IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2169-d844797.html
   My bibliography  Save this article

Thermodynamic Modelling of Transcriptional Control: A Sensitivity Analysis

Author

Listed:
  • Manuel Cambón

    (Applied Mathematics Department, University of Granada, E18071 Granada, Spain
    These authors contributed equally to this work.)

  • Óscar Sánchez

    (Applied Mathematics Department, University of Granada, E18071 Granada, Spain
    These authors contributed equally to this work.)

Abstract

Modelling is a tool used to decipher the biochemical mechanisms involved in transcriptional control. Experimental evidence in genetics is usually supported by theoretical models in order to evaluate the effects of all the possible interactions that can occur in these complicated processes. Models derived from the thermodynamic method are critical in this labour because they are able to take into account multiple mechanisms operating simultaneously at the molecular micro-scale and relate them to transcriptional initiation at the tissular macro-scale. This work is devoted to adapting computational techniques to this context in order to theoretically evaluate the role played by several biochemical mechanisms. The interest of this theoretical analysis relies on the fact that it can be contrasted against those biological experiments where the response to perturbations in the transcriptional machinery environment is evaluated in terms of genetically activated/repressed regions. The theoretical reproduction of these experiments leads to a sensitivity analysis whose results are expressed in terms of the elasticity of a threshold function determining those activated/repressed regions. The study of this elasticity function in thermodynamic models already proposed in the literature reveals that certain modelling approaches can alter the balance between the biochemical mechanisms considered, and this can cause false/misleading outcomes. The reevaluation of classical thermodynamic models gives us a more accurate and complete picture of the interactions involved in gene regulation and transcriptional control, which enables more specific predictions. This sensitivity approach provides a definite advantage in the interpretation of a wide range of genetic experimental results.

Suggested Citation

  • Manuel Cambón & Óscar Sánchez, 2022. "Thermodynamic Modelling of Transcriptional Control: A Sensitivity Analysis," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2169-:d:844797
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2169/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/13/2169/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jason Gertz & Eric D. Siggia & Barak A. Cohen, 2009. "Analysis of combinatorial cis-regulation in synthetic and genomic promoters," Nature, Nature, vol. 457(7226), pages 215-218, January.
    2. Eran Segal & Tali Raveh-Sadka & Mark Schroeder & Ulrich Unnerstall & Ulrike Gaul, 2008. "Predicting expression patterns from regulatory sequence in Drosophila segmentation," Nature, Nature, vol. 451(7178), pages 535-540, January.
    3. Till D Frank & Aimée M Carmody & Boris N Kholodenko, 2012. "Versatility of Cooperative Transcriptional Activation: A Thermodynamical Modeling Analysis for Greater-Than-Additive and Less-Than-Additive Effects," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-15, April.
    4. George von Dassow & Eli Meir & Edwin M. Munro & Garrett M. Odell, 2000. "The segment polarity network is a robust developmental module," Nature, Nature, vol. 406(6792), pages 188-192, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Farzaneh Khajouei & Saurabh Sinha, 2018. "An information theoretic treatment of sequence-to-expression modeling," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-24, September.
    2. Adel Dayarian & Madalena Chaves & Eduardo D Sontag & Anirvan M Sengupta, 2009. "Shape, Size, and Robustness: Feasible Regions in the Parameter Space of Biochemical Networks," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-12, January.
    3. Muir Morrison & Manuel Razo-Mejia & Rob Phillips, 2021. "Reconciling kinetic and thermodynamic models of bacterial transcription," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-30, January.
    4. Cummings, F.W, 2004. "A model of morphogenesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 339(3), pages 531-547.
    5. Ryan N Gutenkunst & Joshua J Waterfall & Fergal P Casey & Kevin S Brown & Christopher R Myers & James P Sethna, 2007. "Universally Sloppy Parameter Sensitivities in Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-8, October.
    6. Zeina Shreif & Vipul Periwal, 2014. "A Network Characteristic That Correlates Environmental and Genetic Robustness," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-23, February.
    7. Andreas Wagner, 2015. "Causal Drift, Robust Signaling, and Complex Disease," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-29, March.
    8. Debasish Mondal & Edward Dougherty & Abhishek Mukhopadhyay & Adria Carbo & Guang Yao & Jianhua Xing, 2014. "Systematic Reverse Engineering of Network Topologies: A Case Study of Resettable Bistable Cellular Responses," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
    9. Kroll, K.M. & Ferrantini, A. & Domany, E., 2010. "Introduction to biology and chromosomal instabilities in cancer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(20), pages 4374-4388.
    10. Haiqing Xu & Chuan Li & Chuan Xu & Jianzhi Zhang, 2023. "Chance promoter activities illuminate the origins of eukaryotic intergenic transcriptions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    11. Guillermo Rodrigo & Santiago F Elena, 2011. "Structural Discrimination of Robustness in Transcriptional Feedforward Loops for Pattern Formation," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
    12. Miles Miller & Marc Hafner & Eduardo Sontag & Noah Davidsohn & Sairam Subramanian & Priscilla E M Purnick & Douglas Lauffenburger & Ron Weiss, 2012. "Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-18, July.
    13. Stefano Ciliberti & Olivier C Martin & Andreas Wagner, 2007. "Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology," PLOS Computational Biology, Public Library of Science, vol. 3(2), pages 1-10, February.
    14. Carl Song & Hilary Phenix & Vida Abedi & Matthew Scott & Brian P Ingalls & Mads Kærn & Theodore J Perkins, 2010. "Estimating the Stochastic Bifurcation Structure of Cellular Networks," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-11, March.
    15. Xuejing Li & Casandra Panea & Chris H Wiggins & Valerie Reinke & Christina Leslie, 2010. "Learning “graph-mer” Motifs that Predict Gene Expression Trajectories in Development," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-13, April.
    16. Amir Shahein & Maria López-Malo & Ivan Istomin & Evan J. Olson & Shiyu Cheng & Sebastian J. Maerkl, 2022. "Systematic analysis of low-affinity transcription factor binding site clusters in vitro and in vivo establishes their functional relevance," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    17. Merav Socolovsky & Michael Murrell & Ying Liu & Ramona Pop & Ermelinda Porpiglia & Andre Levchenko, 2007. "Negative Autoregulation by FAS Mediates Robust Fetal Erythropoiesis," PLOS Biology, Public Library of Science, vol. 5(10), pages 1-16, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2169-:d:844797. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.