Advanced error modeling and Bayesian uncertainty quantification in mechanistic liquid chromatography modeling.

Current mechanistic chromatography process modeling methods lack the ability to account for the impact of experimental errors beyond detector noise (e.g. pump delays and variable feed composition) on the uncertainty in calibrated model parameters and the resulting model-predicted chromatograms. This paper presents an uncertainty quantification method that addresses this limitation by determining the probability distribution of parameters in calibrated models, taking into consideration multiple realistic sources of experimental error. The method, which is based on Bayes' theorem and utilizes Markov chain Monte Carlo with an ensemble sampler, is demonstrated to be robust and extensible using synthetic and industrial data. The corresponding software is freely available as open-source code at https://github.com/modsim/CADET-Match.

Reliable cell retention of mammalian suspension cells in microfluidic cultivation chambers.

Microfluidic cultivation, with its high level of environmental control and spatio-temporal resolution of cellular behavior, is a well-established tool in today's microfluidics. Yet, reliable retention of (randomly) motile cells inside designated cultivation compartments still represents a limitation, which prohibits systematic single-cell growth studies. To overcome this obstacle, current approaches rely on complex multilayer chips or on-chip valves, which makes their application for a broad community of users infeasible. Here, we present an easy-to-implement cell retention concept to withhold cells inside microfluidic cultivation chambers. By introducing a blocking structure into a cultivation chamber's entrance and nearly closing it, cells can be manually pushed into the chamber during loading procedures but are unable to leave it autonomously in subsequent long-term cultivation. CFD simulations as well as trace substance experiments confirm sufficient nutrient supply within the chamber. Through preventing recurring cell loss, growth data obtained from Chinese hamster ovary cultivation on colony level perfectly match data determined from single-cell data, which eventually allows reliable high throughput studies of single-cell growth. Due to its transferability to other chamber-based approaches, we strongly believe that our concept is also applicable for a broad range of cellular taxis studies or analyses of directed migration in basic or biomedical research.

Machine learning in bioprocess development: from promise to practice.

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Mechanisms and Effects of Substrate Channelling in Enzymatic Cascades.

Substrate or metabolite channelling is a transfer of intermediates produced by one enzyme to the sequential enzyme of a reaction cascade or metabolic pathway, without releasing them entirely into bulk. Despite an enormous effort and more than three decades of research, substrate channelling remains the subject of continuing debates and active investigation. Herein, we review the benefits and mechanisms of substrate channelling in vivo and in vitro. We discuss critically the effects that substrate channelling can have on enzymatic cascades, including speeding up or slowing down reaction cascades and protecting intermediates from sequestration and enzymes' surroundings from toxic or otherwise detrimental intermediates. We also discuss how macromolecular crowding affects substrate channelling and point out the galore of open questions.

Bayesian calibration, process modeling and uncertainty quantification in biotechnology.

High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

Advanced score system and automated search strategies for parameter estimation in mechanistic chromatography modeling.

Least squares estimation of unknown parameters from measurement data is a well-established standard method in chromatography modeling but can suffer from critical disadvantages. The description of real-world systems is generally prone to unaccounted mechanisms, such as dispersion in external holdup volumes, and systematic measurement errors, such as caused by pump delays. In this scenario, matching the shape between simulated and measured chromatograms has been found to be more important than the exact peak positions. We have therefore developed a new score system that separately accounts for the shape, position and height of individual peaks. A genetic algorithm is used for optimizing these multiple objectives. Even for non-conflicting objectives, this approach shows superior convergence in comparison to single-objective gradient search, while conflicting objectives indicate incomplete models or inconsistent data. In the latter case, Pareto optima provide important information for understanding the system and improving experiments. The proposed method is demonstrated with synthetic and experimental case studies of increasing complexity. All software is freely available as open source code (https://github.com/modsim/CADET-Match).

Robust mechanistic modeling of protein ion-exchange chromatography.

