Homogenizing bacterial cell factories: Analysis and engineering of phenotypic heterogeneity

Binder, Dennis; Drepper, Thomas; Jaeger, Karl‐Erich; Delvigne, Frank; Wiechert, Wolfgang; Kohlheyer, Dietrich; Grünberger, Alexander

doi:10.1016/j.ymben.2017.06.009

Cited by 95 publications

(81 citation statements)

References 123 publications

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“…Therefore, the introduction of ANN reduces the computational cost. The proposed methodology can be implemented in a biomanufacturing process for estimation and control, as cell‐to‐cell variability can result in reduced yields and a bioreactor instability . Although it is now feasible to measure the dynamics of biomolecules at the single‐cell level, it is still difficult to measure the dynamics of every biomolecule of interest at the single‐cell level .…”

Section: Discussionmentioning

confidence: 99%

“…The proposed methodology can be implemented in a biomanufacturing process for estimation and control, as cell-to-cell variability can result in reduced yields and a bioreactor instability. 5,59,60 Although it is now feasible to measure the dynamics of biomolecules at the single-cell level, 61,62 it is still difficult to measure the dynamics of every biomolecule of interest at the single-cell level. 3 Therefore, the proposed methodology can be used to estimate the PDFs of unmeasurable biomolecules by estimating the underlying PDFs of parameters so that the biological system can be monitored more effectively.…”

Section: Discussionmentioning

confidence: 99%

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Identification of cell‐to‐cell heterogeneity through systems engineering approaches

Lee¹,

Jayaraman²,

Kwon³

2020

AIChE Journal

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Cells in a genetically homogeneous cell‐population exhibit a significant degree of heterogeneity in their responses to an external stimulus. To understand origins and importance of this heterogeneity, individual‐based population model (IBPM), where parameters follow probability density functions (PDFs) instead of being constants, has been previously developed. However, parameter identification for an IBPM is challenging as estimating PDFs is computationally expensive. Also, because of experimental limitations and nonlinearity of models, not all parameters' PDFs are identifiable. Motivated by the above considerations, a new methodology is proposed in this study. First, a subset of parameters whose PDFs is identifiable are determined through sensitivity analysis, and only these PDFs are estimated. Second, an artificial neural network model is developed to find an empirical relation between these parameter and output PDFs to reduce computational costs of the parameter identification. The proposed approach is validated by estimating PDFs of parameters of a tumor necrosis factor‐α signaling model.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of cell‐to‐cell heterogeneity through systems engineering approaches

Lee¹,

Jayaraman²,

Kwon³

2020

AIChE Journal

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“…However, in industrial settings, this heterogeneity allows evolutionary advantage in fitness for cells to survive stress conditions (Ackermann, ), which likely translates to a disadvantage considering performance losses (lower TRY) of biotechnological processes (J. Schmitz, Noll, & Grunberger, ). Research on population heterogeneity can, therefore, reveal novel insights into microbial behavior and can bring avenues to develop more stable cell lines and more reproducible bioprocesses (Binder et al, ). The single‐cell metabolome is an immediate recording of how cells respond to environmental cues and directly indicates the phenotypic diversity of single cells (Zenobi, ).…”

Section: Future Prospectmentioning

confidence: 99%

“…Schmitz, Noll, & Grunberger, 2019). Research on population heterogeneity can, therefore, reveal novel insights into microbial behavior and can bring avenues to develop more stable cell lines and more reproducible bioprocesses (Binder et al, 2017). The single-cell metabolome is an immediate recording of how cells respond to environmental cues and directly indicates the phenotypic diversity of single cells (Zenobi, 2013).…”

Section: Future Prospectmentioning

confidence: 99%

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

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“…a specific cell size, for the whole population, the properties vary from cell to cell and thus appear distributed in the ensemble. These variances can have significant effects on the corresponding processes, such as a reduction of production yields in bioreactors or even process instabilities (Binder et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Hybrid simulation algorithm for efficient numerical solution of population balance equations

Dürr

Waldherr

2018

IFAC-PapersOnLine

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Cell-to-cell variability of multicellular systems affects many processes from bioprocess engineering, systems biology and systems medicine. Multiscale modeling of these cellular heterogeneities with population balance equations is a valuable tool to understand the interplay of individual cell dynamics (intracellular level), interactions of cells with each other (population level) and interactions of cells with other molecular species (extracellular level). The resulting mathematical models represent high dimensional partial integro-differential equations which are rarely solvable analytically. As alternative to classical numerical solution methods which are not well suited for efficient solution, we propose a hybrid simulation algorithm in this contribution. This technique is based on a combination of the method of partial characteristics and approximate moment methods using monomial cubatures. The technique's efficiency and accuracy is shown for a multicellular system describing cell-to-cell signaling in osteochondroswitch models.

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Homogenizing bacterial cell factories: Analysis and engineering of phenotypic heterogeneity

Cited by 95 publications

References 123 publications

Identification of cell‐to‐cell heterogeneity through systems engineering approaches

Identification of cell‐to‐cell heterogeneity through systems engineering approaches

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Hybrid simulation algorithm for efficient numerical solution of population balance equations

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