Biology subjects

Bockmayr, A.

Publications and source records attributed to Bockmayr, A..

6 recordsLinked to original sources

bioRxiv2024

Low-degree decompositions of flux vectors in faces of the flux cone

Decomposing a flux vector into elementary flux modes (EFMs) is a well-known task in the constraint-based analysis of metabolic networks. Using geometric insights into the facial structure of the flux cone, we develop a new method for decomposition. To illustrate our approach, we consider a variety of genome-scale metabolic networks from the BiGG database. We decompose the optimal solutions to flux balance analysis problems into EFMs and compare different possible decompositions. Taking into account their degree allows understanding the interplay of the different EFMs that can participate in the decomposition of a given flux vector and highlights the particular importance of low-degree decompositions. The new method can be applied to genome-scale metabolic networks, where the whole set of EFMs is often too large to be computed in practice. The special properties of low-degree decompositions make them an interesting subject for future biological studies.

bioinformatics↗

bioRxiv2022

On the Geometry of Elementary Flux Modes

Elementary flux modes (EFMs) play a prominent role in the constraint-based analysis of metabolic networks. They correspond to minimal functional units of the metabolic network at steady-state and as such have been studied for almost 30 years. The set of all EFMs in a metabolic network tends to be very large and may have exponential size in the number of reactions. Hence, there is a need to elucidate the structure of this set. Here we focus on geometric properties of EFMs. We analyze the distribution of EFMs in the face lattice of the steady-state flux cone of the metabolic network and show that EFMs in the relative interior of the cone occur only in very special cases. As a measure of complexity, we introduce the concept of the degree of an EFM, which is the dimension of the inclusionwise minimal face containing it. Geometric analysis can help to better understand the structure of the set of EFMs, which is important from both the mathematical and the biological viewpoint.

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

Regulatory dynamic enzyme-cost flux balance analysis: A unifying framework for constraint-based modeling

Integrated modeling of metabolism and gene regulation continues to be a major challenge in computational biology. While there exist approaches like regulatory flux balance analysis (rFBA), dynamic flux balance analysis (dFBA), resource balance analysis (RBA) or dynamic enzyme-cost flux balance analysis (deFBA) extending classical flux balance analysis (FBA) in various directions, there have been no constraint-based methods so far that allow predicting the dynamics of metabolism taking into account both macromolecule production costs and regulatory events. In this paper, we introduce a new constraint-based modeling framework named regulatory dynamic enzyme-cost flux balance analysis (r-deFBA), which unifies dynamic modeling of metabolism, cellular resource allocation and transcriptional regulation in a hybrid discrete-continuous setting. With r-deFBA, we can predict discrete regulatory states together with the continuous dynamics of reaction fluxes, external substrates, enzymes, and regulatory proteins needed to achieve a cellular objective such as maximizing biomass over a time interval. The dynamic optimization problem underlying r-deFBA can be reformulated as a mixed-integer linear optimization problem, for which there exist efficient solvers.

systems biology↗

bioRxiv2019

Finding MEMo: Minimum sets of elementary flux modes

Metabolic network reconstructions are widely used in computational systems biology for in silico studies of cellular metabolism. A common approach to analyse these models are elementary flux modes (EFMs), which correspond to minimal functional units in the network. Already for medium-sized networks, it is often impossible to compute the set of all EFMs, due to their huge number. From a practical point of view, this might also not be necessary because a subset of EFMs may already be sufficient to answer relevant biological questions. In this article, we study MEMos or minimum sets of EFMs that can generate all possible steady-state behaviours of a metabolic network. The number of EFMs in a MEMo may be by several orders of magnitude smaller than the total number of EFMs. Using MEMos, we can compute generating sets of EFMs in metabolic networks where the whole set of EFMs is too large to be enumerated.

systems biology↗

bioRxiv2019

Formalizing metabolic-regulatory networks by hybrid automata

Computational approaches in systems biology have become a powerful tool for understanding the fundamental mechanisms of cellular metabolism and regulation. However, the interplay between the regulatory and the metabolic system is still poorly understood. In particular, there is a need for formal mathematical frameworks that allow analyzing metabolism together with dynamic enzyme resources and regulatory events. Here, we introduce a metabolic-regulatory network model (MRN) that allows integrating metabolism with transcriptional regulation, macromolecule production and enzyme resources. Using this model, we show that the dynamic interplay between these different cellular processes can be formalized by a hybrid automaton, combining continuous dynamics and discrete control.

systems biology↗