A robust methodology for kinetic model parameter estimation for biocatalytic reactions

Effective estimation of parameters in biocatalytic reaction kinetic expressions are very important when building process models to enable evaluation of process technology options and alternative biocatalysts. The kinetic models used to describe enzyme‐catalyzed reactions generally include several parameters, which are strongly correlated with each other. State‐of‐the‐art methodologies such as nonlinear regression (using progress curves) or graphical analysis (using initial rate data, for example, the Lineweaver‐Burke plot, Hanes plot or Dixon plot) often incorporate errors in the estimates and rarely lead to globally optimized parameter values. In this article, a robust methodology to estimate parameters for biocatalytic reaction kinetic expressions is proposed. The methodology determines the parameters in a systematic manner by exploiting the best features of several of the current approaches. The parameter estimation problem is decomposed into five hierarchical steps, where the solution of each of the steps becomes the input for the subsequent step to achieve the final model with the corresponding regressed parameters. The model is further used for validating its performance and determining the correlation of the parameters. The final model with the fitted parameters is able to describe both initial rate and dynamic experiments. Application of the methodology is illustrated with a case study using the ω‐transaminase catalyzed synthesis of 1‐phenylethylamine from acetophenone and 2‐propylamine. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Mechanistic modeling of biodiesel production using a liquid lipase formulation

In this article, a kinetic model for the enzymatic transesterification of rapeseed oil with methanol using Callera? Trans L (a liquid formulation of a modified Thermomyces lanuginosus lipase) was developed from first principles. We base the model formulation on a Ping‐Pong Bi‐Bi mechanism. Methanol inhibition, along with the interfacial and bulk concentrations of the enzyme was also modeled. The model was developed to describe the effect of different oil compositions, as well as different water, enzyme, and methanol concentrations, which are relevant conditions needed for process evaluation, with respect to the industrial production of biodiesel. The developed kinetic model, coupled with a mass balance of the system, was fitted to and validated on experimental results for the fed‐batch transesterification of rapeseed oil. The confidence intervals of the parameter estimates, along with the identifiability of the model parameters were presented. The predictive capability of the model was tested for a case using 0.5% (wt. Enzyme/wt. Oil), 0.5% (wt. Water /wt. Oil) and feeding 1.5 times the stoichiometric amount of methanol in total over 24 h. For this case, an optimized methanol feeding profile that constrains the amount of methanol in the reactor was computed and the predictions experimentally validated. Monte‐Carlo simulations were then used to characterize the effect of the parameter uncertainty on the model outputs, giving a biodiesel yield, based on the mass of oil, of 90.8 ± 0.55 mass %. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:1277–1290, 2014     

Reduced modeling and state observation of an activated sludge process

This article first proposes a reduction strategy of the activated sludge process model with alternated aeration. Initiated with the standard activated sludge model (ASM1), the reduction is based on some biochemical considerations followed by linear approximations of nonlinear terms. Two submodels are then obtained, one for the aerobic phase and one for the anoxic phase, using four state variables related to the organic substrate concentration, the ammonium and nitrate‐nitrite nitrogen, and the oxygen concentration. Then, a two‐step robust estimation strategy is used to estimate both the unmeasured state variables and the unknown inflow ammonium nitrogen concentration. Parameter uncertainty is considered in the dynamics and input matrices of the system. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Directed optimization of biocatalytic transglycosylation processes by the integration of genetic algorithms and fermentative approaches into a kinetic model

Many biocatalysts exhibit strict stereospecificity and regioselectivity. However, their thermodynamically controlled equilibria often limit yields in industrial production processes. Herein, we describe the synthesis of fructooligosaccharides from sucrose by various fructansucrases. We previously demonstrated that transfructosylation to diverse acceptors yields d-glucose and the fructose-containing product along with diverse by-products. To streamline this reaction, we developed a procedure that allows the enhanced transfructosylation of diverse acceptors by different fructansucrases. By diverting the released glucose from the reaction via metabolism by living cells we limited the back reaction and forced the consumption of sucrose. The basic conditions for the resulting fermentation process were optimized by a genetic algorithm and integrated into a kinetic model. This strategy allows the prediction of optimal reaction parameters for the production of desired target compounds.     

