Lars Keld Nielsen

The paper uses automated mini-bioreactors to study the temporal dynamics and multi-omics responses of CRISPRi knockdowns in Pseudomonas putida, identifying a critical time window for phenotypic impact.

This paper improves metabolic flux estimation by applying compositional data analysis to isotopic metabolic flux analysis, reducing mean squared error and narrowing confidence intervals.

The paper presents a robust LC-MS/MS workflow for high-throughput metabolic profiling of bacteria using stable isotope dilution mass spectrometry.

The paper develops enzyme-constrained metabolic models for Synechocystis sp. PCC 6803 under different light conditions, showing improved predictions of metabolism and enzyme usage compared to conventional models.

The paper uses pangenome analysis of Escherichia coli to reveal the genetic basis of metabolic reactions and their evolutionary dynamics.

This study investigates how glycan structures affect GM-CSF bioactivity by engineering glycoforms in Chinese hamster ovary cells and testing their effects on TF-1 cell proliferation.

This paper curates and analyzes 22 genomes of Cupriavidus necator to assess genomic diversity and metabolic potential for one-carbon biomanufacturing, identifying three strains with promising traits.

This paper introduces a Bayesian Independent Component Analysis method to identify co-regulated gene modules and quantify uncertainty, improving integration of multi-strain and multi-omics data.

The paper evaluates the efficiency of apoptotic gene overexpression in CHO cells using targeted integration to improve culture performance and productivity.

The paper presents iCHO3K, a comprehensive metabolic model of Chinese Hamster cells, and uses it to analyze metabolic changes in lactate-free cells, identifying structural insights into metabolic rewiring.

This study eliminates the Warburg Effect in mammalian cells by knocking out lactate dehydrogenase and feedback regulators, maintaining growth rate while reducing lactate production.

This study compares four DOE methods to optimize rAAV production by determining optimal plasmid and reagent ratios, finding that mixture design with face-centered central composite design performs best.

The paper reconstructs pangenomes of Lactobacillaceae to generate metabolic models for 26 species, enabling species-specific metabolic trait predictions.

The paper introduces a pseudo batch transformation method to simplify analysis of fed-batch fermentation data by mapping it to a constant volume batch process.

ASTA-P is a pipeline for detecting, quantifying, and analyzing complex alternative splicing events, showing improved performance over existing methods in simulated data and revealing novel splicing patterns in real data.

MAMBA integrates semi-quantitative metabolomic data with gene-centric omics to improve metabolic network characterization and metabolite prediction in yeast under heat stress.

This paper compares RNA-seq library preparation kits for a gas-fermenting bacterium, finding significant differences in transcriptome profiles and highlighting the importance of selecting appropriate kits for accurate results.

The paper describes an automated workflow for generating high-throughput microbial multi-omics datasets and evaluates its effectiveness with three model organisms.

This study quantifies the proteome of Clostridium autoethanogenum under different gas conditions, revealing proteome allocation priorities and enzyme dynamics.

The paper introduces multiTFA, a Python package that improves thermodynamic-based flux analysis by incorporating multivariate treatment of thermodynamic variables, reducing uncertainty in reaction Gibbs free energy ranges.

The paper introduces the reactobiome, a stratification method for the human gut microbiome, identifying five reactotypes with distinct metabolic features and validating them in a longitudinal cohort.

SmartPeak automates metabolomics data processing with algorithms for alignment, calibration, and peak integration, improving reproducibility and throughput.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.

This study benchmarks kinetic models of Escherichia coli metabolism to evaluate their performance in predicting metabolic fluxes under genetic and environmental perturbations.

The paper identifies that nearly all noncoding exons undergo alternative splicing, generating a vast repertoire of regulatory RNAs and highlighting conserved splicing patterns across species.