Biology subjects

Weilandt, D.

Publications and source records attributed to Weilandt, D..

3 recordsLinked to original sources

bioRxiv2022

Proteome capacity constraints favor respiratory ATP generation

Cells face competing metabolic demands. These include efficient use of both limited substrates and limited proteome capacity, as well as flexibility to deal with different environments. Flexibility requires spare enzyme capacity, which is proteome inefficient. ATP generation can occur via fermentation or respiration. Fermentation is much less substrate-efficient, but often assumed to be more proteome efficient 1-3, thereby favoring fast-growing cells engaging in aerobic glycolysis 4-8. Here, however, we show that mitochondrial respiration is actually more proteome-efficient than aerobic glycolysis. Instead, aerobic glycolysis arises from cells maintaining the flexibility to grow also anaerobically. These conclusions emerged from an unbiased assessment of metabolic regulatory mechanisms, integrating quantitative metabolomics, proteomics, and fluxomics, of two budding yeasts, Saccharomyces cerevisiae and Issatchenkia orientalis, the former more fermentative and the latter respiratory. Their energy pathway usage is largely explained by differences in proteome allocation. Each organisms proteome allocation is remarkably stable across environmental conditions, with metabolic fluxes predominantly regulated at the level of metabolite concentrations. This leaves extensive spare biosynthetic capacity during slow growth and spare capacity of their preferred bioenergetic machinery when it is not essential. The greater proteome-efficiency of respiration is also observed in mammals, with aerobic glycolysis occurring in yeast or mammalian cells that maintain a fermentation-capable proteome conducive to both aerobic and anaerobic growth.

systems biology↗

bioRxiv2022

Optimal enzyme utilization suggests concentrations and thermodynamics favor condition-specific saturations and binding mechanisms

Understanding the dynamic responses of living cells upon genetic and environmental perturbations is crucial to decipher the metabolic functions of organisms. The rates of enzymatic reactions and their evolution are key to this understanding, as metabolic fluxes are limited by enzymatic activity. In this work, we investigate the optimal modes of operations for enzymes with regard that the evolutionary pressure drives enzyme kinetics toward increased catalytic efficiency. We use an efficient mixed-integer formulation to decipher the principles of optimal catalytic properties at various operating points. Our framework allows assessing the distribution of the thermodynamic forces and enzyme states, providing detailed insight into the mode of operation. Our results confirm earlier theoretical studies on the optimal kinetic design using a reversible Michaelis-Menten mechanism. The results further explored the optimal modes of operation for random-ordered multi-substrate mechanisms. We show that optimal enzyme utilization is achieved by unique or alternative modes of operations depending on the reactants concentrations. Our novel formulation allows investigating the optimal catalytic properties of all enzyme mechanisms with known elementary reactions. We propose that our novel framework provides the means to guide and evaluate directed evolution studies and estimate the limits of the direct evolution of enzymes.

systems biology↗

bioRxiv2022

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Kinetic models of metabolic networks relate metabolic fluxes, metabolite concentrations, and enzyme levels through well-defined mechanistic relations rendering them an essential tool for systems biology studies aiming to capture and understand the behavior of living organisms. However, due to the lack of information about the kinetic properties of enzymes and the uncertainties associated with available experimental data, traditional kinetic modeling approaches often yield only a few or no kinetic models with desirable dynamical properties making the computational analysis unreliable and computationally inefficient. We present REKINDLE (REconstruction of KINetic models using Deep LEarning), a deep-learning-based framework for efficiently generating large-scale kinetic models with dynamic properties matching the ones observed in living organisms. We showcase REKINDLEs efficiency and capabilities through three studies where we: (i) generate large populations of kinetic models that allow reliable in silico testing of hypotheses and systems biology designs, (ii) navigate the phenotypic space by leveraging the transfer learning capability of generative adversarial networks, demonstrating that the generators trained for one physiology can be fine-tuned for another physiology using a low amount of data, and (iii) expand upon existing datasets, making them amenable to thorough computational biology and data-science analyses. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate novel kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in health, biotechnology, and systems and synthetic biology. REKINDLE is available as an open-access tool.

systems biology↗