Optimization of bioreactor using metabolic control analysis approach

A new methodology based on a metabolic control analysis (MCA) approach is developed for the optimization of continuous cascade bioreactor system. A general framework for representation of a cascade bioreactor system consisting of a large number of reactors as a single network is proposed. The kinetic and transport processes occurring in the system are represented as a reaction network with appropriate stoichiometry. Such representation of the bioreactor systems makes it amenable to the direct application of the MCA approach. The process sensitivity information is extracted using MCA methodology in the form of flux and concentration control coefficients. The process sensitivity information is shown to be a useful guide for determining the choice of decision variables for the purpose of optimization. A generalized problem of optimization of the bioreactor is formulated in which the decision variables are the operating conditions and kinetic parameters. The gradient of the objective function to be maximized with respect to all decision variables is obtained in the form of response coefficients. This gradient information can be used in any gradient-based optimization algorithm. The efficiency of the proposed technique is demonstrated with two examples taken from literature: biotransformation of crotonobetaine and alcohol fermentation in cascade bioreactor system.     

Strategies for representing metabolic pathways within biochemical systems theory: reversible pathways

The search for systematic methods to deal with the integrated behavior of complex biochemical systems has over the past two decades led to the proposal of several theories of biochemical systems. Among the most promising is biochemical systems theory (BST). Recent comparisons of this theory with several others that have recently been proposed have demonstrated that all are variants of BST and share a common underlying formalism. Hence, the different variants can be precisely related and ranked according to their completeness and operational utility. The original and most fruitful variant within BST is based on a particular representation, called an S-system (for synergistic and saturable systems), that exhibits many advantages not found among alternative representations. Even within the preferred S-system representation there are options, depending on the method of aggregating fluxes, that become especially apparent when one considers reversible pathways. In this paper we focus on the paradigm situation and clearly distinguish the two most common strategies for generating an S-system representation. The first is called the "reversible" strategy because it involves aggregating incoming fluxes separately from outgoing fluxes for each metabolite to define a net flux that can be positive, negative, or zero. The second is the "irreversible" strategy, which involves aggregating forward and reverse fluxes through each reaction to define a net flux that is always positive. This second strategy has been used almost exclusively in all variants of BST. The principal results of detailed analyses are the following: (1) All S-system representations predict the same changes in dependent concentrations for a given change in an independent concentration. (2) The reversible strategy is superior to the irreversible on the basis of several criteria, including accuracy in predicting steady-state flux, accuracy in predicting transient responses, and robustness of representation. (3) Only the reversible strategy yields a representation that is able to capture the characteristic feature of amphibolic pathways, namely, the reversal of nets flux under physiological conditions. Finally, the results document the wide range of variation over which the S-system representation can accurately predict the behavior of intact biochemical systems and confirm similar results of earlier studies [Voit and Savageau, Biochemistry 26: 6869-6880 (1987)].     

An efficient method for calculation of dynamic logarithmic gains in biochemical systems theory

Biochemical systems theory (BST) characterizes a given biochemical system based on the logarithmic gains, rate-constant sensitivities and kinetic-order sensitivities defined at a steady state. This paper describes an efficient method for calculation of the time courses of logarithmic gains, i.e. dynamic logarithmic gains L(Xi, Xj; t), which expresses the percentage change in the value of a dependent variable Xi at a time t in response to an infinitesimal percentage change in the value of an independent variable Xj at t=0. In this method, one first recasts the ordinary differential equations for the dependent variables into an exact canonical nonlinear representation (GMA system) through appropriate transformations of variables. Owing to the structured mathematical form of this representation, the recast system can be fully described by a set of numeric parameters, and the differential equations for the dynamic logarithmic gains can be set up automatically without resource to computer algebra. A simple general-purpose computer program can thus be written that requires only the relevant numeric parameters as input to calculate the time courses of the variables and of the dynamic logarithmic gains for both concentrations and fluxes. Unlike other methods, the proposed method does not require to derive any expression for the partial differentiation of flux expressions with respect to each independent variable. The proposed method has been applied to two kinds of reaction models to elucidate its usefulness.     