Application of iterative robust model‐based optimal experimental design for the calibration of biocatalytic models

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model‐based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω‐transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278–1293, 2017     

Modeling with a view to target identification in metabolic engineering: A critical evaluation of the available tools

The state of the art tools for modeling metabolism, typically used in the domain of metabolic engineering, were reviewed. The tools considered are stoichiometric network analysis (elementary modes and extreme pathways), stoichiometric modeling (metabolic flux analysis, flux balance analysis, and carbon modeling), mechanistic and approximative modeling, cybernetic modeling, and multivariate statistics. In the context of metabolic engineering, one should be aware that the usefulness of these tools to optimize microbial metabolism for overproducing a target compound depends predominantly on the characteristic properties of that compound. Because of their shortcomings not all tools are suitable for every kind of optimization; issues like the dependence of the target compound's synthesis on severe (redox) constraints, the characteristics of its formation pathway, and the achievable/desired flux towards the target compound should play a role when choosing the optimization strategy. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Application of wavelet-based tools to study the dynamics of biological processes

The article makes use of three different examples (sensory information processing in the rat trigeminal complex, intracellular interaction in snail neurons and multimodal dynamics in nephron autoregulation) to demonstrate how modern approaches to time-series analysis based on the wavelet-transform can provide information about the underlying complex biological processes.     

Application of model discriminating experimental design for modeling and development of a fermentative fed-batch L-valine production process

A model discriminating experimental design approach for fed-batch processes has been developed and applied to the fermentative production of L-valine by a genetically modified Corynebacterium glutamicum strain possessing multiple auxotrophies as an example. Being faced with the typical situation of uncertain model information based on preliminary experiments, model discriminating design was successfully applied to improve discrimination between five competing models. Within the same modeling and experimental design framework, also the planning of an optimized production process with respect to the total volumetric productivity is shown. Simulation results were experimentally affirmed, yielding an increased total volumetric productivity of 6.2 mM L-valine per hour. However, also so far unknown metabolic mechanisms were observed in the optimized process, underlining the importance of process optimization during modeling to avoid problems of extreme extrapolation of model predictions during the final process optimization.     

Individual-based modeling of phytoplankton: evaluating approaches for applying the cell quota model

Present phytoplankton models typically use a population-level (lumped) modeling (PLM) approach that assumes average properties of a population within a control volume. For modern biogeochemical models that formulate growth as a nonlinear function of the internal nutrient (e.g. Droop kinetics), this averaging assumption can introduce a significant error. Individual-based (agent-based) modeling (IBM) does not make the assumption of average properties and therefore constitutes a promising alternative for biogeochemical modeling. This paper explores the hypothesis that the cell quota (Droop) model, which predicts the population-average specific growth or cell division rate, based on the population-average nutrient cell quota, can be applied to individual algal cells and produce the same population-level results. Three models that translate the growth rate calculated using the cell quota model into discrete cell division events are evaluated, including a stochastic model based on the probability of cell division, a deterministic model based on the maturation velocity and fraction of the cell cycle completed (maturity fraction), and a deterministic model based on biomass (carbon) growth and cell size. The division models are integrated into an IBM framework (iAlgae), which combines a lumped system representation of a nutrient with an individual representation of algae. The IBM models are evaluated against a conventional PLM (because that is the traditional approach) and data from a number of steady and unsteady continuous (chemostat) and batch culture laboratory experiments. The stochastic IBM model fails the steady chemostat culture test, because it produces excessive numerical randomness. The deterministic cell cycle IBM model fails the batch culture test, because it has an abrupt drop in cell quota at division, which allows the cell quota to fall below the subsistence quota. The deterministic cell size IBM model reproduces the data and PLM results for all experiments and the model parameters (e.g. maximum specific growth rate, subsistence quota) are the same as those for the PLM. In addition, the model-predicted cell age, size (carbon) and volume distributions are consistent with those derived analytically and compare well to observations. The paper discusses and illustrates scenarios where intra-population variability in natural systems leads to differences between the IBM and PLM models.     

肿瘤相关生物学通路的发现和建模

肿瘤是一种严重影响人类健康和生命的复杂疾病。某些生物学通路在肿瘤的发生、发展和转移的过程中发挥了关键作用,如何发现和研究肿瘤相关通路是人们面临的一大挑战。随着以基因芯片数据为代表的海量实验数据的产出,很多研究小组提出了一系列算法和模型通过整合和分析实验数据,鉴定和模拟肿瘤相关的生物学通路,发现了很多重要的生物学结论。文章对这些研究工作进行了综述,给出了一些常用的算法、软件和数据库资源,并讨论了该领域存在的问题和以后的发展方向。     

Practical tools for molecular modeling of complex carbohydrates and their interactions with proteins

Computer modeling has become a valuable component of studies of carbohydrate three-dimensional structures and their relationship to function and properties. In this paper we examine the methods required for conformational modeling of carbohydrates, and we present a series of tools that have been developed to this end. These tools can be integrated into three-dimensional real-time molecular modeling software. A data base of pre-optimized carbohydrate fragments has been established to be used further in the construction of much more complex molecules. In addition we describe some possible uses of a data base of three dimensional structures of the disaccharide fragments present in the glycan moiety ofN-glycoprotein. A molecular mechanical force field appropriate for the conformational analysis of oligosaccharides has been derived by the addition of new parameters to the Tripos force field and is compatible with protein simulations. The new parametrization has been assessed in three stages of increasing complexity: computations of potential energy surfaces, conformational refinement of relevant oligosaccharides, modeling at the atomic level of a protein/carbohydrate complex.     