Mechanistic models for ion-exchange chromatography of proteins are well-established and a broad consensus exists on most aspects of the detailed mathematical and physical description. A variety of specializations of these models can typically capture the general locations of elution peaks, but discrepancies are often observed in peak position and shape, especially if the column load level is in the non-linear range. These discrepancies may prevent the use of models for high-fidelity predictive applications such as process characterization and development of high-purity and -productivity process steps. Our objective is to develop a sufficiently robust mechanistic framework to make both conventional and anomalous phenomena more readily predictable using model parameters that can be evaluated based on independent measurements or well-accepted correlations. This work demonstrates the implementation of this approach for industry-relevant case studies using both a model protein, lysozyme, and biopharmaceutical product monoclonal antibodies, using cation-exchange resins with a variety of architectures (SP Sepharose FF, Fractogel EMD SO3-, Capto S and Toyopearl SP650M). The modeling employs the general rate model with the extension of the surface diffusivity to be variable, as a function of ionic strength or binding affinity. A colloidal isotherm that accounts for protein-surface and protein-protein interactions independently was used, with each characterized by a parameter determined as a function of ionic strength and pH. Both of these isotherm parameters, along with the variable surface diffusivity, were successfully estimated using breakthrough data at different ionic strengths and pH. The model developed was used to predict overloads and elution curves with high accuracy for a wide variety of gradients and different flow rates and protein loads. The in-silico methodology used in this work for parameter estimation, along with a minimal amount of experimental data, can help the industry adopt model-based optimization and control of preparative ion-exchange chromatography with high accuracy.

Patterns of protein adsorption in ion-exchange particles and columns: Evolution of protein concentration profiles during load, hold, and wash steps predicted for pore and solid diffusion mechanisms.

Elucidation of protein transport mechanism in ion exchanges is essential to model separation performance. In this work we simulate intraparticle adsorption profiles during batch adsorption assuming typical process conditions for pore, solid and parallel diffusion. Artificial confocal laser scanning microscopy images are created to identify apparent differences between the different transport mechanisms. Typical sharp fronts for pore diffusion are characteristic for Langmuir equilibrium constants of KL ≥1. Only at KL = 0.1 and lower, the profiles are smooth and practically indistinguishable from a solid diffusion mechanism. During hold and wash steps, at which the interstitial buffer is removed or exchanged, continuation of diffusion of protein molecules is significant for solid diffusion due to the adsorbed phase concentration driving force. For pore diffusion, protein mobility is considerable at low and moderate binding strength. Only when pore diffusion if completely dominant, and the binding strength is very high, protein mobility is low enough to restrict diffusion out of the particles. Simulation of column operation reveals substantial protein loss when operating conditions are not adjusted appropriately.

Complex Evolution of Light-Dependent Protochlorophyllide Oxidoreductases in Aerobic Anoxygenic Phototrophs: Origin, Phylogeny, and Function.

Light-dependent protochlorophyllide oxidoreductase (LPOR) and dark-operative protochlorophyllide oxidoreductase are evolutionary and structurally distinct enzymes that are essential for the synthesis of (bacterio)chlorophyll, the primary pigment needed for both anoxygenic and oxygenic photosynthesis. In contrast to the long-held hypothesis that LPORs are only present in oxygenic phototrophs, we recently identified a functional LPOR in the aerobic anoxygenic phototrophic bacterium (AAPB) Dinoroseobacter shibae and attributed its presence to a single horizontal gene transfer event from cyanobacteria. Here, we provide evidence for the more widespread presence of genuine LPOR enzymes in AAPBs. An exhaustive bioinformatics search identified 36 putative LPORs outside of oxygenic phototrophic bacteria (cyanobacteria) with the majority being AAPBs. Using in vitro and in vivo assays, we show that the large majority of the tested AAPB enzymes are genuine LPORs. Solution structural analyses, performed for two of the AAPB LPORs, revealed a globally conserved structure when compared with a well-characterized cyanobacterial LPOR. Phylogenetic analyses suggest that LPORs were transferred not only from cyanobacteria but also subsequently between proteobacteria and from proteobacteria to Gemmatimonadetes. Our study thus provides another interesting example for the complex evolutionary processes that govern the evolution of bacteria, involving multiple horizontal gene transfer events that likely occurred at different time points and involved different donors.

Development and application of a cultivation platform for mammalian suspension cell lines with single-cell resolution.

