Application of global sensitivity analysis to determine goals for design of experiments: an example study on antibody-producing cell cultures

Deciphering Metabolic Pathways in High-Seeding-Density Fed-Batch Processes for Monoclonal Antibody Production: A Computational Modeling Perspective

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high-seeding-density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells, focused on identifying significant metabolites and their interactions. We observed high glycolytic fluxes, the depletion of asparagine, and a shift from lactate production to consumption. Using a metabolic network and flux analysis, we compared the standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched with amino acids. We reconstructed a metabolic network and kinetic models based on the observations and explored the effects of different feeding strategies on CHO cell metabolism. Our findings revealed that the addition of a bolus medium (BM) containing asparagine improved final titers. However, increasing the asparagine concentration in the feed further prevented the lactate shift, indicating a need to find a balance between increased asparagine to counteract limitations and lower asparagine to preserve the shift in lactate metabolism.

Identification of a time-varying intracellular signalling model through data clustering and parameter selection: application to NF-[inline-formula removed]B signalling pathway induced by LPS in the presence of BFA

Developing a model for a signalling pathway requires several iterations of experimentation and model refinement to obtain an accurate model. However, the implementation of such an approach to model a signalling pathway induced by a poorly-known stimulus can become labour intensive because only limited information on the pathway is available beforehand to formulate an initial model. Therefore, a large number of iterations are required since the initial model is likely to be erroneous. In this work, a numerical scheme is proposed to construct a time-varying model for a signalling pathway induced by a poorly-known stimulus when its nominal model is available in the literature. Here, the nominal model refers to one that describes the signalling dynamics under a well-characterised stimulus. First, global sensitivity analysis is implemented on the nominal model to identify the most important parameters, which are assumed to be piecewise constants. Second, measurement data are clustered to determine temporal subdomains where the parameters take different values. Finally, a least-squares problem is solved to estimate the parameter values in each temporal subdomain. The effectiveness of this approach is illustrated by developing a time-varying model for NF-[inline-formula removed]B signalling dynamics induced by lipopolysaccharide in the presence of brefeldin A.

Strategic Framework for Parameterization of Cell Culture Models

Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and criteria in the sensitivity analysis of a recently developed mathematical model to describe Chinese hamster ovary (CHO) cell metabolism in order to establish a strategic, transferable framework for parameterizing mechanistic cell culture models. For that reason, several types of GSA employing different sampling methods (Sobol’, Pseudo-random and Scrambled-Sobol’), parameter deviations (10%, 30% and 50%) and sensitivity index significance thresholds (0.05, 0.1 and 0.2) were examined. The results were evaluated according to the goodness of fit between the simulation results and experimental data from fed-batch CHO cell cultures. Then, the predictive capability of the model was tested against four different feeding experiments. Parameter value deviation levels proved not to have a significant effect on the results of the sensitivity analysis, while the Sobol’ and Scrambled-Sobol’ sampling methods and a 0.1 significance threshold were found to be the optimum settings. The resulting framework was finally used to calibrate the model for another CHO cell line, resulting in a good overall fit. The results of this work set the basis for the use of a single mechanistic metabolic model that can be easily adapted through the proposed sensitivity analysis method to the behavior of different cell lines and therefore minimize the experimental cost of model development.

Global sensitivity analysis and Bayesian parameter inference for solute transport in porous media colonized by biofilms

The concept of dual flowing continuum is a promising approach for modeling solute transport in porous media that includes biofilm phases. The highly dispersed transit time distributions often generated by these media are taken into consideration by simply stipulating that advection-dispersion transport occurs through both the porous and the biofilm phases. Both phases are coupled but assigned with contrasting hydrodynamic properties. However, the dual flowing continuum suffers from intrinsic equifinality in the sense that the outlet solute concentration can be the result of several parameter sets of the two flowing phases. To assess the applicability of the dual flowing continuum, we investigate how the model behaves with respect to its parameters. For the purpose of this study, a Global Sensitivity Analysis (GSA) and a Statistical Calibration (SC) of model parameters are performed for two transport scenarios that differ by the strength of interaction between the flowing phases. The GSA is shown to be a valuable tool to understand how the complex system behaves. The results indicate that the rate of mass transfer between the two phases is a key parameter of the model behavior and influences the identifiability of the other parameters. For weak mass exchanges, the output concentration is mainly controlled by the velocity in the porous medium and by the porosity of both flowing phases. In the case of large mass exchanges, the kinetics of this exchange also controls the output concentration. The SC results show that transport with large mass exchange between the flowing phases is more likely affected by equifinality than transport with weak exchange. The SC also indicates that weakly sensitive parameters, such as the dispersion in each phase, can be accurately identified. Removing them from calibration procedures is not recommended because it might result in biased estimations of the highly sensitive parameters.

