as growth rate maximization. The model of the metabolism is coupled with the dynamic mass balance equations of the extracellular environment via expressions for the rates of substrate uptake and product excretion, which imposes additional constraints on the linear program (LP) defined by growth rate maximization of the metabolism. The linear program is embedded into the dynamic model of the bioreactor, and together with the additional constraints this provides an accurate model of the substrate consumption, product secretion, and biomass production during operation. A DFBA model consists of a system of ordinary differential equations for which the evaluation of the right‐hand side requires not only function evaluations, but also the solution of one or more linear programs. The numerical tool presented here accurately and efficiently simulates large‐scale dynamic flux balance models. The main advantages that this approach has over existing implementation are that the integration scheme has a variable step size, that the linear program only has to be solved when qualitative changes in the optimal flux distribution of the metabolic network occur, and that it can reliably simulate behavior near the boundary of the domain where the model is defined. This is illustrated through large‐scale examples taken from the literature. Biotechnol. Bioeng. 2013; 110: 792–802. © 2012 Wiley Periodicals, Inc.     

Dynamic flux balance analysis for microbial conversion of glycerol into 1,3-propanediol by Klebsiella pneumoniae

Bioprocess and Biosystems Engineering - To investigate the relationship between the yield of 1,3-propanediol (1,3-PD) and the flux variation in metabolic pathways of Klebsiella pneumoniae, an...     

Metabolism as means for hypoxia adaptation: metabolic profiling and flux balance analysis

Background  

Cellular hypoxia is a component of many diseases, but mechanisms of global hypoxic adaptation and resistance are not completely understood. Previously, a population of Drosophila flies was experimentally selected over several generations to survive a chronically hypoxic environment. NMR-based metabolomics, combined with flux-balance simulations of genome-scale metabolic networks, can generate specific hypotheses for global reaction fluxes within the cell. We applied these techniques to compare metabolic activity during acute hypoxia in muscle tissue of adapted versus "na?ve" control flies.     

Escher-FBA: a web application for interactive flux balance analysis

Background

Flux balance analysis (FBA) is a widely-used method for analyzing metabolic networks. However, most existing tools that implement FBA require downloading software and writing code. Furthermore, FBA generates predictions for metabolic networks with thousands of components, so meaningful changes in FBA solutions can be difficult to identify. These challenges make it difficult for beginners to learn how FBA works.

Results

To meet this need, we present Escher-FBA, a web application for interactive FBA simulations within a pathway visualization. Escher-FBA allows users to set flux bounds, knock out reactions, change objective functions, upload metabolic models, and generate high-quality figures without downloading software or writing code. We provide detailed instructions on how to use Escher-FBA to replicate several FBA simulations that generate real scientific hypotheses.

Conclusions

We designed Escher-FBA to be as intuitive as possible so that users can quickly and easily understand the core concepts of FBA. The web application can be accessed at https://sbrg.github.io/escher-fba.

     

A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling

Cluster Computing - Workflow is composed of some interdependent tasks and workflow scheduling in the cloud environment that refers to sorting the workflow tasks on virtual machines on the cloud...     

Towards a rational approach to the optimization of flux in microbial biotransformations

Many historical attempts to increase the yield of biotechnological processes have been at best semi-empirical. However, given the availability of modern techniques of genetic and protein engineering, the question arises as to how one might rationally seek to choose the most suitable genes to clone and/or modify for this purpose. The metabolic control theory of Kaeser, Burns, Heinrich and Rapoport allows one to decide quantitatively which enzymatic steps are (most) rate-determining to the flux through desired pathways (and why). An extension of these principles allows one rationally to identify optimal strategies for the improvement of microbial processes.     

A comprehensive review of microbial electrochemical systems as a platform technology

Microbial electrochemical systems (MESs) use microorganisms to covert the chemical energy stored in biodegradable materials to direct electric current and chemicals. Compared to traditional treatment-focused, energy-intensive environmental technologies, this emerging technology offers a new and transformative solution for integrated waste treatment and energy and resource recovery, because it offers a flexible platform for both oxidation and reduction reaction oriented processes. All MESs share one common principle in the anode chamber, in which biodegradable substrates, such as waste materials, are oxidized and generate electrical current. In contrast, a great variety of applications have been developed by utilizing this in situ current, such as direct power generation (microbial fuel cells, MFCs), chemical production (microbial electrolysis cells, MECs; microbial electrosynthesis, MES), or water desalination (microbial desalination cells, MDCs). Different from previous reviews that either focus on one function or a specific application aspect, this article provides a comprehensive and quantitative review of all the different functions or system constructions with different acronyms developed so far from the MES platform and summarizes nearly 50 corresponding systems to date. It also provides discussions on the future development of this promising yet early-stage technology.     

