Model-driven insights into the effects of temperature on metabolism

Philipp Wendering; Zoran Nikoloski

doi:10.1016/j.biotechadv.2023.108203

Model-driven insights into the effects of temperature on metabolism

Biotechnol Adv. 2023 Oct:67:108203. doi: 10.1016/j.biotechadv.2023.108203. Epub 2023 Jun 20.

Authors

Philipp Wendering¹, Zoran Nikoloski²

Affiliations

¹ Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.
² Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany. Electronic address: nikoloski@mpimp-golm.mpg.de.

PMID: 37348662
DOI: 10.1016/j.biotechadv.2023.108203

Abstract

Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.

Keywords: Data integration; Metabolic model; Temperature-dependent modelling; Thermodynamics.

Publication types

Systematic Review
Review
Research Support, Non-U.S. Gov't

MeSH terms

Metabolic Networks and Pathways* / genetics
Models, Biological*
Phenotype
Temperature