Computation and analysis of time-dependent sensitivities in Generalized Mass Action systems

Understanding biochemical system dynamics is becoming increasingly important for insights into the functioning of organisms and for biotechnological manipulations, and additional techniques and methods are needed to facilitate investigations of dynamical properties of systems. Extensions to the method of Ingalls and Sauro, addressing time-dependent sensitivity analysis, provide a new tool for executing such investigations. We present here the results of sample analyses using time-dependent sensitivities for three model systems taken from the literature, namely an anaerobic fermentation pathway in yeast, a negative feedback oscillator modeling cell-cycle phenomena, and the Mitogen Activated Protein (MAP) kinase cascade. The power of time-dependent sensitivities is particularly evident in the case of the MAPK cascade. In this example it is possible to identify the emergence of a concentration of MAPKK that provides the best response with respect to rapid and efficient activation of the cascade, while over- and under-expression of MAPKK relative to this concentration have qualitatively different effects on the transient response of the cascade. Also of interest is the quite general observation that phase-plane representations of sensitivities in oscillating systems provide insights into the manner with which perturbations in the envelope of the oscillation result from small changes in initial concentrations of components of the oscillator. In addition to these applied analyses, we present an algorithm for the efficient computation of time-dependent sensitivities for Generalized Mass Action (GMA) systems, the most general of the canonical system representations of Biochemical Systems Theory (BST). The algorithm is shown to be comparable to, or better than, other methods of solution, as exemplified with three biochemical systems taken from the literature.     

Multicriteria optimization of biochemical systems by linear programming: application to production of ethanol by Saccharomyces cerevisiae

In this study we present a method for simultaneous optimization of several metabolic responses of biochemical pathways. The method, based on the use of the power law formalism to obtain a linear system in logarithmic coordinates, is applied to ethanol production by Saccharomyces cerevisiae. Starting from an experimentally based kinetic model, we translated it to its power law equivalent. With this new model representation, we then applied the multiobjective optimization method. Our intent was to maximize ethanol production and minimize each of the internal metabolite concentrations. To ensure cell viability, all optimizations were carried out under imposed constraints. The different solutions obtained, which correspond to alternative patterns of enzyme overexpression, were implemented in the original model. We discovered few discrepancies between the S-system-optimized steady state and the corresponding optimized state in the original kinetic model, thus demonstrating the suitability of the S-system representation as the basis for the optimization procedure. In all optimized solutions, the ATP level reached its maximum and any increase in its activity positively affected the optimization process. This work illustrates that in any optimization study no single criteria is of general application being the multiobjective and constrained task the proper way to address it. It is concluded that the proposed multiobjective method can serve to carry out, in a single study, the general pattern of behavior of a given metabolic system with regard to its control and optimization.     

Yield optimization of regulated metabolic systems using deterministic branch-and-reduce methods

This paper addresses product yield optimization in microorganisms grown in continuous culture. Traditional optimization strategies of random mutagenesis and selection will eventually have limited efficacy, thus requiring more focused strategies. The best candidates for such strategies appear to be mathematical models that capture the essence of metabolic systems and permit optimization with computational methods. In the past, models used for this purpose have been stoichiometric, kinetic in the form of S-systems, or ad hoc. This work presents a deterministic approach based on generalized mass action (GMA) systems. These systems are interesting in that they allow direct merging of stoichiometric and S-system models. Two illustrations are considered. In the first case, the fermentation pathway in Saccharomyces cerevisiae is optimized for ethanol production under steady-state conditions. The model of this pathway is relatively small, with five states and eight rate constants. The second example addresses the maximization of citric acid in the mold Aspergillus niger. For the optimization of this larger pathway system with 30 states and 60 reactions, a Mixed Integer Nonlinear Programming (MINLP) is proposed. It is shown that efficient MINLP algorithms, based on convexification, branch-and-reduce methods, and binary variable selection, are essential for solving these difficult optimization problems.     

采用最小二乘支持向量机的青霉素发酵过程建模研究

生化过程通常是严重非线性和时变的复杂动态系统,而且重要过程参数缺少在线测量仪表,对其建立机理模型往往非常耗时和困难。采用最小二乘支持向量机(LS_SVM)并以Pensim仿真平台为例对青霉素发酵这一典型生化过程进行建模研究。给出了LS_SVM参数的调整策略和分析结果,建立了青霉素产物浓度、菌体浓度和底物浓度等重要过程变量的在线预报模型。仿真结果表明用LS_SVM建立的在线预报模型拟合误差小,推广性能好,可以作为发酵过程的进一步控制和优化的参考依据。     

