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Chemotaxis pp 397–415Cite as

Modeling Excitable Dynamics of Chemotactic Networks

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1407))

Abstract

The study of chemotaxis has benefited greatly from computational models that describe the response of cells to chemoattractant stimuli. These models must keep track of spatially and temporally varying distributions of numerous intracellular species. Moreover, recent evidence suggests that these are not deterministic interactions, but also include the effect of stochastic variations that trigger an excitable network. In this chapter we illustrate how to create simulations of excitable networks using the Virtual Cell modeling environment.

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References

  1. Levchenko A, Iglesias PA (2002) Models of eukaryotic gradient sensing: application to chemotaxis of amoebae and neutrophils. Biophys J 82:50–63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Levine H, Kessler DA, Rappel WJ (2006) Directional sensing in eukaryotic chemotaxis: a balanced inactivation model. Proc Natl Acad Sci U S A 103:9761–9766

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Onsum MD, Rao CV (2009) Calling heads from tails: the role of mathematical modeling in understanding cell polarization. Curr Opin Cell Biol 21:74–81

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jilkine A, Edelstein-Keshet L (2011) A comparison of mathematical models for polarization of single eukaryotic cells in response to guided cues. PLoS Comput Biol 7:e1001121

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yang L, Effler JC, Kutscher BL et al (2008) Modeling cellular deformations using the level set formalism. BMC Syst Biol 2:68

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wolgemuth CW, Stajic J, Mogilner A (2011) Redundant mechanisms for stable cell locomotion revealed by minimal models. Biophys J 101:545–553

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Vanderlei B, Feng JJ, Edelstein-Keshet L (2011) A computational model of cell polarization and motility coupling mechanics and biochemistry. Multiscale Model Simul 9:1420–1443

    Article  PubMed  PubMed Central  Google Scholar 

  8. Neilson MP, Veltman DM, van Haastert PJ et al (2011) Chemotaxis: a feedback-based computational model robustly predicts multiple aspects of real cell behaviour. PLoS Biol 9:e1000618

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hecht I, Skoge ML, Charest PG et al (2011) Activated membrane patches guide chemotactic cell motility. PLoS Comput Biol 7:e1002044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Holmes WR, Edelstein-Keshet L (2012) A comparison of computational models for eukaryotic cell shape and motility. PLoS Comput Biol 8:e1002793

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Shi C, Huang CH, Devreotes PN, Iglesias PA (2013) Interaction of motility, directional sensing, and polarity modules recreates the behaviors of chemotaxing cells. PLoS Comput Biol 9:e1003122

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Naoki H, Sakumura Y, Ishii S (2008) Stochastic control of spontaneous signal generation for gradient sensing in chemotaxis. J Theor Biol 255:259–266

    Article  CAS  PubMed  Google Scholar 

  13. Xiong Y, Huang CH, Iglesias PA, Devreotes PN (2010) Cells navigate with a local-excitation, global-inhibition-biased excitable network. Proc Natl Acad Sci U S A 107:17079–17086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Iglesias PA, Devreotes PN (2012) Biased excitable networks: how cells direct motion in response to gradients. Curr Opin Cell Biol 24:245–253

    Article  CAS  PubMed  Google Scholar 

  15. Ryan GL, Petroccia HM, Watanabe N, Vavylonis D (2012) Excitable actin dynamics in lamellipodial protrusion and retraction. Biophys J 102:1493–1502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cooper RM, Wingreen NS, Cox EC (2012) An excitable cortex and memory model successfully predicts new pseudopod dynamics. PLoS One 7:e33528

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Huang CH, Tang M, Shi C et al (2013) An excitable signal integrator couples to an idling cytoskeletal oscillator to drive cell migration. Nat Cell Biol 15:1307–1316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Nishikawa M, Hörning M, Ueda M, Shibata T (2014) Excitable signal transduction induces both spontaneous and directional cell asymmetries in the phosphatidylinositol lipid signaling system for eukaryotic chemotaxis. Biophys J 106:723–734

