Time Integrated Flux Analysis: Exploiting the Concentration Measurements Directly for Cost-Effective Metabolic Network Flux Analysis

Portela, Rui M C; Richelle, Anne; Dumas, Patrick; Stosch, Moritz von

doi:10.3390/microorganisms7120620

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…The estimation of the rates can be accomplished in two different ways, a differential or integral way. [ 41–44 ] Since the interest was in analyzing changes in the specific rates over time, the differential way was adopted here, following the best practice, [ 45,46,28 ] that is, Starting from the integrate version of the material balance (Equation (5)), the rate related terms were isolated on the right‐hand side since they cannot be measured:

c_{normalex} (t_{i}) \cdot V (t_{i}) - c_{normalex} (t_{0}) \cdot V (t_{0}) - \int_{t_{0}}^{t_{i}} u \cdot d t = \int_{t_{0}}^{t_{i}} q \cdot x \cdot V \cdot d t

Fit arbitrary time dependent functions (e.g., cubic smoothing splines, gaussian process models, polynomials or others), f ( t , w ), to approximate the measured quantities,

y_{m} false(t_{i} false) = c_{ex, m} false(t_{i} false) \cdot V_{normalm} false(t_{i} false) - c_{ex, m} false(t_{0} false) \cdot V_{normalm} false(t_{0} false) - \int_{t_{0}}^{t_{i}} u_{normalm} \cdot d t

, such that the residual ɛ was small, though the function also does not overfit the data.

y_{normalm} = f (t, w) + ε

Build the derivative of f ( t , w ) analytically with respect to time, that is,

\frac{d f false(t, w false)}{d t}

Evaluate the derivative at the time instance t i at which the concentrations have been measured (assuming that the concentrations have the lowest measurement frequency) and divide by the approximated biomass ( x m ( t ) · V m ( t ) = g ( t , ω...…”

Section: Methodsmentioning

confidence: 99%

Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

Richelle¹,

Lee²,

Portela³

et al. 2020

Biotechnology Journal

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In recent years, multivariate data analysis (MVDA) and modeling approaches have found increasing applications for upstream bioprocess studies (e.g., monitoring, development, optimization, scale-up, etc.). Many of these studies look at variations in the concentrations of metabolites and cell-based measurements. However, these measures are subject to system inherent variations (e.g., changes in metabolic activity) but also intentional operational changes. It is proposed to perform MVDA and modeling on data representative of the underlying biological system operation, that is, the specific rates, which are per se independent of the scale, operational strategy (e.g., batch, fed-batch), and biomass content. Two industrial case studies are highlighted to showcase the approach: one is HEK medium performance comparison study and the other is CHO scale-up/-down study. It is shown that analyzing processes in this way reveals insights into behavior of the underlying biological system, which cannot to the same degree be deducted from the analysis of concentrations.

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c_{normalex} (t_{i}) \cdot V (t_{i}) - c_{normalex} (t_{0}) \cdot V (t_{0}) - \int_{t_{0}}^{t_{i}} u \cdot d t = \int_{t_{0}}^{t_{i}} q \cdot x \cdot V \cdot d t

Fit arbitrary time dependent functions (e.g., cubic smoothing splines, gaussian process models, polynomials or others), f ( t , w ), to approximate the measured quantities,

y_{m} false(t_{i} false) = c_{ex, m} false(t_{i} false) \cdot V_{normalm} false(t_{i} false) - c_{ex, m} false(t_{0} false) \cdot V_{normalm} false(t_{0} false) - \int_{t_{0}}^{t_{i}} u_{normalm} \cdot d t

, such that the residual ɛ was small, though the function also does not overfit the data.

y_{normalm} = f (t, w) + ε

Build the derivative of f ( t , w ) analytically with respect to time, that is,

\frac{d f false(t, w false)}{d t}

Section: Methodsmentioning

confidence: 99%

Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

Richelle¹,

Lee²,

Portela³

et al. 2020

Biotechnology Journal

View full text Add to dashboard Cite

show abstract

Time Integrated Flux Analysis: Exploiting the Concentration Measurements Directly for Cost-Effective Metabolic Network Flux Analysis

Cited by 1 publication

References 23 publications

Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

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