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  1. Article ; Online: Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae.

    Persson, Sebastian / Shashkova, Sviatlana / Österberg, Linnea / Cvijovic, Marija

    FEMS yeast research

    2022  Volume 22, Issue 1

    Abstract: Saccharomyces cerevisiae has a sophisticated signalling system that plays a crucial role in cellular adaptation to changing environments. The SNF1 pathway regulates energy homeostasis upon glucose derepression; hence, it plays an important role in ... ...

    Abstract Saccharomyces cerevisiae has a sophisticated signalling system that plays a crucial role in cellular adaptation to changing environments. The SNF1 pathway regulates energy homeostasis upon glucose derepression; hence, it plays an important role in various processes, such as metabolism, cell cycle and autophagy. To unravel its behaviour, SNF1 signalling has been extensively studied. However, the pathway components are strongly interconnected and inconstant; therefore, elucidating its dynamic behaviour based on experimental data only is challenging. To tackle this complexity, systems biology approaches have been successfully employed. This review summarizes the progress, advantages and disadvantages of the available mathematical modelling frameworks covering Boolean, dynamic kinetic, single-cell models, which have been used to study processes and phenomena ranging from crosstalks to sources of cell-to-cell variability in the context of SNF1 signalling. Based on the lessons from existing models, we further discuss how to develop a consensus dynamic mechanistic model of the entire SNF1 pathway that can provide novel insights into the dynamics of nutrient signalling.
    MeSH term(s) Glucose/metabolism ; Protein Serine-Threonine Kinases ; Saccharomyces cerevisiae/genetics ; Saccharomyces cerevisiae/metabolism ; Saccharomyces cerevisiae Proteins/genetics ; Saccharomyces cerevisiae Proteins/metabolism ; Signal Transduction
    Chemical Substances Saccharomyces cerevisiae Proteins ; Protein Serine-Threonine Kinases (EC 2.7.11.1) ; Glucose (IY9XDZ35W2)
    Language English
    Publishing date 2022-03-03
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2036775-2
    ISSN 1567-1364 ; 1567-1356
    ISSN (online) 1567-1364
    ISSN 1567-1356
    DOI 10.1093/femsyr/foac012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 5454: Show issues Location:
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  2. Article: Fine-Tuning of Energy Levels Regulates

    Persson, Sebastian / Welkenhuysen, Niek / Shashkova, Sviatlana / Cvijovic, Marija

    Frontiers in physiology

    2020  Volume 11, Page(s) 954

    Abstract: Nutrient sensing pathways are playing an important role in cellular response to different energy levels. In budding yeast, ...

    Abstract Nutrient sensing pathways are playing an important role in cellular response to different energy levels. In budding yeast,
    Language English
    Publishing date 2020-08-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2564217-0
    ISSN 1664-042X
    ISSN 1664-042X
    DOI 10.3389/fphys.2020.00954
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Scalable and flexible inference framework for stochastic dynamic single-cell models.

    Persson, Sebastian / Welkenhuysen, Niek / Shashkova, Sviatlana / Wiqvist, Samuel / Reith, Patrick / Schmidt, Gregor W / Picchini, Umberto / Cvijovic, Marija

    PLoS computational biology

    2022  Volume 18, Issue 5, Page(s) e1010082

    Abstract: Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a ...

    Abstract Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability.
    MeSH term(s) Bayes Theorem ; Cell Physiological Phenomena ; Models, Biological ; Saccharomyces cerevisiae ; Stochastic Processes ; Systems Biology/methods
    Language English
    Publishing date 2022-05-19
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010082
    Database MEDical Literature Analysis and Retrieval System OnLINE

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