Improving the Reliability of RNA-seq: Approaching Single-Cell Transcriptomics To Explore Individuality in Bacteria

Bossert, Martin; Kracht, David; Scherer, Stephen W.; Landstorfer, Richard; Neuhaus, Klaus

doi:10.1007/978-3-319-54729-9_7

Cited by 1 publication

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“…Lemoine et al provided a review about tools for monitoring population heterogeneities on a single cell basis [ 66 ]. Today, even single cell transcription analysis using novel sequencing technologies is becoming achievable [ 67 ] which may further increase the quality of mathematical models.…”

Section: Data-driven Approachmentioning

confidence: 99%

In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models

Zieringer

2018

Computational and Structural Biotechnology Journal

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Industrial bioreactors range from 10.000 to 700.000 L and characteristically show different zones of substrate availabilities, dissolved gas concentrations and pH values reflecting physical, technical and economic constraints of scale-up. Microbial producers are fluctuating inside the bioreactors thereby experiencing frequently changing micro-environmental conditions. The external stimuli induce responses on microbial metabolism and on transcriptional regulation programs. Both may deteriorate the expected microbial production performance in large scale compared to expectations deduced from ideal, well-mixed lab-scale conditions. Accordingly, predictive tools are needed to quantify large-scale impacts considering bioreactor heterogeneities. The review shows that the time is right to combine simulations of microbial kinetics with calculations of large-scale environmental conditions to predict the bioreactor performance. Accordingly, basic experimental procedures and computational tools are presented to derive proper microbial models and hydrodynamic conditions, and to link both for bioreactor modeling. Particular emphasis is laid on the identification of gene regulatory networks as the implementation of such models will surely gain momentum in future studies.

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Section: Data-driven Approachmentioning

confidence: 99%

In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models

Zieringer

2018

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

show abstract

Improving the Reliability of RNA-seq: Approaching Single-Cell Transcriptomics To Explore Individuality in Bacteria

Cited by 1 publication

References 1 publication

In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models

In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models

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