Biology subjects

Steuer, R.

Publications and source records attributed to Steuer, R..

8 recordsLinked to original sources

bioRxiv2026

Glycogen removal enhances ethylene production in Synechocystis sp. PCC 6803 but exposes a 2-oxoglutarate-GS-GOGAT-phosphate constraint

Cyanobacteria are promising platforms for converting CO2 directly into value-added chemicals. However, redirecting fixed carbon toward a product requires understanding of how cells balance the energetic and redox supplies and demands - and how efficiently metabolic flux can be channelled into product formation rather than being dissipated or excreted. Glycogen, synthesized via ADP-glucose pyrophosphorylase (GlgC), is the major native carbon and energy sink, and its removal forces cells to redirect excess carbon and reducing power into alternative pathways. As a target product we used ethylene, a major industrial building block currently derived almost entirely from fossil feedstocks, which can be produced photoautotrophically by the ethylene-forming enzyme (EFE) at the expense of 2-oxoglutarate (2-OG), a central node linking carbon and nitrogen metabolism. Here, we compared wild-type Synechocystis sp. PCC 6803 with strains carrying either a complete ({Delta}glgC) or attenuated, light-responsive (LGS; psbA2-driven glgC) glycogen synthesis block, with an EFE-expressing strain (EFE), and with the combined {Delta}glgC+EFE strain. All strains were cultivated across a light gradient (35-300 mol photons m-2 s-1) under nitrogen-replete conditions. Integrating measurements of growth and ethylene production rates, chlorophyll-fluorescence analysis, quantitative proteomics, FTIR profiling, and overflow-metabolite analysis revealed that partial suppression of glycogen synthesis (LGS) enhanced growth under low irradiance but became detrimental above a critical light threshold. EFE expression in the {Delta}glgC background increased ethylene production in a light-dependent manner but further compromised growth. Both strains with EFE increased photosynthetic electron transport consistent with the carbon-sink effect, yet this electron surplus was not channelled proportionally into product or biomass. Instead, continuous 2-OG drain by EFE constrained flux through the GS-GOGAT node and induced a secondary inorganic phosphate limitation in {Delta}glgC+EFE that activated photoprotection and limited productivity. These results point to the 2-OG/GS-GOGAT/Pi axis as a key design constraint determining whether the surplus photosynthetic flux gets converted into product, dissipated through photoprotection, or released as overflow metabolites.

plant biology↗

bioRxiv2025

Contextualizing Models: Deriving a Kinetic Model of Cancer Metabolism including Growth via Stoichiometric Reduction

Genome-scale metabolic models (GEMs) offer unprecedented possibilities to study human metabolism, including alterations in cancers. Yet, analyses of GEMs still entail several disadvantages. In particular, constraint-based methods, such as flux balance analysis, are typically restricted to analyse steady-state flux distributions. In contrast, kinetic models based on ordinary differential equations allow assessment of regulatory properties and dynamics. Building kinetic models, however, is still hampered by the lack of knowledge about kinetic parameters and is typically focused on individual pathways. Here, we present an approach to derive kinetic models of metabolism augmented by coarse-grained overall reactions that represent the remaining cellular metabolism and biosynthetic processes. Using algorithmic network reduction, we derive coarse-grained reactions that preserve the correct stoichiometry of precursors, energy, and redox equivalents required for cellular growth. Analysis of the GEM-embedded kinetic model uses Monte Carlo sampling to address parameter uncertainty. We exemplify our approach by constructing a kinetic model of cancer metabolism that includes an explicit description of cellular growth. We show that the GEM-embedded kinetic model differs in its control properties from the corresponding model without growth, with implications for understanding regulatory hotspots and drug target identification.

systems biology↗

bioRxiv2024

A quantitative description of light-limited cyanobacterial growth using flux balance analysis

The metabolism of phototrophic cyanobacterial is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of the genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its analysis using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of quantitative experimental analyses, we develop a novel approach to describe light absorption and light utilization. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable to predict quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototropic microorganisms.

systems biology↗

bioRxiv2022

Time-Optimal Adaptation in Metabolic Network Models

Analysis of metabolic models using constraint-based optimization has emerged as an important computational technique to elucidate and eventually predict cellular metabolism and growth. In this work, we introduce time-optimal adaptation (TOA), a new constraint-based modeling approach that allows us to evaluate the fastest possible adaptation to a pre-defined cellular state while fulfilling a given set of dynamic and static constraints. TOA falls into the mathematical problem class of time-optimal control problems, and, in its general form, can be applied in a broad sense and thereby extends most existing constraint-based modeling frameworks. Specifically, We introduce a general mathematical framework that captures many existing constraint-based methods and define TOA within this framework. We then exemplify TOA using a coarse-grained self-replicator model and demonstrate that TOA allows us to explain several well known experimental phenomena that are difficult to explore using existing constraint-based analysis methods. We show that TOA can explain accumulation of storage compounds in constant environments, as well as overshoot uptake metabolism after a period of nutrient scarcity. TOA reveals that organisms with internal temporal degrees of freedom, such as storage, can in most environments outperform organisms with a static intracellular composition. Furthermore, TOA shows that organisms adapted to better growth conditions than present in the environment ("optimists") typically outperform organisms adapted to poorer growth conditions ("pessimists").

