IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002157.html
   My bibliography  Save this article

Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network

Author

Listed:
  • Jérémie Bourdon
  • Damien Eveillard
  • Anne Siegel

Abstract

Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments. Author Summary: Understanding the response of a biological system to a stress is of great interest in biology. This issue is usually tackled by integrating information arising from different experiments into mathematical models. In particular, continuous models take quantitative information into account after a parameter estimation step whereas much recent research has focused on the qualitative behaviors of macromolecular networks. However, both modeling approaches fail to handle the true nature of biological information, including heterogeneity, incompleteness and multi-scale features, as emphasized by recent advances in molecular techniques. The principle novelty of our method lies in the use of probabilities and average-case analysis to overcome this weakness and to fill the gap between qualitative and quantitative models. Our framework is applied to study the response of Escherichia coli to a carbon starvation stress. We combine a small amount of quantitative information on protein concentrations with a qualitative model of transcriptional regulations. We derive quantitative predictions about proteins, quantify the robustness and relevance of transcriptional interactions, and automatically extract the key features of the model. The main biological novelty is therefore the presentation of new knowledge derived from the combination of quantitative and qualitative multi-scale information in a single approach.

Suggested Citation

  • Jérémie Bourdon & Damien Eveillard & Anne Siegel, 2011. "Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network," PLOS Computational Biology, Public Library of Science, vol. 7(9), pages 1-11, September.
  • Handle: RePEc:plo:pcbi00:1002157
    DOI: 10.1371/journal.pcbi.1002157
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002157
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002157&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1002157?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Long Cai & Nir Friedman & X. Sunney Xie, 2006. "Stochastic protein expression in individual cells at the single molecule level," Nature, Nature, vol. 440(7082), pages 358-362, March.
    2. Johan Paulsson, 2004. "Summing up the noise in gene networks," Nature, Nature, vol. 427(6973), pages 415-418, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hailin Meng & Jianfeng Wang & Zhiqiang Xiong & Feng Xu & Guoping Zhao & Yong Wang, 2013. "Quantitative Design of Regulatory Elements Based on High-Precision Strength Prediction Using Artificial Neural Network," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.

    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. Lee, Julian, 2023. "Poisson distributions in stochastic dynamics of gene expression: What events do they count?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    2. Benjamin B Kaufmann & Qiong Yang & Jerome T Mettetal & Alexander van Oudenaarden, 2007. "Heritable Stochastic Switching Revealed by Single-Cell Genealogy," PLOS Biology, Public Library of Science, vol. 5(9), pages 1-8, September.
    3. Jin Wang & Bo Huang & Xuefeng Xia & Zhirong Sun, 2006. "Funneled Landscape Leads to Robustness of Cell Networks: Yeast Cell Cycle," PLOS Computational Biology, Public Library of Science, vol. 2(11), pages 1-10, November.
    4. Mohammad Soltani & Cesar A Vargas-Garcia & Duarte Antunes & Abhyudai Singh, 2016. "Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-23, August.
    5. Mayu Sugiyama & Takashi Saitou & Hiroshi Kurokawa & Asako Sakaue-Sawano & Takeshi Imamura & Atsushi Miyawaki & Tadahiro Iimura, 2014. "Live Imaging-Based Model Selection Reveals Periodic Regulation of the Stochastic G1/S Phase Transition in Vertebrate Axial Development," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-16, December.
    6. Chen Jia & Ramon Grima, 2024. "Holimap: an accurate and efficient method for solving stochastic gene network dynamics," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    7. Kyung H Kim & Herbert M Sauro, 2012. "Adjusting Phenotypes by Noise Control," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-14, January.
    8. Yann S Dufour & Sébastien Gillet & Nicholas W Frankel & Douglas B Weibel & Thierry Emonet, 2016. "Direct Correlation between Motile Behavior and Protein Abundance in Single Cells," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-25, September.
    9. Ruoyu Luo & Lin Ye & Chenyang Tao & Kankan Wang, 2013. "Simulation of E. coli Gene Regulation including Overlapping Cell Cycles, Growth, Division, Time Delays and Noise," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-10, April.
    10. Shu Wang & Jia-Ren Lin & Eduardo D Sontag & Peter K Sorger, 2019. "Inferring reaction network structure from single-cell, multiplex data, using toric systems theory," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-25, December.
    11. Paolo Annibale & Stefano Vanni & Marco Scarselli & Ursula Rothlisberger & Aleksandra Radenovic, 2011. "Quantitative Photo Activated Localization Microscopy: Unraveling the Effects of Photoblinking," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
    12. Elijah Roberts & Andrew Magis & Julio O Ortiz & Wolfgang Baumeister & Zaida Luthey-Schulten, 2011. "Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-21, March.
    13. Keun-Young Kim & Jin Wang, 2007. "Potential Energy Landscape and Robustness of a Gene Regulatory Network: Toggle Switch," PLOS Computational Biology, Public Library of Science, vol. 3(3), pages 1-13, March.
    14. Luca Cardelli & Rosa D Hernansaiz-Ballesteros & Neil Dalchau & Attila Csikász-Nagy, 2017. "Efficient Switches in Biology and Computer Science," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-16, January.
    15. Bar Haim Y. & Booth James G. & Wells Martin T., 2012. "A Mixture-Model Approach for Parallel Testing for Unequal Variances," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-21, January.
    16. Vera Bettenworth & Simon Vliet & Bartosz Turkowyd & Annika Bamberger & Heiko Wendt & Matthew McIntosh & Wieland Steinchen & Ulrike Endesfelder & Anke Becker, 2022. "Frequency modulation of a bacterial quorum sensing response," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    17. Chen, Aimin & Tian, Tianhai & Chen, Yiren & Zhou, Tianshou, 2022. "Stochastic analysis of a complex gene-expression model," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    18. Yuval Elhanati & Naama Brenner, 2012. "Metabolic Variability in Micro-Populations," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-9, December.
    19. Piers J Ingram & Michael P H Stumpf & Jaroslav Stark, 2008. "Nonidentifiability of the Source of Intrinsic Noise in Gene Expression from Single-Burst Data," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-10, October.
    20. Cappelletti, Daniele & Pal Majumder, Abhishek & Wiuf, Carsten, 2021. "The dynamics of stochastic mono-molecular reaction systems in stochastic environments," Stochastic Processes and their Applications, Elsevier, vol. 137(C), pages 106-148.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pcbi00:1002157. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.