A novel cost function to estimate parameters of oscillatory biochemical systems

Oscillatory pathways are among the most important classes of biochemical systems with examples ranging from circadian rhythms and cell cycle maintenance. Mathematical modeling of these highly interconnected biochemical networks is needed to meet numerous objectives such as investigating, predicting and controlling the dynamics of these systems. Identifying the kinetic rate parameters is essential for fully modeling these and other biological processes. These kinetic parameters, however, are not usually available from measurements and most of them have to be estimated by parameter fitting techniques. One of the issues with estimating kinetic parameters in oscillatory systems is the irregularities in the least square (LS) cost function surface used to estimate these parameters, which is caused by the periodicity of the measurements. These irregularities result in numerous local minima, which limit the performance of even some of the most robust global optimization algorithms. We proposed a parameter estimation framework to address these issues that integrates temporal information with periodic information embedded in the measurements used to estimate these parameters. This periodic information is used to build a proposed cost function with better surface properties leading to fewer local minima and better performance of global optimization algorithms. We verified for three oscillatory biochemical systems that our proposed cost function results in an increased ability to estimate accurate kinetic parameters as compared to the traditional LS cost function. We combine this cost function with an improved noise removal approach that leverages periodic characteristics embedded in the measurements to effectively reduce noise. The results provide strong evidence on the efficacy of this noise removal approach over the previous commonly used wavelet hard-thresholding noise removal methods. This proposed optimization framework results in more accurate kinetic parameters that will eventually lead to biochemical models that are more precise, predictable, and controllable.     

Serum markers change for intraocular metastasis in renal cell carcinoma

Objective: Renal cell carcinoma is prone to early metastasis. In general, intraocular metastasis (IOM) is not common. In the present study, we studied the relationship between different biochemical indicators and the occurrence of IOM in renal cancer patients, and identified the potential risk factors.Methods: A retrospective analysis of the clinical data of 214 patients with renal cell carcinoma from October 2001 to August 2016 was carried out. The difference and correlation of various indicators between the two groups with or without IOM was analyzed, and binary logistic regression analysis was used to explore the risk factors of IOM in renal cancer patients. The diagnostic value of each independent related factor was calculated according to the receiver operating curve (ROC).Results: The level of neuron-specific enolase (NSE) in renal cell carcinoma patients with IOM was significantly higher than that in patients without IOM (P<0.05). There was no significant difference in alkaline phosphatase (ALP), hemoglobin (Hb), serum calcium concentration, α fetoprotein (AFP), carcinoembryonic antigen (CEA), CA-125 etc. between IOM group and non-IOM (NIOM) group (P>0.05). Binary logistic regression analysis showed that NSE was an independent risk factor for IOM in renal cell carcinoma patients (P<0.05). ROC curve shows that the factor has high accuracy in predicting IOM, and the area under the curve (AUC) is 0.774. The cut-off value of NSE was 49.5 U/l, the sensitivity was 72.2% and the specificity was 80.1%.Conclusion: NSE concentration is a risk factor for IOM in patients with renal cell cancer. If the concentration of NSE in the patient’s body is ≥49.5 U/l, disease monitoring and eye scans should be strengthened.     

A rapid immunogold-silver staining for detection of bromodeoxyuridine in large numbers of plastic sections, using microwave irradiation

Summary A rapid and convenient method for the large scale, immunogold-silver staining (IGSS) of bromodeoxyuridine (BrdU) incorporated by S phase cells, by means of a monoclonal antibody (anti-BrdU) is described. Nineteen slides at a time can be incubated with the antibodies and the protein A-gold (PAG) in staining jars. The antibody and protein A-gold solutions could be used at least five times to incubate new batches of slides. The incubation times with these solutions were shortened by means of microwave irradiation. In this way 200 slides carrying at least 800 sections could be easily processed under the same conditions in one day, using 1.25ml neat antibody solutions of anti-BrdU and rabbit anti-mouse.For light microscopy bothpplastic embedding systems: methylmethacrylate (MMA) and glycolmethacrylate (GMA) can be stained with this technique. The MMA sections, of which the plastic has to be removed before the IGSS, has the advantage of a stronger labelling intensity. The GMA plastic, which contains a cross-linking, agent cannot be removed and consequently for GMA sections it is necessary to incubate the sections with a proteolytic enzyme (trypsin) before the IGSS, to reexpose the antigenic binding sides. However, the GMA sections can be allowed to air dry during the IGSS without negative effects on the morphology. This makes it possible to perform the antibody and the PAG-incubating steps on one day and to finish the IGSS the next day. In this way twice as many GMA slides can be incubated with the same antibody and PAG solutions than with MMA slides.In both plastic embedding systems the intensity of the BrdU labelling was found to be stronger in Carnoy's than in Bouin's fixed sections.     