In bioproduction processes, cellular heterogeneity can cause unpredictable process outcomes or even provoke process failure. Still, cellular heterogeneity is not examined systematically in bioprocess research and development. One reason for this shortcoming is the applied average bulk analyses, which are not able to detect cell-to-cell differences. In this study, we present a microfluidic tool for mammalian single-cell cultivation (MaSC) of suspension cells. The design of our platform allows cultivation in highly controllable environments. As a model system, Chinese hamster ovary cells (CHO-K1) were cultivated over 150 h. Growth behavior was analyzed on a single-cell level and resulted in growth rates between 0.85 and 1.16 day-1 . At the same time, heterogeneous growth and division behavior, for example, unequal division time, as well as rare cellular events like polynucleation or reversed mitosis were observed, which would have remained undetected in a standard population analysis based on average measurements. Therefore, MaSC will open the door for systematic single-cell analysis of mammalian suspension cells. Possible fields of application represent basic research topics like cell-to-cell heterogeneity, clonal stability, pharmaceutical drug screening, and stem cell research, as well as bioprocess related topics such as media development and novel scale-down approaches.

dMSCC: a microfluidic platform for microbial single-cell cultivation of Corynebacterium glutamicum under dynamic environmental medium conditions.

In nature and in technical systems, microbial cells are often exposed to rapidly fluctuating environmental conditions. These conditions can vary in quality, e.g., the existence of a starvation zone, and quantity, e.g., the average residence time in this zone. For strain development and process design, cellular response to such fluctuations needs to be systematically analysed. However, the existing methods for physically imitating rapidly changing environmental conditions are limited in spatio-temporal resolution. Hence, we present a novel microfluidic system for cultivation of single cells and small cell clusters under dynamic environmental conditions (dynamic microfluidic single-cell cultivation (dMSCC)). This system enables the control of nutrient availability and composition between two media with second to minute resolution. We validate our technology using the industrially relevant model organism Corynebacterium glutamicum. The organism was exposed to different oscillation frequencies between nutrient excess (feasts) and scarcity (famine). The resulting changes in cellular physiology, such as the colony growth rate and cell morphology, were analysed and revealed significant differences in the growth rate and cell length between the different conditions. dMSCC also allows the application of defined but randomly changing nutrient conditions, which is important for reproducing more complex conditions from natural habitats and large-scale bioreactors. The presented system lays the foundation for the cultivation of cells under complex changing environmental conditions.

Toward in silico CMC: An industrial collaborative approach to model-based process development.

The Third Modeling Workshop focusing on bioprocess modeling was held in Kenilworth, NJ in May 2019. A summary of these Workshop proceedings is captured in this manuscript. Modeling is an active area of research within the biotechnology community, and there is a critical need to assess the current state and opportunities for continued investment to realize the full potential of models, including resource and time savings. Beyond individual presentations and topics of novel interest, a substantial portion of the Workshop was devoted toward group discussions of current states and future directions in modeling fields. All scales of modeling, from biophysical models at the molecular level and up through large scale facility and plant modeling, were considered in these discussions and are summarized in the manuscript. Model life cycle management from model development to implementation and sustainment are also considered for different stages of clinical development and commercial production. The manuscript provides a comprehensive overview of bioprocess modeling while suggesting an ideal future state with standardized approaches aligned across the industry.

Dynamic Environmental Control in Microfluidic Single-Cell Cultivations: From Concepts to Applications.

Microfluidic single-cell cultivation (MSCC) is an emerging field within fundamental as well as applied biology. During the last years, most MSCCs were performed at constant environmental conditions. Recently, MSCC at oscillating and dynamic environmental conditions has started to gain significant interest in the research community for the investigation of cellular behavior. Herein, an overview of this topic is given and microfluidic concepts that enable oscillating and dynamic control of environmental conditions with a focus on medium conditions are discussed, and their application in single-cell research for the cultivation of both mammalian and microbial cell systems is demonstrated. Furthermore, perspectives for performing MSCC at complex dynamic environmental profiles of single parameters and multiparameters (e.g., pH and O2 ) in amplitude and time are discussed. The technical progress in this field provides completely new experimental approaches and lays the foundation for systematic analysis of cellular metabolism at fluctuating environments.

Multiscale dynamic modeling and simulation of a biorefinery.

