Biology subjects

Heckmann, D.

Publications and source records attributed to Heckmann, D..

6 recordsLinked to original sources

bioRxiv2020

Prediction of Michaelis constants from structuralfeatures using deep learning

The Michaelis constant KM describes the affinity of an enzyme for a specific substrate, and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme-substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme-substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and the domain structure of the enzyme. Model predictions can be used to estimate enzyme efficiencies, to relate metabolite concentrations to cellular physiology, and to fill gaps in the parameterization of kinetic models of cellular metabolism.

systems biology↗

bioRxiv2020

Matrix factorization recovers consistent regulatory signals from disparate datasets

The availability of gene expression data has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We also show that echoes of this structure remain in the proteome, accelerating biological discovery through multi-omics analysis. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets.

bioinformatics↗

bioRxiv2019

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they are often noisy, non-physiological, inconsistent, and labor-intensive to obtain.\n\nWe use a data-driven approach to estimate in vivo kcats using metabolic specialist E. coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo kcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of noisy and inconsistent parameterizations of cellular models.

systems biology↗

bioRxiv2018

Modeling Cellular Resource Allocation Reveals Low Phenotypic Plasticity of C4 Plants and Infers Environments of C4 Photosynthesis Evolution

- The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to what extend do they reflect ancestral environments? Here, we explore these questions for C3, C4, and C3-C4 intermediate plants of the model genus Flaveria.\n- We developed a detailed mathematical model of carbon fixation, which accounts for various environmental parameters and for energy and nitrogen partitioning across photosynthetic components. This allows us to assess environment-dependent plant physiology and performance as a function of resource allocation patterns.\n- To achieve maximal CO2 fixation rates under growth conditions differing from those experienced during their evolution, C4 species need to re-allocate significantly more nitrogen between photosynthetic components than their C3 relatives. As this is linked to a limited phenotypic plasticity, observed resource distributions in C4 plants still reflect optimality in ancestral environments, allowing their quantitative inference.\n- Our work allows us to quantify environmental effects on resource allocation and performance of photosynthetic organisms. This understanding paves the way for interpreting present photosynthetic physiology in the light of evolutionary history.

plant biology↗

bioRxiv2018

Modeling genome-wide evolution of catalytic turnover rates: Strong epistasis shaped modern enzyme kinetics

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. To address this question, we combine genome-scale metabolic modelling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. A small number of biophysically constrained enzymes suffice to induce diminishing returns epistasis that prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. In addition, multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape is smooth and causes kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data on in vitro and in vivo turnover rates, validating our modelling approach. Our analysis thus suggests that enzyme evolution can be predicted on a genome scale and reveals the mechanisms by which evolutionary forces shape modern kcats and the whole of cell metabolism.

evolutionary biology↗

bioRxiv2017

Multi-scale model of the proteomic and metabolic consequences of reactive oxygen species

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can completely inhibit cell growth when unmanaged and thus elicits an essential stress response that is universal and fundamental in biology. We develop a computable multi-scale description of the ROS stress response in Escherichia coli. We show that this quantitative framework allows for the understanding and prediction of ROS stress responses at three levels: 1) pathways: amino acid auxotrophies, 2) networks: the systemic response to ROS stress, and 3) genetic basis: adaptation to ROS stress during laboratory evolution. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

systems biology↗