Sensitivity-based adaptive mesh refinement collocation method for dynamic optimization of chemical and biochemical processes

Collocation on finite element (CFE) is an effective simultaneous method of dynamic optimization to increase the profitability or productivity of industrial process. The approach needs to select an optimal mesh of time interval to balance the computational cost with desired solution. A new CFE approach with non-uniform refinement procedure based on the sensitivity analysis for dynamic optimization problems is, therefore, proposed, where a subinterval is further refined if the obtained control parameters have significant effect on the performance index. To improve the efficiency, the sensitivities of state parameters with respect to control parameters are derived from the solution of the discretized dynamic system. The proposed method is illustrated by testing two classic dynamic optimization problems from chemical and biochemical engineering. The detailed comparisons among the proposed method, the CFE with uniform mesh, and other reported methods are also carried out. The research results reveal the effectiveness of the proposed approach.

     

Growth and ligninolytic system production dynamics of the Phanerochaete chrysosporium fungus A modelling and optimization approach

The well-documented ability to degrade lignin and a variety of complex chemicals showed by the white-rot fungus Phanerochaete chrysosporium has made it the subject of many studies in areas of environmental concern, including pulp bioleaching and bioremediation technologies. However, until now, most of the work in this field has been focused on the ligninolytic sub-system but, due to the great complexity of the involved processes, less progress has been made in understanding the biochemical regulatory structure that could explain growth dynamics, the substrate utilization and the ligninolytic system production itself. In this work we want to tackle this problem from the perspectives and approaches of systems biology, which have been shown to be effective in the case of complex systems. We will use a top-down approach to the construction of this model aiming to identify the cellular sub-systems that play a major role in the whole process. We have investigated growth dynamics, substrate consumption and lignin peroxidase production of the P. chrysosporium wild type under a set of definite culture conditions. Based on data gathered from different authors and in our own experimental determinations, we built a model using a GMA power-law representation, which was used as platform to make predictive simulations. Thereby, we could assess the consistency of some current assumptions about the regulatory structure of the overall process. The model parameters were estimated from a time series experimental measurements by means of an algorithm previously adapted and optimized for power-law models. The model was subsequently checked for quality by comparing its predictions with the experimental behavior observed in new, different experimental settings and through perturbation analysis aimed to test the robustness of the model. Hence, the model showed to be able to predict the dynamics of two critical variables such as biomass and lignin peroxidase activity when in conditions of nutrient deprivation and after pulses of veratryl alcohol. Moreover, it successfully predicts the evolution of the variables during both, the active growth phase and after the deprivation shock. The close agreement between the predicted and observed behavior and the advanced understanding of its kinetic structure and regulatory features provides the necessary background for the design of a biotechnological set-up designed for the continuous production of the ligninolityc system and its optimization.     

An approach for Condition Based Maintenance strategy optimization oriented to multi-source data

Condition Based Maintenance (CBM) scheduling for power equipment is based on some types of data in power system, such as the health condition of components, the load level of the substation and Life Cycle Cost (LCC), etc. Due to the lack of necessary data, traditional CBM strategy optimization models are usually established without enough data, and the optimization results are usually difficult to obtain the enough accuracy. With the development of Chinese power industry, the data volume in power system is increasing dramatically in recent years, and the power industry is undoubtedly entering the era of Big Data. To handle and store these increasing data, more and more data management systems have been developed for different application. For example, Energy Management System, Production Management System and Management Information System have been widely applied. The multi-source data in these systems gives an opportunity to improve the accuracy and rationality for CBM optimization results. Thus, it is of great significance to make full use of these data in maintenance strategy optimization process. This paper presents a new approach to optimize the CBM strategy for components in the substation. After analyzing the interconnection relations of different types of components, the substation is divided into different maintenance units, the components in the same unit can be maintained together. To quantitatively evaluate the reliability of the components before and after repaired, two failure rate calculation models based on Health Index (HI) and age reduction factor are established respectively, and all the alternative maintenance strategies for the abnormal components are proposed based on the location and severity of the faults or defects of the components. According to the theory of LCC, the CBM optimization model is established when determines the minimum total cost as the optimization goal during the maintenance period. The total cost consists of the reparation cost, the interruption cost and the maintenance cost. Finally, an application example in a 220 kV substation is proposed, and these multi-source data are fully applied in the optimization process. The calculation results indicate that these types of data have benefit to improve the accuracy and rationality of the optimization results, and the strategy after optimized can obviously improve both the power supply reliability and the economy of the substation.     