A biorefinery comprises a variety of process steps to synthesize products from sustainable natural resources. Dynamic plant-wide simulation enhances the process understanding, leads to improved cost efficiency and enables model-based operation and control. It is thereby important for an increased competitiveness to conventional processes. To this end, we developed a Modelica library with replaceable building blocks that allow dynamic modeling of an entire biorefinery. For the microbial conversion step, we built on the dynamic flux balance analysis (DFBA) approach to formulate process models for the simulation of cellular metabolism under changing environmental conditions. The resulting system of differential-algebraic equations with embedded optimization criteria (DAEO) is solved by a tailor-made toolbox. In summary, our modeling framework comprises three major pillars: A Modelica library of dynamic unit operations, an easy-to-use interface to formulate DFBA process models and a DAEO toolbox that allows simulation with standard environments based on the Modelica modeling language. A biorefinery model for dynamic simulation of the OrganoCat pretreatment process and microbial conversion of the resulting feedstock by Corynebacterium glutamicum serves as case study to demonstrate its practical relevance.

Can enzyme proximity accelerate cascade reactions?

The last decade has seen an exponential expansion of interest in conjugating multiple enzymes of cascades in close proximity to each other, with the overarching goal being to accelerate the overall reaction rate. However, some evidence has emerged that there is no effect of proximity channeling on the reaction velocity of the popular GOx-HRP cascade, particularly in the presence of a competing enzyme (catalase). Herein, we rationalize these experimental results quantitatively. We show that, in general, proximity channeling can enhance reaction velocity in the presence of competing enzymes, but in steady state a significant enhancement can only be achieved for diffusion-limited reactions or at high concentrations of competing enzymes. We provide simple equations to estimate the effect of channeling quantitatively and demonstrate that proximity can have a more pronounced effect under crowding conditions in vivo, particularly that crowding can enhance the overall rates of channeled cascade reactions.

Microbial single-cell growth response at defined carbon limiting conditions.

Growth is one of the most fundamental characteristics of life, but detailed knowledge regarding growth at nutrient limiting conditions remains scarce. In recent years progress in microfluidic single-cell analysis and cultivation techniques has given insights into many fundamental growth characteristics such as growth homeostasis, aging and cell division of microbial cells. Using microfluidic single-cell cultivation technologies we examined how single-cell growth at defined carbon conditions, ranging from strongly limiting conditions (0.01 mmol L-1) to a carbon surplus (100 mmol L-1), influenced cell-to-cell variability. The experiments showed robust growth of populations at intermediate concentrations and cell-to-cell variability was higher at low and high carbon concentrations, among an isogenic population. Single-cell growth at extremely limiting conditions led not only to significant variability of division times, but also to an increased number of cells that did not pursue growth. Overall, the results demonstrate that cellular behaviour shows robust, Monod-like growth, with significant cell-to-cell heterogeneity at extreme limiting conditions, resembling natural habitats. Due to this significant influence of the environment on cellular physiology, more carefulness needs to be given future microfluidic single-cell experiments. Consequently, our results lay the foundation for the re-interpretation and design of workflows for future experiments aiming at an improved understanding of cell growth mechanisms.

A microfluidic co-cultivation platform to investigate microbial interactions at defined microenvironments.

Interspecies interactions inside microbial communities bear a tremendous diversity of complex chemical processes that are by far not understood. Even for simplified, often synthetic systems, the interactions between two microbes are barely revealed in detail. Here, we present a microfluidic co-cultivation platform for the analysis of growth and interactions inside microbial consortia with single-cell resolution. Our device allows the spatial separation of two different microbial organisms inside adjacent microchambers facilitating sufficient exchange of metabolites via connecting nanochannels. Inside the cultivation chambers cell growth can be observed with high spatio-temporal resolution by live-cell imaging. In contrast to conventional approaches, in which single-cell activity is typically fully masked by the average bulk behavior, the small dimensions of the microfluidic cultivation chambers enable accurate environmental control and observation of cellular interactions with full spatio-temporal resolution. Our method enables one to study phenomena in microbial interactions, such as gene transfer or metabolic cross-feeding. We chose two different microbial model systems to demonstrate the wide applicability of the technology. First, we investigated commensalistic interactions between an industrially relevant l-lysine-producing Corynebacterium glutamicum strain and an l-lysine auxotrophic variant of the same species. Spatially separated co-cultivation of both strains resulted in growth of the auxotrophic strain due to secreted l-lysine supplied by the producer strain. As a second example we investigated bacterial conjugation between Escherichia coli S17-1 and Pseudomonas putida KT2440 cells. We could show that direct cell contact is essential for the successful gene transfer via conjugation and was hindered when cells were spatially separated. The presented device lays the foundation for further studies on contactless and contact-based interactions of natural and synthetic microbial communities.