A link between neuroscience and informatics: large-scale modeling of memory processes

Utilizing advances in functional neuroimaging and computational neural modeling, neuroscientists have increasingly sought to investigate how distributed networks, composed of functionally defined subregions, combine to produce cognition. Large-scale, biologically realistic neural models, which integrate data from cellular, regional, whole brain, and behavioral sources, delineate specific hypotheses about how these interacting neural populations might carry out high-level cognitive tasks. In this review, we discuss neuroimaging, neural modeling, and the utility of large-scale biologically realistic models using modeling of short-term memory as an example. We present a sketch of the data regarding the neural basis of short-term memory from non-human electrophysiological, computational and neuroimaging perspectives, highlighting the multiple interacting brain regions believed to be involved. Through a review of several efforts, including our own, to combine neural modeling and neuroimaging data, we argue that large scale neural models provide specific advantages in understanding the distributed networks underlying cognition and behavior.     

A review of advanced small-scale parallel bioreactor technology for accelerated process development: current state and future need

The pharmaceutical and biotech industries face continued pressure to reduce development costs and accelerate process development. This challenge occurs alongside the need for increased upstream experimentation to support quality by design initiatives and the pursuit of predictive models from systems biology. A small scale system enabling multiple reactions in parallel (n ≥ 20), with automated sampling and integrated to purification, would provide significant improvement (four to fivefold) to development timelines. State of the art attempts to pursue high throughput process development include shake flasks, microfluidic reactors, microtiter plates and small-scale stirred reactors. The limitations of these systems are compared to desired criteria to mimic large scale commercial processes. The comparison shows that significant technological improvement is still required to provide automated solutions that can speed upstream process development.     

An engineering and economic evaluation of wet and dry pre-fractionation processes for dry-grind ethanol facilities

An engineering-economic model was developed to compare the profitability of the wet fractionation process, a generic dry fractionation process, and the conventional dry grind process. Under market conditions as of January 2011, only fractionation processes generated a positive cash flow. Reduced unit manufacturing costs and increased ethanol production capacity were two major contributions. Corn and ethanol price sensitivity analysis showed that the wet fractionation process always outperformed a generic dry fractionation process at any scenario considered in this research. A generic dry fractionation process would provide better economic performance than the conventional dry grind process if corn price was low and ethanol price was high. All three processes would perform more resiliently if the DDGS price was determined by its composition.     

Concepts of sulfur,carbon, and nitrogen transformations in soil: evaluation by simulation modeling

The dynamics of sulfur immobilization and mineralization in soil were simulated to test hypotheses about their regulation by the availability of carbon and nitrogen. The concept of chemical bond classes was incorporated into the model to account for variation in composition of carbon, nitrogen, and sulfur compounds. Microbial biomass was differentiated into bacteria and fungi, and the element ratios of both groups were assumed to vary. Organic residues were divided between dead microbes plus microbial products, and the more labile fraction of stabilized soil organic matter. Concepts and hypotheses in the model were tested by applying it to data on microbial biomass, sulfate, nitrate, and CO₂ evolution obtained in laboratory incubations of two soils amended with sulfate and cellulose. An important mechanism of regulation tested in the model was the stimulation of sulfohydrolase enzyme production depending on sulfur stress in microbial biomass. The hypothesis that excess sulfate is stored as ester sulfate was supported by model dynamics.     

Using L-systems for modeling source-sink interactions, architecture and physiology of growing trees: the L-PEACH model

Functional-structural plant models simulate the development of plant structure, taking into account plant physiology and environmental factors. The L-PEACH model is based on the development of peach trees. It demonstrates the usefulness of L-systems in constructing functional-structural models. L-PEACH uses L-systems both to simulate the development of tree structure and to solve differential equations for carbohydrate flow and allocation. New L-system-based algorithms are devised for simulating the behavior of dynamically changing structures made of hundreds of interacting, time-varying, nonlinear components. L-PEACH incorporates a carbon-allocation model driven by source-sink interactions between tree components. Storage and mobilization of carbohydrates during the annual life cycle of a tree are taken into account. Carbohydrate production in the leaves is simulated based on the availability of water and light. Apices, internodes, leaves and fruit grow according to the resulting local carbohydrate supply. L-PEACH outputs an animated three-dimensional visual representation of the growing tree and user-specified statistics that characterize selected stages of plant development. The model is applied to simulate a tree's response to fruit thinning and changes in water stress. L-PEACH may be used to assist in horticultural decision-making processes after being calibrated to specific trees.