Smooth bistable S-systems

S-systems have been used as models of biochemical systems for over 30 years. One of their hallmarks is that, although they are highly non-linear, their steady states are characterised by linear equations. This allows streamlined analyses of stability, sensitivities and gains as well as objective, mathematically controlled comparisons of similar model designs. Regular S-systems have a unique steady state at which none of the system variables is zero. This makes it difficult to represent switching phenomena, as they occur, for instance, in the expression of genes, cell cycle phenomena and signal transduction. Previously, two strategies were proposed to account for switches. One was based on a technique called recasting, which permits the modelling of any differentiable non-linearities, including bistability, but typically does not allow steady-state analyses based on linear equations. The second strategy formulated the switching system in a piece-wise fashion, where each piece consisted of a regular S-system. A representation gleaned from a simplified form of recasting is proposed and it is possible to divide the characterisation of the steady states into two phases, the first of which is linear, whereas the other is non-linear, but easy to execute. The article discusses a representative pathway with two stable states and one unstable state. The pathway model exhibits strong separation between the stable states as well as hysteresis.     

A comparison of variant theories of intact biochemical systems. II. Flux-oriented and metabolic control theories

In the past two decades, several theories, all ultimately based upon the same power-law formalism, have been proposed to relate the behavior of intact biochemical systems to the properties of their underlying determinants. Confusion concerning the relatedness of these alternatives has become acute because the implications of these theories have never been compared. In the preceding paper we characterized a specific system involving enzyme-enzyme interactions for reference in comparing alternative theories. We also analyzed the reference system by using an explicit variant that involves the S-system representation within biochemical systems theory (BST). We now analyze the same reference system according to two other variants within BST. First, we carry out the analysis by using an explicit variant that involves the generalized mass action representation, which includes the flux-oriented theory of Crabtree and Newsholme as a special case. Second, we carry out the analysis by using an implicit variant that involves the generalized mass action representation, which includes the metabolic control theory of Kacser and his colleagues as a special case. The explicit variants are found to provide a more complete characterization of the reference system than the implicit variants. Within each of these variant classes, the S-system representation is shown to be more mathematically tractable and accurate than the generalized mass action representation. The results allow one to make clear distinctions among the variant theories.     

Global mapping analysis: stochastic approximation for multidimensional scaling.

In this paper, we propose global mapping analysis (GMA) as a new method to solve multidimensional scaling (MDS). By GMA, MDS is done by an online learning rule based on stochastic approximation. GMA need not directly calculate the disparity matrix for carrying out MDS, as Oja's PCA network do not calculate the correlation matrix. So, GMA is expected to be useful for multivariate data analysis on a large scale. Actually, it was verified by numerical experiments based on artificial data that GMA can work well even if the number of the attribute N is quite large (N=10,000.)     

Label-free electrochemical immunosensors based on surface-initiated atom radical polymerization

A novel label-free immunosensing strategy for sensitive detection of tumor necrosis factor-alpha antigen (TNF-α) via surface-initiated atom transfer radical polymerization (SI-ATRP) was proposed. In this strategy, the Au electrode was first modified by consecutive SI-ATRP of ferrocenylmethyl methacrylate (FMMA) and glycidyl methacrylate (GMA), and TNF-α antibody was coupled to the copolymer segment of GMA (PGMA) by aqueous carbodiimide coupling reaction. Subsequently, the target TNF-α antigen was captured onto the Au electrode surface through immunoreaction. The whole process was confirmed by scanning electron microscopy (SEM) and surface plasmon resonance (SPR) measurements. With introduction of redox polymer segment of FMMA (PFMMA) as electron-transfer mediator, the antigen-coupled Au electrode exhibited well electrochemical behavior, as revealed by cyclic voltammetry measurement. This provided a sensing platform for sensitive detection of TNF-α with a low detection limit of 3.9pgmL(-1). Furthermore, the "living" characteristics of the ATRP process can not only be readily controlled but also allow further surface functionalization of the electrodes, thus the proposed method presented a way for label-free and flexible detection of biomolecules.     

A knowledge-based architecture for protein sequence analysis and structure prediction

Methods for analyzing the amino-acid sequence of a protein for the purposes of predicting its three-dimensional structure were systematically analyzed using knowledge engineering techniques. The resulting entities (data) and relations (processing methods and constraints) have been represented within a generalized dependency network consisting of 29 nodes and over 100 links. It is argued that such a representation meets the requirements of knowledge-based systems in molecular biology. This network is used as the architecture for a prototype knowledge-based system that simulates logically the processes used in protein structure prediction. Although developed specifically for applications in protein structure prediction, the network architecture provides a strategy for tackling the general problem of orchestrating and integrating the diverse sources of knowledge that are characteristic of many areas of science.