Model-Based Design of Long-Distance Tracer Transport Experiments in Plants.

Studies of long-distance transport of tracer isotopes in plants offer a high potential for functional phenotyping, but so far measurement time is a bottleneck because continuous time series of at least 1 h are required to obtain reliable estimates of transport properties. Hence, usual throughput values are between 0.5 and 1 samples h-1. Here, we propose to increase sample throughput by introducing temporal gaps in the data acquisition of each plant sample and measuring multiple plants one after each other in a rotating scheme. In contrast to common time series analysis methods, mechanistic tracer transport models allow the analysis of interrupted time series. The uncertainties of the model parameter estimates are used as a measure of how much information was lost compared to complete time series. A case study was set up to systematically investigate different experimental schedules for different throughput scenarios ranging from 1 to 12 samples h-1. Selected designs with only a small amount of data points were found to be sufficient for an adequate parameter estimation, implying that the presented approach enables a substantial increase of sample throughput. The presented general framework for automated generation and evaluation of experimental schedules allows the determination of a maximal sample throughput and the respective optimal measurement schedule depending on the required statistical reliability of data acquired by future experiments.

Single-cell computational analysis of light harvesting in a flat-panel photo-bioreactor.

BACKGROUND: Flat-panel photo-bioreactors (PBRs) are customarily applied for investigating growth of microalgae. Optimal design and operation of such reactors is still a challenge due to complex non-linear combinations of various impact factors, particularly hydrodynamics, light irradiation, and cell metabolism. A detailed analysis of single-cell light reception can lead to novel insights into the complex interactions of light exposure and algae movement in the reactor. RESULTS: The combined impacts of hydrodynamics and light irradiation on algae cultivation in a flat-panel PBR were studied by tracing the light exposure of individual cells over time. Hydrodynamics and turbulent mixing in this air-sparged bioreactor were simulated using the Eulerian approach for the liquid phase and a slip model for the gas phase velocity profiles. The liquid velocity was then used for tracing single cells and their light exposure, using light intensity profiles obtained from solving the radiative transfer equation at different wavelengths. The residence times of algae cells in defined dark and light zones of the PBR were statistically analyzed for different algal concentrations and sparging rates. The results indicate poor mixing caused by the reactor design which can be only partially improved by increased sparging rates. CONCLUSIONS: The results provide important information for optimizing algal biomass productivity by improving bioreactor design and operation and can further be utilized for an in-depth analysis of algal growth by using advanced models of cell metabolism.

Laboratory-scale photobiotechnology-current trends and future perspectives.

Phototrophic bioprocesses are a promising puzzle piece in future bioeconomy concepts but yet mostly fail for economic reasons. Besides other aspects, this is mainly attributed to the omnipresent issue of optimal light supply impeding scale-up and -down of phototrophic processes according to classic established concepts. This MiniReview examines two current trends in photobiotechnology, namely microscale cultivation and modeling and simulation. Microphotobioreactors are a valuable and promising trend with microfluidic chips and microtiter plates as predominant design concepts. Providing idealized conditions, chip systems are preferably to be used for acquiring physiological data of microalgae while microtiter plate systems are more appropriate for process parameter and medium screenings. However, these systems are far from series technology and significant improvements especially regarding flexible light supply remain crucial. Whereas microscale is less addressed by modeling and simulation so far, benchtop photobioreactor design and operation have successfully been studied using such tools. This particularly includes quantitative model-assisted understanding of mixing, mass transfer, light dispersion and particle tracing as well as their relevance for microalgal performance. The ultimate goal will be to combine physiological data from microphotobioreactors with hybrid models to integrate metabolism and reactor simulation in order to facilitate knowledge-based scale transfer of phototrophic bioprocesses.