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Tuning the diffusion constant to optimize the readout of positional information of spatial concentration patterns |
| Ka Kit Kong(江嘉杰)1, Chunxiong Luo(罗春雄)1,2,3, and Feng Liu(刘峰)4,† |
1 The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing 100871, China;
2 Center for Quantitative Biology, Peking University, Beijing 100871, China;
3 Wenzhou Institute University of Chinese Academy of Sciences, Wenzhou 325001, China;
4 Key Laboratory of Hebei Province for Molecular Biophysics, Institute of Biophysics, School of Health Science & Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China |
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Abstract Positional information encoded in spatial concentration patterns is crucial for the development of multicellular organisms. However, it is still unclear how such information is affected by the physically dissipative diffusion process. Here we study one-dimensional patterning systems with analytical derivation and numerical simulations. We find that the diffusion constant of the patterning molecules exhibits a nonmonotonic effect on the readout of the positional information from the concentration patterns. Specifically, there exists an optimal diffusion constant that maximizes the positional information. Moreover, we find that the energy dissipation due to the physical diffusion imposes a fundamental upper limit on the positional information.
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Received: 08 April 2024
Revised: 07 May 2024
Accepted manuscript online:
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PACS:
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87.18.Hf
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(Spatiotemporal pattern formation in cellular populations)
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87.19.lo
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(Information theory)
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87.15.Vv
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(Diffusion)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 32271293 and 11875076). |
Corresponding Authors:
Feng Liu
E-mail: liufeng@hebut.edu.cn
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Cite this article:
Ka Kit Kong(江嘉杰), Chunxiong Luo(罗春雄), and Feng Liu(刘峰) Tuning the diffusion constant to optimize the readout of positional information of spatial concentration patterns 2024 Chin. Phys. B 33 088703
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[1] Lander A D 2013 Science 339 923
[2] Lewis J, Slack J M W and Wolpert L 1977 J. Theor. Biology 65 579
[3] Yang Z, Wu X, Yang N and Liu F 2018 Quantitative Biology 6 15
[4] Rogers K W and Schier A F 2011 Annual Review of Cell and Developmental Biology 27 377
[5] Muller P, Rogers K W, Yu S R, Brand M and Schier A F 2013 Development 140 1621
[6] Vetter R and Iber D 2022 Nat. Commun. 13 1145
[7] Fancher S and Mugler A 2020 eLife 9 1
[8] Tkačik G, Dubuis J O, Petkova M D and Gregor T 2015 Genetics 199 39
[9] Dubuis J O, Tkacik G, Wieschaus E F, Gregor T and Bialek W 2013 Proc. Natl. Acad. Sci. USA 110 16301
[10] Tkačik G and Gregor T 2021 Development 148 dev176065
[11] Bauer M, Petkova M D, Gregor T, Wieschaus E F and Bialek W 2021 Proc. Nat. Acad. Sci. USA 118 e2109011118
[12] Murray S M and Sourjik V 2017 Nat. Phys. 13 1006
[13] Zhang D, Zhang C, Ouyang Q and Tu Y 2023 J. R. Soc. Interface. 20 20230276
[14] Mori Y, Jilkine A and Edelstein-Keshet L 2008 Biophys. J. 94 3684
[15] Stapornwongkul K S and Vincent J P 2021 Nature Reviews Genetics 22 393
[16] Weil T T, Forrest K M and Gavis E R 2006 Developmental Cell 11 251
[17] Reck-Peterson S L, Redwine W B, Vale R D and Carter A P 2018 Nat. Rev. Mol. Cell Biol. 19 382
[18] Berezhkovskii A M, Sample C and Shvartsman S Y 2010 Biophys. J. 99 L59
[19] Emberly E 2008 Phys. Rev.E 77 041903
[20] Saunders T E and Howard M 2009 Phys. Rev. E 80 041902
[21] Sokolowski T R and Tkačik G 2015 Phys. Rev. E 91 062710
[22] Song Y and Hyeon C 2021 eLife 10 e70034
[23] McDonnell M D and Abbott D 2009 PLoS Comput Biol 5 e1000348
[24] Tottori T, Fujii M and Kuroda S 2019 Phys. Rev. E 100 042403
[25] Martineau S, Saffold T, Chang T T and Ronellenfitsch H 2022 Phys. Rev. Lett. 128 98301
[26] Nicolaou Z G, Sebek M, Kiss I Z and Motter A E 2020 Phys. Rev. Lett. 125 094101
[27] Berg H C and Purcell E M 1977 Biophys. J. 20 193
[28] Kaizu K, de Ronde W, Paijmans J, Takahashi K, Tostevin F and ten Wolde P R 2014 Biophys. J. 106 976
[29] Ten Wolde P R, Becker N B, Ouldridge T E and Mugler A 2016 J. Stat. Phys. 162 1395
[30] Bialek W and Setayeshgar S 2005 Proc. Natl. Acad. Sci. USA 102 10040
[31] Gregor T, Tank D W, Wieschaus E F and Bialek W 2007 Cell 130 153
[32] Kraskov A, Stögbauer H and Grassberger P 2004 Phys. Rev. E 69 066138
[33] Grant P K, Szep G, Patange O, Halatek J, Coppard V, Csikász-Nagy A, Haseloff J, Locke J C W, Dalchau N and Phillips A 2020 Nat. Commun. 11 5545
[34] Sokolowski T R, Gregor T, Bialek W and Tkačik G 2023 arXiv: 2302.05680 [physics.bio-ph] |
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