Cyclin and DNA distributed cell cycle model for GS-NS0 cells

Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins-key molecules that regulate cell cycle transition-have been utilized. Herein, a novel integrated experimental-modelling platform is presented whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cycle-specific production rates. The solution method delivered fast computational time that renders the model's use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies.

Systematic methodology for the development of mathematical models for biological processes

Synthetic biology gives researchers the opportunity to rationally (re-)design cellular activities to achieve a desired function. The design of networks of pathways towards accomplishing this calls for the application of engineering principles, often using model-based tools. Success heavily depends on model reliability. Herein, we present a systematic methodology for developing predictive models comprising model formulation considerations, global sensitivity analysis, model reduction (for highly complex models or where experimental data are limited), optimal experimental design for parameter estimation, and predictive capability checking. Its efficacy and validity are demonstrated using an example from bioprocessing. This approach systematizes the process of developing reliable mathematical models at a minimum experimental cost, enabling in silico simulation and optimization.

Towards controlling the glycoform: a model framework linking extracellular metabolites to antibody glycosylation

Glycoproteins represent the largest group of the growing number of biologically-derived medicines. The associated glycan structures and their distribution are known to have a large impact on pharmacokinetics. A modelling framework was developed to provide a link from the extracellular environment and its effect on intracellular metabolites to the distribution of glycans on the constant region of an antibody product. The main focus of this work is the mechanistic in silico reconstruction of the nucleotide sugar donor (NSD) metabolic network by means of 34 species mass balances and the saturation kinetics rates of the 60 metabolic reactions involved. NSDs are the co-substrates of the glycosylation process in the Golgi apparatus and their simulated dynamic intracellular concentration profiles were linked to an existing model describing the distribution of N-linked glycan structures of the antibody constant region. The modelling framework also describes the growth dynamics of the cell population by means of modified Monod kinetics. Simulation results match well to experimental data from a murine hybridoma cell line. The result is a modelling platform which is able to describe the product glycoform based on extracellular conditions. It represents a first step towards the in silico prediction of the glycoform of a biotherapeutic and provides a platform for the optimisation of bioprocess conditions with respect to product quality.

Switch of sensitivity dynamics revealed with DyGloSA toolbox for dynamical global sensitivity analysis as an early warning for system's critical transition

Systems with bifurcations may experience abrupt irreversible and often unwanted shifts in their performance, called critical transitions. For many systems like climate, economy, ecosystems it is highly desirable to identify indicators serving as early warnings of such regime shifts. Several statistical measures were recently proposed as early warnings of critical transitions including increased variance, autocorrelation and skewness of experimental or model-generated data. The lack of automatized tool for model-based prediction of critical transitions led to designing DyGloSA - a MATLAB toolbox for dynamical global parameter sensitivity analysis (GPSA) of ordinary differential equations models. We suggest that the switch in dynamics of parameter sensitivities revealed by our toolbox is an early warning that a system is approaching a critical transition. We illustrate the efficiency of our toolbox by analyzing several models with bifurcations and predicting the time periods when systems can still avoid going to a critical transition by manipulating certain parameter values, which is not detectable with the existing SA techniques. DyGloSA is based on the SBToolbox2 and contains functions, which compute dynamically the global sensitivity indices of the system by applying four main GPSA methods: eFAST, Sobol's ANOVA, PRCC and WALS. It includes parallelized versions of the functions enabling significant reduction of the computational time (up to 12 times). DyGloSA is freely available as a set of MATLAB scripts at http://bio.uni.lu/systems_biology/software/dyglosa. It requires installation of MATLAB (versions R2008b or later) and the Systems Biology Toolbox2 available at www.sbtoolbox2.org. DyGloSA can be run on Windows and Linux systems, -32 and -64 bits.

Analysis and design of a genetic circuit for dynamic metabolic engineering

Recent advances in synthetic biology have equipped us with new tools for bioprocess optimization at the genetic level. Previously, we have presented an integrated in silico design for the dynamic control of gene expression based on a density-sensing unit and a genetic toggle switch. In the present paper, analysis of a serine-producing Escherichia coli mutant shows that an instantaneous ON-OFF switch leads to a maximum theoretical productivity improvement of 29.6% compared to the mutant. To further the design, global sensitivity analysis is applied here to a mathematical model of serine production in E. coli coupled with a genetic circuit. The model of the quorum sensing and the toggle switch involves 13 parameters of which 3 are identified as having a significant effect on serine concentration. Simulations conducted in this reduced parameter space further identified the optimal ranges for these 3 key parameters to achieve productivity values close to the maximum theoretical values. This analysis can now be used to guide the experimental implementation of a dynamic metabolic engineering strategy and reduce the time required to design the genetic circuit components.

