Sensitivities in protein allocation models reveal distribution of metabolic capacity and flux control.

Motivation: Expanding on constraint-based metabolic models, protein allocation models (PAMs) enhance flux predictions by accounting for protein resource allocation in cellular metabolism. Yet, to this date, there are no dedicated methods for analyzing and understanding the growth-limiting factors in simulated phenotypes in PAMs.

Results: Here, we introduce a systematic framework for identifying the most sensitive enzyme concentrations (sEnz) in PAMs.

A growth selection system for sucrose synthases (SuSy): design and test.

High throughput screening (HTS) methods of enzyme variants are essential for the development of robust biocatalysts suited for low impact, industrial scale, biobased synthesis of a myriad of compounds. However, for the majority of enzyme classes, current screening methods have limited throughput, or need expensive substrates in combination with sophisticated setups. Here, we present a straightforward, high throughput selection system that couples sucrose synthase activity to growth.

Expanding Pseudomonas taiwanensis VLB120's acyl-CoA portfolio: Propionate production in mineral salt medium.

As one of the main precursors, acetyl-CoA leads to the predominant production of even-chain products. From an industrial biotechnology perspective, extending the acyl-CoA portfolio of a cell factory is vital to producing industrial relevant odd-chain alcohols, acids, ketones and polyketides. The bioproduction of odd-chain molecules can be facilitated by incorporating propionyl-CoA into the metabolic network.

Cross-Species Synthetic Promoter Library: Finding Common Ground between VLB120 and .

The potential of nonmodel organisms for industrial biotechnology is increasingly becoming evident since advances in systems and synthetic biology have made it possible to explore their unique traits. However, the lack of adequately characterized genetic elements that drive gene expression impedes benchmarking nonmodel with model organisms. Promoters are one of the genetic elements that contribute significantly to gene expression, but information about their performance in different organisms is limited.

Proteome Regulation Patterns Determine Escherichia coli Wild-Type and Mutant Phenotypes.

It is generally recognized that proteins constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. To account for the growth condition-dependent proteome in the constraint-based metabolic modeling of , we consolidated a coarse-grained protein allocation approach with the explicit consideration of enzymatic constraints on reaction fluxes.

High titer methyl ketone production with tailored Pseudomonas taiwanensis VLB120.

Methyl ketones present a group of highly reduced platform chemicals industrially produced from petroleum-derived hydrocarbons. They find applications in the fragrance, flavor, pharmacological, and agrochemical industries, and are further discussed as biodiesel blends. In recent years, intense research has been carried out to achieve sustainable production of these molecules by re-arranging the fatty acid metabolism of various microbes.

Determination of growth-coupling strategies and their underlying principles.

Background: Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts.

Genetic Optimization Algorithm for Metabolic Engineering Revisited.

To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research.