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Tang M, Wang M, Shi C et al (2014) Evolutionarily conserved coupling of adaptive and excitable networks mediates eukaryotic chemotaxis. Nat Commun 5:5175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Skoge M, Yue H, Erickstad M et al (2014) Cellular memory in eukaryotic chemotaxis. Proc Natl Acad Sci U S A 111:14448–14453

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Janetopoulos C, Ma L, Devreotes PN, Iglesias PA (2004) Chemoattractant-induced phosphatidylinositol 3,4,5-trisphosphate accumulation is spatially amplified and adapts, independent of the actin cytoskeleton. Proc Natl Acad Sci U S A 101:8951–8956

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Postma M, Bosgraaf L, Loovers HM, Van Haastert PJ (2004) Chemotaxis: signalling modules join hands at front and tail. EMBO Rep 5:35–40

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Balázsi G, van Oudenaarden A, Collins JJ (2011) Cellular decision making and biological noise: from microbes to mammals. Cell 144:910–925

    Article  PubMed  PubMed Central  Google Scholar 

  24. Eldar A, Elowitz MB (2010) Functional roles for noise in genetic circuits. Nature 467:167–173

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rao CV, Wolf DM, Arkin AP (2002) Control, exploitation and tolerance of intracellular noise. Nature 420:231–237

    Article  CAS  PubMed  Google Scholar 

  26. Gillespie DT (2007) Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 58:35–55

    Article  CAS  PubMed  Google Scholar 

  27. Munsky B, Khammash M (2006) The finite state projection algorithm for the solution of the chemical master equation. J Chem Phys 124:044104

    Article  PubMed  Google Scholar 

  28. Gillespie DT (2000) The chemical Langevin equation. J Chem Phys 113:297–306

    Article  CAS  Google Scholar 

  29. Andrews SS (2012) Spatial and stochastic cellular modeling with the Smoldyn simulator. Methods Mol Biol 804:519–542

    Article  CAS  PubMed  Google Scholar 

  30. Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comput Biol 6:e1000705

    Article  PubMed  PubMed Central  Google Scholar 

  31. Alves R, Antunes F, Salvador A (2006) Tools for kinetic modeling of biochemical networks. Nat Biotechnol 24:667–672

    Article  CAS  PubMed  Google Scholar 

  32. Bathe K-J (1996) Finite element procedures. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  33. Cowan AE, Moraru II, Schaff JC et al (2012) Spatial modeling of cell signaling networks. Methods Cell Biol 110:195–221

    Article  PubMed  PubMed Central  Google Scholar 

  34. Resasco DC, Gao F, Morgan F et al (2012) Virtual Cell: computational tools for modeling in cell biology. Wiley Interdiscip Rev Syst Biol Med 4:129–140

    Article  CAS  PubMed  Google Scholar 

  35. Yang L, Iglesias PA (2009) Modeling spatial and temporal dynamics of chemotactic networks. Methods Mol Biol 571:489–505

    Article  CAS  PubMed  Google Scholar 

  36. Li HY, Ng WP, Wong CH et al (2007) Coordination of chromosome alignment and mitotic progression by the chromosome-based Ran signal. Cell Cycle 6:1886–1895

    Article  CAS  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA

    Sayak Bhattacharya

  2. Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, 21205, MD, USA

    Pablo A. Iglesias

Authors
  1. Sayak Bhattacharya
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  2. Pablo A. Iglesias
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Corresponding author

Correspondence to Pablo A. Iglesias .

Editor information

Editors and Affiliations

  1. Nat Inst of Allergy& Infectious Dis, NIH, Laboratory of Immunogenetics, Rockville, Maryland, USA

    Tian Jin

  2. National Institutes of Health, Nat Inst Alcohol Abuse & Alcoholism, Rockville, Maryland, USA

    Dale Hereld

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© 2016 Springer Science+Business Media New York

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Bhattacharya, S., Iglesias, P.A. (2016). Modeling Excitable Dynamics of Chemotactic Networks. In: Jin, T., Hereld, D. (eds) Chemotaxis. Methods in Molecular Biology, vol 1407. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3480-5_27

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  • DOI: https://doi.org/10.1007/978-1-4939-3480-5_27

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3478-2

  • Online ISBN: 978-1-4939-3480-5

  • eBook Packages: Springer Protocols

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