Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model - Application to amino acid depletion in CHO cell culture.

Mammalian cell lines are characterized by a complex and flexible metabolism. A single model that could describe the variations in metabolic behavior triggered by variations in the culture conditions would be a precious tool in bioprocess development. In this paper, we introduce an approach to generate a poly-pathway model and use it to simulate diverse metabolic states triggered in response to removal, reduction or doubling of amino acids in the culture medium of an antibody-producing CHO cell line. Macro-reactions were obtained from a metabolic network via elementary flux mode enumeration and the fluxes were modeled by kinetic equations with saturation and inhibition effects from external medium components. Importantly, one set of kinetic parameters was estimated using experimental data of the multiple metabolic states. A good fit between the model and the data was obtained for the majority of the metabolites and the experimentally observed flux variations. We find that the poly-pathway modeling approach is promising for the simulation of multiple metabolic states.

RETRACTED: Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model - Application to amino acid depletion in CHO cell culture.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). The authors of the paper wish to retract the paper due to the discovery of a calculation error in the processing of the raw data. The discovered error concerns the calculation of the specific uptake/secretion rates for several metabolites in one of the experimental conditions, i.e. glutamine omission (called Q0). In other words, in Figure 2, the variations of the metabolic fluxes for the condition Q0 are not correct. When this error is corrected, the resulting mathematical model changes (in particular for the results associated with Q0 conditions), several figures and tables are modified, and the interpretation of the fluxes in Q0 has to be slightly modified. Therefore the authors wish to retract the article. However, the error does not affect the modelling approach or the methodology presented in the article. Therefore, a revised version with the correct data has since been published: http://www.sciencedirect.com/science/article/pii/S0168165617302663. We apologize to the scientific community for the need to retract the article and the inconvenience caused.

Multilevel model based glucose control for type-1 diabetes patients.

Diabetes is a disease that involves alterations at multiple biological levels, ranging from intracellular signalling to organ processes. Since glucose homeostasis is the consequence of complex interactions that involve a number of factors, the control of diabetes should be based on a multilevel analysis. In this paper, a novel approach to design of closed-loop glucose controllers based on multilevel models is presented. A control scheme is proposed based on combining a pharmacokinetic/pharmacodynamic model with an insulin signal transduction model for type 1 diabetes mellitus patients. Based on this, an insulin feedback control schemes is designed. Two main advantages of explicitly utilizing information at the intracellular level were obtained. First, significant reduction of hypoglycaemic risk by reducing the undershoot in glucose levels in response to added insulin. Second, robust performance for inter-patient changes, demonstrated through application of the multilevel control strategy to a well established in silico population of diabetic patients.

Multilevel model of type 1 diabetes mellitus patients for model-based glucose controllers.

BACKGROUND: Glucose homeostasis is the result of complex interactions across different biological levels. This multilevel characteristic should be considered when analyzing and designing closed-loop glucose control algorithms. Classic control schemes use only a pharmacokinetic-pharmacodynamic (PKPD) perspective to describe the gluco-regulatory system. METHODS: A multilevel model combining a PKPD model with an insulin signaling model is proposed for patients with type 1 diabetes mellitus T1DM (T1DM). The PKPD Dalla Man model for T1DM is expanded to include an intracellular level involving insulin signaling to control glucose uptake through glucose transporter type 4 (GLUT4) translocation. A model-based controller is then designed and used as an example to illustrate the feasibility of the proposal. RESULTS: Two significant results were obtained for the controller explicitly utilizing multilevel information. No hypo-glycemic events were registered and an excellent performance for interpatient variability was achieved. Controller performance was evaluated using two indexes. The glucose was kept inside the range (70-180) mg/dl more than 99% of the time, and the intrapatient variability measured using control variability grid analysis was solid with 90% of the population inside the target zone. CONCLUSIONS: Multilevel models open new possibilities for designing glucose control algorithms. They allow controllers to take into account variables that have a strong influence on glucose homeostasis. A model-based controller was used for demonstrating how improved knowledge of the multilevel nature of diabetes increases the robustness and performance of glucose control algorithms. Using the proposed multi-level approach, a reduction of the hypoglycemic risk and robust behaviour for intrapatient variability was demonstrated.

Identification of small scale biochemical networks based on general type system perturbations.

New technologies enable acquisition of large data-sets containing genomic, proteomic and metabolic information that describe the state of a cell. These data-sets call for systematic methods enabling relevant information about the inner workings of the cell to be extracted. One important issue at hand is the understanding of the functional interactions between genes, proteins and metabolites. We here present a method for identifying the dynamic interactions between biochemical components within the cell, in the vicinity of a steady-state. Key features of the proposed method are that it can deal with data obtained under perturbations of any system parameter, not only concentrations of specific components, and that the direct effect of the perturbations does not need to be known. This is important as concentration perturbations are often difficult to perform in biochemical systems and the specific effects of general type perturbations are usually highly uncertain, or unknown. The basis of the method is a linear least-squares estimation, using time-series measurements of concentrations and expression profiles, in which system states and parameter perturbations are estimated simultaneously. An important side-effect of also employing estimation of the parameter perturbations is that knowledge of the system's steady-state concentrations, or activities, is not required and that deviations from steady-state prior to the perturbation can be dealt with. Time derivatives are computed using a zero-order hold discretization, shown to yield significant improvements over the widely used Euler approximation. We also show how network interactions with dynamics that are too fast to be captured within the available sampling time can be determined and excluded from the network identification. Known and unknown moiety conservation relationships can be processed in the same manner. The method requires that the number of samples equals at least the number of network components and, hence, is at present restricted to relatively small-scale networks. We demonstrate herein the performance of the method on two small-scale in silico genetic networks.

Determination of atrazine in rainfall and surface water by enzyme immunoassay.

Rainwater and surface water from four sites in Germany (Bavaria and Lower Saxony) were analyzed for atrazine by enzyme immunoassay from June 1990 until October 1992. The limit of quantification of the immunoassay was 0.02 µg/L with a middle of the test at 0.2 µg/L. About 60 % of the samples contained measurable amounts of atrazine. Seasonal trends were observed, with the highest concentration in the summer months of up to 4 µg/L for rainwater and up to 15 µg/L for surface waters. The highest concentrations were found in agricultural areas, while in the investigated national parks up to 0.56 µg/L could be detected in rain water. This points to long-range atmospheric transport from agricultural areas to pristine national parks. Samples from forest stands usually showed higher atrazine concentrations than samples from open fields. Deposition rates of 10 - 50 µg/m(2) · yr were observed in the national parks and 10-180 µg/m(2) · yr at the agricultural sites. Comparison of results obtained by enzyme immunoassay and GC/MS showed a good correlation of r = 0.95.

Atmospheric transport of atrazine: a simple device for its detection.

Thin-layer chromatography plates were exposed at three different locations in Bavaria for 3-week periods during the growing season of maize. The adsorption of traces of atrazine was detected by thin-layer chromatography, GC-MS, and by an enzyme immunoassay. It was restricted to the sowing season, which coincides with the application of atrazine. The herbicide could not be detected after this time. The simplicity of the adsorption device, combined with a serological assay, render this procedure suitable for detecting airborne organic pollutants.