Experimental and in silico modelling analyses of the gene expression pathway for recombinant antibody and by-product production in NS0 cell lines

Monoclonal antibodies are commercially important, high value biotherapeutic drugs used in the treatment of a variety of diseases. These complex molecules consist of two heavy chain and two light chain polypeptides covalently linked by disulphide bonds. They are usually expressed as recombinant proteins from cultured mammalian cells, which are capable of correctly modifying, folding and assembling the polypeptide chains into the native quaternary structure. Such recombinant cell lines often vary in the amounts of product produced and in the heterogeneity of the secreted products. The biological mechanisms of this variation are not fully defined. Here we have utilised experimental and modelling strategies to characterise and define the biology underpinning product heterogeneity in cell lines exhibiting varying antibody expression levels, and then experimentally validated these models. In undertaking these studies we applied and validated biochemical (rate-constant based) and engineering (nonlinear) models of antibody expression to experimental data from four NS0 cell lines with different IgG4 secretion rates. The models predict that export of the full antibody and its fragments are intrinsically linked, and cannot therefore be manipulated individually at the level of the secretory machinery. Instead, the models highlight strategies for the manipulation at the precursor species level to increase recombinant protein yields in both high and low producing cell lines. The models also highlight cell line specific limitations in the antibody expression pathway.

Effects of clonal variation on growth, metabolism, and productivity in response to trophic factor stimulation: a study of Chinese hamster ovary cells producing a recombinant monoclonal antibody

The growth, metabolism, and productivity of five Chinese hamster ovary (CHO) clones were explored in response to stimulation with insulin (5 mg/L) and LONG(®)R(3)IGF-I (20 μg/L or 100 μg/L). All five clones were derived from the same parental CHO cell line (DG44) and produced the same recombinant monoclonal antibody, with varying specific productivities. There was no uniform response among the clones to stimulation with the different trophic factors. One of the high productivity clones (clone D) exhibited significantly better growth in response to LONG(®)R(3)IGF-I; whereas the other clones showed equivalent or slightly better growth in the presence of insulin. Three out of the five clones had higher specific productivities in the presence of insulin (although not statistically significant); one was invariant, and the final clone exhibited slightly higher specific productivity in the presence of LONG(®)R(3)IGF-I. Total product titers exhibited moderate variation between culture conditions, again with neither trophic factor being clearly superior. Overall product titers were affected by variations in both integrated viable cell density and specific productivity. Nutrient uptake and metabolite generation patterns varied strongly between clones and much less with culture conditions. These results point to the need for careful clonal analysis when selecting clones, particularly for platform processes where media and culture conditions are predetermined.

Crystal ball - 2011

In this feature, leading researchers in the field of microbial biotechnology speculate on the technical and conceptual developments that will drive innovative research and open new vistas over the next few years.

Sloppy models, parameter uncertainty, and the role of experimental design

Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by trajectory measurements. We examined this question in the context of a pathway model of epidermal growth factor (EGF) and neuronal growth factor (NGF) signaling. Computationally, we examined a palette of experimental perturbations that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, experimental design methodology identified a set of five complementary experiments that could. These results suggest optimism for the prospects for calibrating even large models, that the success of parameter estimation is intimately linked to the experimental perturbations used, and that experimental design methodology is important for parameter fitting of biological models and likely for the accuracy that can be expected from them.

Analysis of kinetic, stoichiometry and regulation of glucose and glutamine metabolism in hybridoma batch cultures using logistic equations

Batch cultures were carried out to study the kinetic, stoichiometry, and regulation of glucose and glutamine metabolism of a murine hybridoma line. Asymmetric logistic equations (ALEs) were used to fit total and viable cell density, and nutrient and metabolite/product concentrations. Since these equations were analytically differentiable, specific rates and yield coefficients were readily calculated. Asymmetric logistic equations described satisfactorily uncontrolled batch cultures, including death phase. Specific growth rate showed a Monod-type dependence on initial glucose and glutamine concentrations. Yield coefficients of cell and lactate from glucose, and cell and ammonium from glutamine were all found to change dramatically at low residual glucose and glutamine concentrations. Under stoichiometric glucose limitation, the glucose-to-cell yield increased and glucose-to-lactate yield decreased, indicating a metabolic shift. Under stoichiometric glutamine limitation the glutamine-to-cell and glutamine-to-ammonium yields increased, but also glucose-to-cell yield increased and the glucose-to-lactate yield decreased. Monoclonal antibody production was mainly non-growth associated, independently of glucose and glutamine levels.