Comparison and analysis of objective functions in flux balance analysis

Flux balance analysis (FBA) is currently one of the most important and used techniques for estimation of metabolic reaction rates (fluxes). This mathematical approach utilizes an optimization criterion in order to select a distribution of fluxes from the feasible space delimited by the metabolic reactions and some restrictions imposed over them, assuming that cellular metabolism is in steady state. Therefore, the obtained flux distribution depends on the specific objective function used. Multiple studies have been aimed to compare distinct objective functions at given conditions, in order to determine which of those functions produces values of fluxes closer to real data when used as objective in the FBA; in other words, what is the best objective function for modeling cell metabolism at a determined environmental condition. However, these comparative studies have been designed in very dissimilar ways, and in general, several factors that can change the ideal objective function in a cellular condition have not been adequately considered. Additionally, most of them have used only one dataset for representing one condition of cell growth, and different measuring techniques have been used. For these reasons, a rigorous study on the effect of factors such as the quantity of used data, the number and type of fluxes utilized as input data, and the selected classification of growth conditions, are required in order to obtain useful conclusions for these comparative studies, allowing limiting clearly the application range on any of those results. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:985–991, 2014     

Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks

Background  

In recent years, constrained optimization – usually referred to as flux balance analysis (FBA) – has become a widely applied method for the computation of stationary fluxes in large-scale metabolic networks. The striking advantage of FBA as compared to kinetic modeling is that it basically requires only knowledge of the stoichiometry of the network. On the other hand, results of FBA are to a large degree hypothetical because the method relies on plausible but hardly provable optimality principles that are thought to govern metabolic flux distributions.     

A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.     

Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems

The elucidation of organism-scale metabolic networks necessitates the development of integrative methods to analyze and interpret the systemic properties of cellular metabolism. A shift in emphasis from single metabolic reactions to systemically defined pathways is one consequence of such an integrative analysis of metabolic systems. The constraints of systemic stoichiometry, and limited thermodynamics have led to the definition of the flux space within the context of convex analysis. The flux space of the metabolic system, containing all allowable flux distributions, is constrained to a convex polyhedral cone in a high-dimensional space. From metabolic pathway analysis, the edges of the high-dimensional flux cone are vectors that correspond to systemically defined "extreme pathways" spanning the capabilities of the system. The addition of maximum flux capacities of individual metabolic reactions serves to further constrain the flux space and has led to the development of flux balance analysis using linear optimization to calculate optimal flux distributions. Here we provide the precise theoretical connections between pathway analysis and flux balance analysis allowing for their combined application to study integrated metabolic function. Shifts in metabolic behavior are calculated using linear optimization and are then interpreted using the extreme pathways to demonstrate the concept of pathway utilization. Changes to the reaction network, such as the removal of a reaction, can lead to the generation of suboptimal phenotypes that can be directly attributed to the loss of pathway function and capabilities. Optimal growth phenotypes are calculated as a function of environmental variables, such as the availability of substrate and oxygen, leading to the definition of phenotypic phase planes. It is illustrated how optimality properties of the computed flux distributions can be interpreted in terms of the extreme pathways. Together these developments are applied to an example network and to core metabolism of Escherichia coli demonstrating the connections between the extreme pathways, optimal flux distributions, and phenotypic phase planes. The consequences of changing environmental and internal conditions of the network are examined for growth on glucose and succinate in the face of a variety of gene deletions. The convergence of the calculation of optimal phenotypes through linear programming and the definition of extreme pathways establishes a different perspective for the understanding of how a defined metabolic network is best used under different environmental and internal conditions or, in other words, a pathway basis for the interpretation of the metabolic reaction norm.     

Capturing the essence of a metabolic network: A flux balance analysis approach

As genome-scale metabolic reconstructions emerge, tools to manage their size and complexity will be increasingly important. Flux balance analysis (FBA) is a constraint-based approach widely used to study the metabolic capabilities of cellular or subcellular systems. FBA problems are highly underdetermined and many different phenotypes can satisfy any set of constraints through which the metabolic system is represented.Two of the main concerns in FBA are exploring the space of solutions for a given metabolic network and finding a specific phenotype which is representative for a given task such as maximal growth rate. Here, we introduce a recursive algorithm suitable for overcoming both of these concerns. The method proposed is able to find the alternate optimal patterns of active reactions of an FBA problem and identify the minimal subnetwork able to perform a specific task as optimally as the whole.Our method represents an alternative to and an extension of other approaches conceived for exploring the space of solutions of an FBA problem. It may also be particularly helpful in defining a scaffold of reactions upon which to build up a dynamic model, when the important pathways of the system have not yet been well-defined.     

DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis

Background

Dynamic Flux Balance Analysis (DFBA) is a dynamic simulation framework for biochemical processes. DFBA can be performed using different approaches such as static optimization (SOA), dynamic optimization (DOA), and direct approaches (DA). Few existing simulators address the theoretical and practical challenges of nonunique exchange fluxes or infeasible linear programs (LPs). Both are common sources of failure and inefficiencies for these simulators.

Results

DFBAlab, a MATLAB-based simulator that uses the LP feasibility problem to obtain an extended system and lexicographic optimization to yield unique exchange fluxes, is presented. DFBAlab is able to simulate complex dynamic cultures with multiple species rapidly and reliably, including differential-algebraic equation (DAE) systems. In addition, DFBAlab’s running time scales linearly with the number of species models. Three examples are presented where the performance of COBRA, DyMMM and DFBAlab are compared.

