Biology subjects

Waldherr, S.

Publications and source records attributed to Waldherr, S..

4 recordsLinked to original sources

bioRxiv2025

Phage-mediated just-in-time circuit amplification and delivery for recombinant expression of toxic proteins

In order to address outstanding challenges in bacterial bioreactor bioprocessing, we have developed an approach that alleviates the need for chemical inducers and selective agents (such as antibiotics), and mitigates leaky expression of the production circuit. More specifically, temperate bacteriophage Lambda ({lambda}) was engineered as a chassis to just-in-time amplify and deliver a production circuit to a wild-type Escherichia coli population. Since the expression of the production pathway is engineered to be dependent on {lambda}s central regulatory switch towards the lysogenic state, {lambda} will first lytically amplify in the background without significantly altering overall bioreactor growth dynamics or gene expression. Only when {lambda} eventually starts outnumbering the host population, its natural switch to lysogeny will actively install and trigger the production circuit. As such, the {lambda} chassis serves as an intrinsic self-amplifying inducer that meanwhile rules out any leaky expression. The impact of the initial phage-to-host ratio on population dynamics and production efficiency was assessed experimentally and through ODE modeling, revealing that it can be used to fine-tune the trade-off between gene dosage and biomass conversion into virions. We demonstrate that this leakproof approach is particularly promising for the expression of toxic proteins such as Benzonase, a highly potent DNase and RNase.

synthetic biology↗

bioRxiv2024

Machine learning and data-driven inverse modeling of metabolomics unveil key process of active aging

Physical inactivity and a weak fitness status have become a global health concern. Metabolomics, as an integrative systematic approach, might link to individuals fitness at the molecular level. In this study, we performed blood samples metabolomics analysis of a cohort of elderly people with different treatments. By defining two groups of fitness and corresponding metabolites profiles, we tested several machine learning classification approaches to identify key metabolite biomarkers, which showed robustly aspartate as a dominant negative marker of fitness. Following, the metabolomics data of the two groups were analyzed by a novel approach for metabolic network interaction termed COVRECON. Where we identified the enzyme AST as the most important metabolic regulation between the fit and the less fit groups. Routine blood tests in these two cohorts validated significant differences in AST and ALT. In summary, we combine machine learning classification and COVRECON to identify metabolomics biomarkers and causal processes for fitness of elderly people.

systems biology↗

bioRxiv2023

Enzyme fluctuations data improve inference of metabolic interaction networks with an inverse differential Jacobian approach

The development of next-generation sequencing and single-cell technology has generated vast genome-scale multi-omics datasets. Dedicated mathematical algorithms are required to dissect intricate molecular causality within metabolic networks using these datasets. Based on the network analysis, recent research has introduced the inverse differential Jacobian algorithm, which combines metabolic interaction network construction and covariance matrix analysis of genome-scale metabolomics data to elucidate system regulatory factors near steady-state dynamics. Traditionally, these studies assumed metabolomics variations solely resulted from metabolic system fluctuations, acting independently on each metabolite. However, emerging evidence highlights the role of internal network fluctuations, particularly from the gene expression fluctuations, leading to correlated perturbations on metabolites. In this article, we propose a novel approach that exploits these correlations to reconstruct relevant metabolic interactions. Thereby, enzymes exhibiting significant variances in activity values serve as indicators of large fluctuations in their catalyzed reactions. By integrating this information in an inverse Jacobian algorithm, we are able to exploit the underlying reaction network structure to improve the construction of the fluctuation matrix required in the inverse Jacobian algorithm. After a comprehensive assessment of three critical factors affecting the algorithms accuracy, we conclude that using the enzyme fluctuation data significantly enhances the inverse Jacobian algorithms performance. We applied this approach to a breast cancer dataset with two different cell lines, which highlighted metabolic interactions where fluctuations in enzyme gene expression yield a relevant difference between the cell lines.

bioinformatics↗

bioRxiv2020

Cell size statistics in cell lineages and population snapshots with different growth regimes and division strategies.

Growing populations of bacteria control their growth and division reaching narrow distributions of cell-sizes. In this paper we explored how different combinations of growth regimes and division mechanisms lead to different cell-size statistics in these populations. Deterministic and stochastic modeling were used to describe the size distribution of a population of cells that is observed from two different perspectives: as single cell lineages, i.e. random paths in the lineage tree, or as snapshots, at given times, of a population in which all descendants of a single ancestor cell are observed. Our time-dependent approaches allowed us to obtain both the transient dynamics and the steady state values for the main statistical moments of the cell-size distribution. Also, we established mathematical relationships among the statistics in the two considered perspectives, thus improving our knowledge of how cells control their growth and proliferation.

systems biology↗