Optimization of Information Transmission through Noisy Biochemical Pathway

Main Article Content

Anush Mahajan Bhaswar Ghosh

Abstract

Living organisms are required to sense the environment accurately in order to ensure appropriate responses. The accuracy of estimating the environmental input is severely limited by noise stemming from inherent stochasticity of the chemical reactions involved in signaling pathways. Cells employ multiple strategies to improve the accuracy by tuning the reaction rates, for instance amplifying the response, reducing the noise etc.. However, the pathway also consumes energy through incorporating ATP in phosphorylating key signaling proteins involved in the reaction pathways. In many instances, improvements in accuracy elicit extra energetic cost. For example, higher deactivation rate suppresses the basal pathway activity effectively amplifying the dynamic range of the response which leads to improvement in accuracy. Higher deactivation rate also enhances the energy dissipation rate. Here, we employed a theoretical approach based on thermodynamics of information to explore the role of accuracy and energetic cost in the performance of a Mitogen Activated Protein Kinase signaling system. Our study shows that the accuracy-energy trade-off can explain the optimality of the reaction rates of the reaction pathways rather than accuracy alone. Our analysis elucidates the role of interplay between accuracy and energetic cost in evolutionary shaping of the parameter space of signaling pathways.

Article Details

How to Cite
MAHAJAN, Anush; GHOSH, Bhaswar. Optimization of Information Transmission through Noisy Biochemical Pathway. Medical Research Archives, [S.l.], v. 12, n. 3, apr. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/5139>. Date accessed: 16 apr. 2024. doi: https://doi.org/10.18103/mra.v12i3.5139.
Section
Research Articles

