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.     

A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis

Bioprocess and Biosystems Engineering - Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the...     

Culture-Independent Phylogenetic Analysis of the Microbial Community in Industrial Sugarcane Bagasse Feedstock Piles

Sugarcane bagasse is an important lignocellulosic by-product with potential for conversion to biofuels and chemicals in biorefinery. As a step towards an understanding of microbial diversity and the processes existing in bagasse collection sites, the microbial community in industrial bagasse feedstock piles was investigated. Molecular biodiversity analysis of 16S rDNA sequences revealed the presence of a complex bacterial community. A diverse group of mainly aerobic and facultative anaerobic bacteria was identified reflecting the aerobic and high temperature microenvironmental conditions under the pile surface. The major bacterial taxa present were identified as Firmicutes, Alpha- and Gammaproteobacteria, Acidobacteria, Bacteroidetes, and Actinobacteria. Analysis of the eukaryotic microbial assemblage based on an internal transcribed spacer revealed the predominance of diverse cellulolytic and hemicellulolytic ascomycota. A microbial interaction model is proposed, focusing on lignocellulose degradation and methane metabolism. The insights into the microbial community in this study provide a basis for efficient utilization of bagasse in lignocellulosic biomass-based industries.     

C. botulinum inactivation kinetics implemented in a computational model of a high‐pressure sterilization process

High‐pressure, high‐temperature (HPHT) processing is effective for microbial spore inactivation using mild preheating, followed by rapid volumetric compression heating and cooling on pressure release, enabling much shorter processing times than conventional thermal processing for many food products. A computational thermal fluid dynamic (CTFD) model has been developed to model all processing steps, including the vertical pressure vessel, an internal polymeric carrier, and food packages in an axis‐symmetric geometry. Heat transfer and fluid dynamic equations were coupled to four selected kinetic models for the inactivation of C. botulinum; the traditional first‐order kinetic model, the Weibull model, an nth‐order model, and a combined discrete log‐linear nth‐order model. The models were solved to compare the resulting microbial inactivation distributions. The initial temperature of the system was set to 90°C and pressure was selected at 600 MPa, holding for 220 s, with a target temperature of 121°C. A representation of the extent of microbial inactivation throughout all processing steps was obtained for each microbial model. Comparison of the models showed that the conventional thermal processing kinetics (not accounting for pressure) required shorter holding times to achieve a 12D reduction of C. botulinum spores than the other models. The temperature distribution inside the vessel resulted in a more uniform inactivation distribution when using a Weibull or an nth‐order kinetics model than when using log‐linear kinetics. The CTFD platform could illustrate the inactivation extent and uniformity provided by the microbial models. The platform is expected to be useful to evaluate models fitted into new C. botulinum inactivation data at varying conditions of pressure and temperature, as an aid for regulatory filing of the technology as well as in process and equipment design. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

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.     

Managing Spatial and Temporal Switchgrass Biomass Yield Variability

Biorefineries that plan to use switchgrass exclusively will encounter year-to-year variability in feedstock production. The economic success of the biorefinery will depend in part on the ability of the management team to strategically identify land for conversion from current use to the production of switchgrass enabling a flow of feedstock for the life of the biorefinery. The objective of this research is to determine the optimal quality, quantity, and location of land to lease while considering the spatial and temporal variability of switchgrass biomass yield. A calibrated biophysical simulation model was used to simulate switchgrass biomass yields for 50 years based on historical weather data from 1962 to 2011, for three land capability classes for each of 30 counties. Mathematical programming models were constructed and solved to determine the optimal leasing scheme for each of three strategies for a biorefinery that requires 2,000 Mg/day. As expected, a model based on the assumption that the average yield would be obtained in each year finds that production from land identified for leasing would be insufficient to fulfill the biorefinery’s needs in half of the years. In the absence of other sources of biomass, the feedstock shortage would require forced idling of the biorefinery for an average of 29.5 days during these years. Results of a strategy of leasing sufficient land to cover feedstock needs in the worst year from among 50 years for which data are available are compared to that of a strategy enabling year-to-year storage.     

Modeling and optimization of steroid transformation in a mixed culture

Summary Maximization of microbial two-step conversion of 9α-fluorohydrocortisone to Δ¹-dehydro-16α-hydroxy-9α-fluorohydrocortisone (triamcinolone) in a mixed culture of two microorganisms, Arthrobacter simplex and Streptomyces roseochromogenus, was attempted by a digital simulation, to optimize an operational parameter, pH, during batch cultivation. A mathematical model of the steroid transformation in the mixed culture was constructed with nine differential equations. The kinetic parameters other than the Michaelis-Menten constants in the mathematical model varied with pH of the culture medium. The mathematical model facilitated the simulation of the effect of pH on steroid conversion in the mixed culture. A modified Simplex method of direct search was applied to optimize the switching time and the combination of pH values in the batch culture with several step-changes of pH. Varied-pH processes showed much higher conversion yields than the constant pH process. A process of 3-phase pH change is expected to be advantageous in practical operation because of its lower susceptibility to pH perturbation, though the yield is almost the same as that of the 2-phase process.     