systems biology↗

bioRxiv2019

Modeling microbial communities using biochemical resource allocation analysis

To understand the functioning and dynamics of microbial communities remains a fundamental challenge at the forefront of current biology. To tackle this challenge, the construction of computational models of interacting microbes is an indispensable tool. Currently, however, there is a large chasm between ecologically-motivated descriptions of microbial growth used in ecosystems simulations, and detailed metabolic pathway and genome-based descriptions developed within systems and synthetic biology. Here, we seek to demonstrate how current biochemical resource allocation models of microbial growth offer the potential to advance ecosystem simulations and their parameterization. In particular, recent work on quantitative microbial growth and cellular resource allocation allow us to formulate mechanistic models of microbial growth that are physiologically meaningful while remaining computationally tractable. Biochemical resource allocation models go beyond Michaelis-Menten and Monod-type growth models, and allow to account for emergent properties that underlie the remarkable plasticity of microbial growth. We exemplify our approach using a coarse-grained model of cyanobacterial phototrophic growth, and demonstrate how the model allows us to represent physiological acclimation to different environments, co-limitation of growth by several nutrients, as well as emergent switches between alternative nutrient sources. Our approach has implications for building models of microbial communities to understand their interactions, dynamics and response to environmental changes.

systems biology↗

bioRxiv2018

Quantitative insights into the cyanobacterial cell economy

Phototrophic microorganisms are promising resources for green biotechnology. Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic growth is still insufficiently understood. We provide a quantitative analysis of light-limited, light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were quantified to monitor proteome allocation as a function of growth rate. Among other physiological adaptations, we identify an upregulation of the translational machinery and downregulation of light harvesting components with increasing light intensity and growth rate. The resulting growth laws are discussed in the context of a coarse-grained model of phototrophic growth and available data obtained by a comprehensive literature search. Our insights into quantitative aspects of cyanobacterial adaptations to different growth rates have implications to understand and optimize photosynthetic productivity.

microbiology↗

bioRxiv2017

A minimal model of protein allocation during phototrophic growth

Photoautotrophic growth depends upon an optimal allocation of finite cellular resources to diverse intracellular processes. Commitment of a certain mass fraction of the proteome to a specific cellular function, typically reduces the proteome available for other cellular functions. Here, we develop a minimal semi-quantitative kinetic model of cyanobacterial phototrophic growth to describe such trade-offs of cellular protein allocation. The model is based on coarse-grained descriptions of key cellular processes, in particular carbon uptake, metabolism, photosynthesis, and protein translation. The model is parametrized using literature data and experimentally obtained growth curves. Of particular interest are the resulting cyanobacterial growth laws as fundamental characteristics of cellular growth. We show that the model gives rise to similar growth laws as observed for heterotrophic organisms, with several important differences due to the distinction between light energy and carbon uptake. We discuss recent experimental data supporting the model results and show that minimal growth models have implications for our understanding of the limits of phototrophic growth and bridge a gap between molecular physiology and ecology.

systems biology↗

bioRxiv2017

Modules Of Co-Occurrence In The Cyanobacterial Pan-Genome

The increasing availability of fully sequenced cyanobacterial genomes opens unprecedented opportunities to investigate the manifold adaptations and functional relationships that determine the genetic content of individual bacterial species. Here, we use comparative genome analysis to investigate the cyanobacterial pan-genome based on 77 strains whose complete genome sequence is available. Our focus is the co-occurrence of likely ortholog genes, denoted as CLOGs. We conjecture that co-occurrence CLOGs is indicative of functional relationships between the respective genes. Going beyond the analysis of pair-wise co-occurrences, we introduce a novel network approach to identify modules of co-occurring ortholog genes. Our results demonstrate that these modules exhibit a high degree of functional coherence and reveal known as well as previously unknown functional relationships. We argue that the high functional coherence observed for the extracted modules is a consequence of the similar-yet-diverse nature of the cyanobacterial phylum. We provide a simple toolbox that facilitates further analysis of our results with respect to specific cyanobacterial genes of interest.

bioinformatics↗