References

1. Grote, J., Krysciak, D. & Streit, W. R. Phenotypic heterogeneity, a phenomenon that may explain why quorum sensing does not always result in truly homogenous cell behavior. Appl. environmental microbiology 81, 5280–5289 (2015).
2. Perkins, T. J. & Swain, P. S. Strategies for cellular decision-making. Mol. systems biology 5, 326 (2009).
3. Lei, X., Tian, W., Zhu, H., Chen, T. & Ao, P. Biological sources of intrinsic and extrinsic noise in ci expression of lysogenic phage lambda. Sci. reports 5, 1–12 (2015).
4. Uda, S. Application of information theory in systems biology. Biophys. reviews 12, 377–384 (2020).
5. Brunel, N. & Nadal, J.-P. Mutual information, fisher information, and population coding. Neural computation 10, 1731–1757 (1998).
6. Gatenby, R. A. & Frieden, B. R. Information theory in living systems, methods, applications, and challenges. Bull. mathematical biology 69, 635–657 (2007).
7. Adami, C. Information theory in molecular biology. Phys. Life Rev. 1, 3–22 (2004).
8. Shannon, C. E. A mathematical theory of communication. The Bell system technical journal 27, 379–423 (1948).
9. Petkova, M. D., Tkacˇik, G., Bialek, W., Wieschaus, E. F. & Gregor, T. Optimal decoding of information from a genetic network. arXiv preprint arXiv:1612.08084 (2016).
10. Levchenko, A. & Nemenman, I. Cellular noise and information transmission. Curr. opinion biotechnology 28, 156–164 (2014).
11. Tu, K. C., Long, T., Svenningsen, S. L., Wingreen, N. S. & Bassler, B. L. Negative feedback loops involving small regulatory rnas precisely control the vibrio harveyi quorum-sensing response. Mol. cell 37, 567–579 (2010).
12. Anders, A., Ghosh, B., Glatter, T. & Sourjik, V. Design of a mapk signalling cascade balances energetic cost versus accuracy of information transmission. Nat. communications 11, 1–10 (2020).
13. Parrondo, J. M., Horowitz, J. M. & Sagawa, T. Thermodynamics of information. Nat. physics 11, 131–139 (2015).
14. Shacter, E., Chock, P. B. & Stadtman, E. Energy consumption in a cyclic phosphorylation/dephosphorylation cascade. J. Biol. Chem. 259, 12260–12264 (1984).
15. Barato, A. C., Hartich, D. & Seifert, U. Efficiency of cellular information processing. New J. Phys. 16, 103024 (2014).
16. Bardwell, L. A walk-through of the yeast mating pheromone response pathway. Peptides 26, 339–350 (2005).
17. Naider, F. & Becker, J. M. The α-factor mating pheromone of saccharomyces cerevisiae: a model for studying the interaction of peptide hormones and g protein-coupled receptors. Peptides 25, 1441–1463 (2004).
18. Dohlman, H. G. Proteins and pheromone signaling. Annu. review physiology 64, 129 (2002).
19. Lewis, T. S., Shapiro, P. S. & Ahn, N. G. Signal transduction through map kinase cascades. Adv. cancer research 74, 49–139 (1998).
20. Doi, K. et al. Msg5, a novel protein phosphatase promotes adaptation to pheromone response in s. cerevisiae. The EMBO journal 13, 61–70 (1994).
21. Yu, R. C. et al. Negative feedback that improves information transmission in yeast signalling. Nature 456, 755–761 (2008).
22. Madden, K. & Snyder, M. Cell polarity and morphogenesis in budding yeast. Annu. review microbiology 52, 687 (1998).
23. Malleshaiah, M. K., Shahrezaei, V., Swain, P. S. & Michnick, S. W. The scaffold protein ste5 directly controls a switch-like mating decision in yeast. Nature 465, 101–105 (2010).
24. Dixit, G., Kelley, J. B., Houser, J. R., Elston, T. C. & Dohlman, H. G. Cellular noise suppression by the regulator of g protein signaling sst2. Mol. cell 55, 85–96 (2014).
25. Hatano, T. & Sasa, S.-i. Steady-state thermodynamics of langevin systems. Phys. review letters 86, 3463 (2001).
26. Mehta, P. & Schwab, D. J. Energetic costs of cellular computation. Proc. Natl. Acad. Sci. 109, 17978–17982 (2012).
27. Lahiri, S., Sohl-Dickstein, J. & Ganguli, S. A universal tradeoff between power, precision and speed in physical communication. arXiv preprint arXiv:1603.07758 (2016).
28. Tânia, T. & de. Oliveira, M. J. Stochastic thermodynamics and entropy production of chemical reaction systems. J. Chem. Phys 148, 224104–1–224104–11 (2018).
29. Horowitz, J. & England, J. Spontaneous fine-tuning to environment in many-species chemical reaction networks. Proc Natl Acad Sci U S A 114, 7565–7570 (2017).
30. Sanner, M. F. et al. Python: a programming language for software integration and development. J Mol Graph Model. 17, 57–61 (1999).
31. Virtanen, P. et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat. Methods 17, 261–272, DOI: 10.1038/s41592-019-0686-2 (2020).
32. Goldberg, D. E. Genetic algorithms in search, optimization and machine learning addison welssey publishing company Reading, MA (1989).
33. Haupt, R. L. & Haupt, S. E. Practical genetic algorithms (John Wiley & Sons, 2004).
34. Michalewicz, Z. & Michalewicz, Z. GAs: Why Do They Work? (Springer, 1996).
35. Colman-Lerner, A. et al. Regulated cell-to-cell variation in a cell-fate decision system. Nature 437, 699–706 (2005).
36. Lan, G., Sartori, P., Neumann, S., Sourjik, V. & Tu, Y. The energy–speed–accuracy trade-off in sensory adaptation. Nat. physics 8, 422–428 (2012).
37. Govern, C. C. & Ten Wolde, P. R. Optimal resource allocation in cellular sensing systems. Proc. Natl. Acad. Sci. 111, 17486–17491 (2014).
38. Mehta, P. & Schwab, D. J. Energetic costs of cellular computation. Proc. Natl. Acad. Sci. 109, 17978–17982 (2012).
39. Yu, Q., Mallory, J. D., Kolomeisky, A. B., Ling, J. & Igoshin, O. A. Trade-offs between speed, accuracy, and dissipation in trnaile aminoacylation. The journal physical chemistry letters 11, 4001–4007 (2020).
40. Kanitscheider, I., Coen-Cagli, R., Kohn, A. & Pouget, A. Measuring fisher information accurately in correlated neural populations. PLoS computational biology 11, e1004218 (2015).
41. Zulkowski, P. R., Sivak, D. A. & DeWeese, M. R. Optimal control of transitions between nonequilibrium steady states. PloS one 8, e82754 (2013).
42. Sivak, D. A. & Crooks, G. E. Thermodynamic metrics and optimal paths. Phys. review letters 108, 190602 (2012).