Stochastic functional data analysis: a diffusion model-based approach

This article presents a new modeling strategy in functional data analysis. We consider the problem of estimating an unknown smooth function given functional data with noise. The unknown function is treated as the realization of a stochastic process, which is incorporated into a diffusion model. The method of smoothing spline estimation is connected to a special case of this approach. The resulting models offer great flexibility to capture the dynamic features of functional data, and allow straightforward and meaningful interpretation. The likelihood of the models is derived with Euler approximation and data augmentation. A unified Bayesian inference method is carried out via a Markov chain Monte Carlo algorithm including a simulation smoother. The proposed models and methods are illustrated on some prostate-specific antigen data, where we also show how the models can be used for forecasting.     

Production of elemental sulfur from sulfide and nitrate-laden wastewaters by methanogenic culture via sulfide denitrifying removal process

The denitrifying sulfide removal (DSR) process is a biorefinery process that can produce colloidal S⁰ from sulfide and nitrate-laden wastewaters. At long reaction time the formed S⁰ produced is reduced back to sulfide by sulfate-reducing bacteria so the resulting in poor conversion rate of S⁰ is poor. The presence of optimal hydraulic retention time (HRT) for maximizing S⁰ conversion from DSR wastewaters was proposed by batch assays and then confirmed in continuous flow tests.     

An introduction to predictive microbiology and the development and use of probability models withClostridium botulinum

Summary Traditionally, food microbiologists have relied on empirical studies to assess the microbiological safety of a particular food. However, these studies are time-consuming and, because only one or two inhibitory factors are usually dealt with, they are often of limited value. Today, the food industry is constantly developing new products with new formulations and alternative packaging strategies, resulting in a wide diversity of factors to be studied. It is therefore advantageous to develop mathematical models describing microbial growth which may be used to predict how changes in formulations or storage conditions may affect microbial growth. A brief overview of the basic concepts and steps of modeling procedures will be presented, along with some of the difficulties encountered therein. The safety of foods with respect toClostridium botulinum depends on the probability (P) of growth or of toxigenesis, andP has been the dependent variable in several models. The development of these probability models will be discussed.Mention of brand or firm names does not constitute an endorsement by the US Department of Agriculture over others of a similar nature not mentioned.     

WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks

SUMMARY: WebCell is a web-based environment for managing quantitative and qualitative information on cellular networks and for interactively exploring their steady-state and dynamic behaviors in response to systemic perturbations. It is designed as a user-friendly web interface, allowing users to efficiently construct, visualize, analyze and store reaction network models, thereby facilitating kinetic modeling and in silico simulation of biological systems of interest. A collected model library is also available to provide comprehensive implications for cellular dynamics of the published models.     

Mining diversity of the natural biorefinery housed within Tipula abdominalis larvae for use in an industrial biorefinery for production of lignocellulosic ethanol

Abstract Although they are the largest taxonomic group of animals, relatively few insects have been examined for symbiotic relationships with micro-organisms. However, this is rapidly changing because of the potential for examination of the natural insect–microbe–lignocellulose interactions to provide insights for biofuel technology. Micro-organisms associated with lignocellulose-consuming insects often facilitate the digestion of the recalcitrant plant diet; therefore these microbial communities may be mined for novel lignocellulose-degrading microbes, or for robust and inexpensive biocatalysts necessary for economically feasible biofuel production from lignocellulose. These insect–microbe interactions are influenced by the ecosystem and specific lignocellulose diet, and appreciating the whole ecosystem–insect–microbiota–lignocellulose as a natural biorefinery provides a rich and diverse framework from which to design novel industrial processes. One such natural biorefinery, the Tipula abdominalis larvae in riparian ecosystems, is reviewed herein with applications for biochemical processes and overcoming challenges involved in conversion of lignocellulosic biomass to fuel ethanol. From the dense and diverse T. abdominalis larval hindgut microbial community, a cellulolytic bacterial isolate, 27C64, demonstrated enzymatic activity toward many model plant polymers and also produced a bacterial antibiotic. 27C64 was co-cultured with yeast in fermentation of pine to ethanol, which allowed for a 20% reduction of commercial enzyme. In this study, a micro-organism from a lignocellulose-consuming insect was successfully applied for improvement of biomass-to-biofuel technology.     