Conclusions

Lexicographic optimization is used to determine unique exchange fluxes which are necessary for a well-defined dynamic system. DFBAlab does not fail during numerical integration due to infeasible LPs. The extended system obtained through the LP feasibility problem in DFBAlab provides a penalty function that can be used in optimization algorithms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0409-8) contains supplementary material, which is available to authorized users.     

Dynamic flux balance modeling of microbial co-cultures for efficient batch fermentation of glucose and xylose mixtures

Sequential uptake of pentose and hexose sugars that compose lignocellulosic biomass limits the ability of pure microbial cultures to efficiently produce value-added bioproducts. In this work, we used dynamic flux balance modeling to examine the capability of mixed cultures of substrate-selective microbes to improve the utilization of glucose/xylose mixtures and to convert these mixed substrates into products. Co-culture simulations of Escherichia coli strains ALS1008 and ZSC113, engineered for glucose and xylose only uptake respectively, indicated that improvements in batch substrate consumption observed in previous experimental studies resulted primarily from an increase in ZSC113 xylose uptake relative to wild-type E. coli. The E. coli strain ZSC113 engineered for the elimination of glucose uptake was computationally co-cultured with wild-type Saccharomyces cerevisiae, which can only metabolize glucose, to determine if the co-culture was capable of enhanced ethanol production compared to pure cultures of wild-type E. coli and the S. cerevisiae strain RWB218 engineered for combined glucose and xylose uptake. Under the simplifying assumption that both microbes grow optimally under common environmental conditions, optimization of the strain inoculum and the aerobic to anaerobic switching time produced an almost twofold increase in ethanol productivity over the pure cultures. To examine the effect of reduced strain growth rates at non-optimal pH and temperature values, a break even analysis was performed to determine possible reductions in individual strain substrate uptake rates that resulted in the same predicted ethanol productivity as the best pure culture.     

Photosensitivity in Smith-Lemli-Opitz syndrome: a flux balance analysis of altered metabolism

Ultraviolet A photosensitivity is a debilitating symptom associated with the metabolic disorder Smith-Lemli-Opitz syndrome (SLOS). SLOS is a manifestation of the deficiency of 7-dehydrocholesterol reductase, an enzyme involved in the cholesterol biosynthesis. As a result several abnormal intermediary compounds are formed among which Cholesta 5, 7, 9(11)-trien-3beta-ol is the most likely cause of photosensitivity. The effect of various drugs acting on cholesterol biosynthetic pathway on SLOS is not clear as clinical trials are not available for this rare disorder. A Flux Balance Analysis (FBA) has been carried out using the software CellNetAnalyzer or FluxAnalyzer to gain insight into the probable effects of various drugs acting on cholesterol biosynthetic pathway on photosensitivity in SLOS. The model consisted of 44 metabolites and 40 reactions. The formation flux of Cholesta 5, 7, 9(11)-trien-3beta-ol increased in SLOS and remained unchanged on simulation of the effect of miconazole and SR31747. However zaragozic acid can potentially reduce the flux through the entire pathway. FBA predicts zaragozic acid along with cholesterol supplementation as an effective treatment for photosensitivity in SLOS.     

Selection of Saccharomyces cerevisiae strains for use as a microbial feed additive

Saccharomyces cerevisiae ITCCF 2094, NCIM 3052, 1031, 1032, NCDC 42, 45, 47, 49 and 50 were screened for their tolerance to pH 2.0-7.0, various concentrations (0.00, 0.10, 0.25 0.50 and 1.0%) of a mixture of acetic, propionic and butyric acids (70:20:10), and bile salts (0.00, 0.30, 0.60 and 0.90%). Low pH (2.0-4.0) and addition of organic acids or bile salts in the medium inhibited the growth of all the strains tested, but the percentage of inhibition was variable in the different strains of yeast. Two of the strains showing maximum tolerance, 42 and 49, were further tested for in vitro dry matter degradability (IVDMD) using green berseem, wheat straw and oat hay as substrates. Saccharomyces cerevisiae 49 enhanced the IVDMD of berseem and wheat straw whereas S. cerevisiae 42 was ineffective. Based on the results of the present experiment, S. cerevisiae NCDC 49 can be considered as the best strain which might tolerate the adverse conditions in the gastrointestinal tract when used as a live microbial feed supplement in the diet of the animals.     

Identification of active constraints in dynamic flux balance analysis

This study deals with the calibration of dynamic metabolic flux models that are formulated as the maximization of an objective subject to constraints. Two approaches were applied for identifying the constraints from data. In the first approach a minimal active number of limiting constraints is found based on data that are assumed to be bounded within sets whereas, in the second approach, the limiting constraints are found based on parametric sensitivity analysis. The ability of these approaches to finding the active limiting constraints was verified through their application to two case studies: an in‐silico (simulated) data‐based study describing the growth of E. coli and an experimental data‐based study for Bordetella pertussis (B. pertussis). © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:26–36, 2017