Biologie des systèmes

Résumé

La biologie des systèmes (ou biologie intégrative) est un domaine récent de la biologie qui étudie les organismes vivants comme les systèmes qu'ils sont en réalité, par opposition aux approches historiques qui tendent à décomposer l'étude à tous les niveaux, en biologie, physiologie, biochimie… La biologie systémique cherche à intégrer différents niveaux d'informations pour comprendre comment fonctionne réellement un système biologique. En étudiant les relations et les interactions entre différentes parties du système biologique (organites, cellules, systèmes physiologiques, réseaux de gènes et de protéines permettant la communication des cellules), le chercheur tente de former un modèle de fonctionnement de la totalité du système. Si la biologie des systèmes est un domaine théorisé à la fin du , plusieurs biologistes et chimistes ont travaillé dans ce sens dès la fin du (par exemple le biologiste et chimiste français Marcellin Berthelot qui privilégiait une approche systémique et s

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, ,

Many applications of systems biology such as the simulation of isotope labeling experiments and pathway inference in metabolic engineering rely on atom-level representation of metabolism. Because of its challenging complexity and the difficulty of obtaining correct atom maps of biochemical reactions, the current understanding of the atomic level of metabolism cannot yet fully explain the outcome of stable-isotope labeling experiments. To our knowledge, iAM.NICE is the only computational tool for automatic mapping of single atoms in metabolic reactions, pathways and networks, that ensures the correctness of the mapping based on biochemical reaction mechanisms. iAM.NICE is an extension of BNICE.ch, which reconstructs biochemical reactions using expert curated, generalized biochemical reaction rules. However, inferring differential flux profiles from different atom-mapped metabolic pathways remains a challenge. In this study, we apply iAM.NICE to the core metabolism of E. coli to understand (i) the impact of different physiological states on the distribution of atom labels in the compounds of the central carbon metabolism, and (ii) the consecutive relation of labeled biomass precursor metabolites to the possible labeling distributions in the biomass building blocks (BBB). Our results show that different physiological states of a cell give rise to different distributions of carbon atoms in BBB precursor metabolites, information that is not only crucial for the design and the analysis of isotope labeling experiments, but also deepens our understanding of metabolism at the atomic level. Finally, our method can be easily extended to study in silico the conversion of elements other than carbon, and it can be applied to study organisms other than E. coli. The exhaustive results of our study can be consulted on our website (http://lcsb-databases.epfl.ch/pathways/LabelingList ). Access is freely available for academic purpose upon subscription.

2018

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Modelling the Intracellular Environment under Uncertainty, from Post-translational Modification to Metabolic Networks

Robin Alexander Denhardt-Eriksson

Modelling and analysis of biological systems is crucial in order to quantitatively explain and predict their behaviour. The importance of modelling in systems biology becomes even more evident when tackling complex, large systems. Approaches that rely on modelling can be used to tackle a range of problems, such as cellular signalling or using genetically modified bacteria to synthesize human proteins for medical purposes. These phenomena range across different spatiotemporal scales, whether it is predicting the decay of red blood cells for blood transfusion or finding engineering targets in order to increase the productivity of bioprocesses. Reconciling models with experimental data and quantifying how uncertainty propagates through these models is crucial to exploiting them to their full potential. Uncertainty impacts not only the calibration and validation of a model, but also the predictions extracted from it. Developing and implementing uncertainty quantification methods into these models is a key step in exploring their behaviour and accelerating the adoption of such methods in metabolic engineering. In this thesis I firstly propose to implement and explore uncertainty quantification methods on control coefficients obtained by Metabolic Control Analysis (MCA) of Genome Scale Metabolic Models (GEMs), with the goal of quantifying uncertainty propagation from model parameters to control coefficients. I additionally want to build Single Protein Models that describe the effects of Post Translational Modifications (PTMs). The nature of the experimental data available to calibrate these models makes uncertainty an inherent property, and therefore Global Sensitivty Analysis (GSA) techniques are well suited to describing the links between parameter uncertainty and model behaviour.

EPFL2021

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Principles of Systems Biology, No. 11

Noushin Hadadi, Jasmin Maria Hafner, Vassily Hatzimanikatis, Laurent Potvin-Trottier

This month: AI that learns patterns and facts, new protein-RNA and protein-protein relationships, engineering signaling and metabolism, and more variants of Cas9.

Cell Press2016

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