Culture-independent phylogenetic analysis of the microbial community in industrial sugarcane bagasse feedstock piles

Sugarcane bagasse is an important lignocellulosic by-product with potential for conversion to biofuels and chemicals in biorefinery. As a step towards an understanding of microbial diversity and the processes existing in bagasse collection sites, the microbial community in industrial bagasse feedstock piles was investigated. Molecular biodiversity analysis of 16S rDNA sequences revealed the presence of a complex bacterial community. A diverse group of mainly aerobic and facultative anaerobic bacteria was identified reflecting the aerobic and high temperature microenvironmental conditions under the pile surface. The major bacterial taxa present were identified as Firmicutes, Alpha- and Gammaproteobacteria, Acidobacteria, Bacteroidetes, and Actinobacteria. Analysis of the eukaryotic microbial assemblage based on an internal transcribed spacer revealed the predominance of diverse cellulolytic and hemicellulolytic ascomycota. A microbial interaction model is proposed, focusing on lignocellulose degradation and methane metabolism. The insights into the microbial community in this study provide a basis for efficient utilization of bagasse in lignocellulosic biomass-based industries.     

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric,thermodynamic and physiological constraints

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.     

Dynamic metagenome-scale metabolic modeling of a yogurt bacterial community

Genome-scale metabolic models and flux balance analysis (FBA) have been extensively used for modeling and designing bacterial fermentation. However, FBA-based metabolic models that accurately simulate the dynamics of coculture are still rare, especially for lactic acid bacteria used in yogurt fermentation. To investigate metabolic interactions in yogurt starter culture of Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus, this study built a dynamic metagenome-scale metabolic model which integrated constrained proteome allocation. The accuracy of the model was evaluated by comparing predicted bacterial growth, consumption of lactose and production of lactic acid with reference experimental data. The model was then used to predict the impact of different initial bacterial inoculation ratios on acidification. The dynamic simulation demonstrated the mutual dependence of S. thermophilus and L. d. bulgaricus during the yogurt fermentation process. As the first dynamic metabolic model of the yogurt bacterial community, it provided a foundation for the computer-aided process design and control of the production of fermented dairy products.     

CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis

Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.     

Modular modeling of cellular systems with ProMoT/Diva

MOTIVATION: Need for software to setup and analyze complex mathematical models for cellular systems in a modular way, that also integrates the experimental environment of the cells. RESULTS: A computer framework is described which allows the building of modularly structured models using an abstract, modular and general modeling methodology. With this methodology, reusable modeling entities are introduced which lead to the development of a modeling library within the modeling tool ProMot. The simulation environment Diva is used for numerical analysis and parameter identification of the models. The simulation environment provides a number of tools and algorithms to simulate and analyze complex biochemical networks. The described tools are the first steps towards an integrated computer-based modeling, simulation and visualization environment Availability: Available on request to the authors. The software itself is free for scientific purposes but requires commercial libraries. SUPPLEMENTARY INFORMATION: http://www.mpi-magdeburg.mpg.de/projects/promot     

Integrating Separation and Conversion—Conversion of Biorefinery Process Streams to Biobased Chemicals and Fuels

The concept of the integrated biorefinery is critical to developing a robust biorefining industry in the USA. Within this model, the biorefinery will produce fuel as a high-volume output addressing domestic energy needs and biobased chemical products (high-value organics) as an output providing necessary economic support for fuel production. This paper will overview recent developments within two aspects of the integrated biorefinery—the fractionation of biomass into individual process streams and the subsequent conversion of lignin into chemical products. Solvent-based separation of switchgrass, poplar, and mixed feedstocks is being developed as a biorefinery “front end” and will be described as a function of fractionation conditions. Control over the properties and structure of the individual biomass components (carbohydrates and lignin) can be observed by adjusting the fractionation process. Subsequent conversion of the lignin isolated from this fractionation leads to low molecular weight aromatics from selective chemical oxidation. Together, processes such as these provide examples of foundational technology that will contribute to a robust domestic biorefining industry.     

Exploring the gap between dynamic and constraint-based models of metabolism

Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of Escherichia coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identified by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.     

Critical applications of Mucor circinelloides within a biorefinery context

The establishment of an efficient and feasible biorefinery model depends on, among other factors, particularly the selection of the most appropriate microorganism. Mucor circinelloides is a dimorphic fungus species able to produce a wide variety of hydrolytic enzymes, lipids prone to biodiesel production, carotenoids, ethanol, and biomass with significant nutritional value. M. circinelloides also has been selected as a model species for genetic modification by being the first filamentous oleaginous species to have its genome fully characterized, as well as being a species characterized as a potential bioremediation agent. Considering the potential of replacing several nonrenewable feedstocks is widely dependent on fossil fuels, the exploitation of microbial processes and products is a desirable solution for promoting a green and sustainable future. Here, we introduce and thoroughly describe the recent and critical applications of this remarkable fungus within the context of developing a fungal-based biorefinery.