Skip to main content
Log in

Regulatory disturbances in the dynamical signaling systems of \(Ca^{2+}\) and NO in fibroblasts cause fibrotic disorders

  • Research
  • Published:
Journal of Biological Physics Aims and scope Submit manuscript

Abstract

Studying the calcium dynamics within a fibroblast cell individually has provided only a restricted understanding of its functions. However, research efforts focusing on systems biology approaches for such investigations have been largely neglected by researchers until now. Fibroblast cells rely on signaling from calcium \((Ca^{2+})\) and nitric oxide (NO) to maintain their physiological functions and structural stability. Various studies have demonstrated the correlation between NO and the control of \(Ca^{2+}\) dynamics in cells. However, there is currently no existing model to assess the disruptions caused by various factors in regulatory dynamics, potentially resulting in diverse fibrotic disorders. A mathematical model has been developed to investigate the effects of changes in parameters such as buffer, receptor, sarcoplasmic endoplasmic reticulum \(Ca^{2+}\)-ATPase (SERCA) pump, and source influx on the regulation and dysregulation of spatiotemporal calcium and NO dynamics in fibroblast cells. This model is based on a system of reaction-diffusion equations, and numerical simulations are conducted using the finite element method. Disturbances in key processes related to calcium and nitric oxide, including source influx, buffer mechanism, SERCA pump, and inositol trisphosphate \((IP_3)\) receptor, may contribute to deregulation in the calcium and NO dynamics within fibroblasts. The findings also provide new insights into the extent and severity of disorders resulting from alterations in various parameters, potentially leading to deregulation and the development of fibrotic disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Wang, R., Ghahary, A., Shen, Y.J., Scott, P.G., Tredget, E.E.: Human dermal fibroblasts produce nitric oxide and express both constitutive and inducible nitric oxide synthase isoforms. J. Invest. Dermatol. 106(3), 419–427 (1996). https://doi.org/10.1111/1523-1747.ep12343428

    Article  Google Scholar 

  2. Tsoukias, N.M.: Nitric oxide bioavailability in the microcirculation: insights from mathematical models. Microcirculation 15(8), 813–834 (2008). https://doi.org/10.1080/10739680802010070

    Article  Google Scholar 

  3. Childress, B.B., Stechmiller, J.K.: Role of nitric oxide in wound healing. Biol. Res. Nurs. 4(1), 5–15 (2002). https://doi.org/10.1177/1099800402004001002

    Article  Google Scholar 

  4. Witte, M.B., Barbul, A.: Role of nitric oxide in wound repair. Am. J. Surg. 183(4), 406–412 (2002). https://doi.org/10.1016/S0002-9610(02)00815-2

    Article  Google Scholar 

  5. Iwakiri, Y.: Nitric oxide in liver fibrosis: The role of inducible nitric oxide synthase. Clin. Mol. Hepatol. 21(4), 319 (2015). https://doi.org/10.3350/cmh.2015.21.4.319

    Article  Google Scholar 

  6. de Winter-de Groot, K.M., van der Ent, C.K.: Nitric oxide in cystic fibrosis. J. Cyst. Fibros. 4, 25–29 (2005). https://doi.org/10.1016/j.jcf.2005.05.008

    Article  Google Scholar 

  7. Xu, W., Liu, L.Z., Loizidou, M., Ahmed, M., Charles, I.G.: The role of nitric oxide in cancer. Cell Res. 12(5), 311–320 (2002). https://doi.org/10.1038/sj.cr.7290133

    Article  Google Scholar 

  8. Wagner, J., Keizer, J.: Effects of rapid buffers on calcium diffusion and calcium oscillations. Biophys. J. 67(1), 447–456 (1994). https://doi.org/10.1016/S0006-3495(94)80500-4

    Article  ADS  Google Scholar 

  9. Li, Y.-X., Rinzel, J.: Equations for insp3 receptor-mediated [Ca2+]i oscillations derived from a detailed kinetic model: a Hodgkin-Huxley like formalism. J. Theor. Biol. 166(4), 461–473 (1994). https://doi.org/10.1006/jtbi.1994.1041

  10. Jafri, M., Keizer, J.: On the roles of calcium diffusion, calcium buffers, and the endoplasmic reticulum in IP3-induced calcium waves. Biophys. J. 69(5), 2139–2153 (1995). https://doi.org/10.1016/S0006-3495(95)80088-3

  11. Smith, G.D.: Analytical steady-state solution to the rapid buffering approximation near an open calcium channel. Biophys. J. 71(6), 3064–3072 (1996). https://doi.org/10.1016/S0006-3495(96)79500-0

    Article  ADS  Google Scholar 

  12. Wagner, J., Fall, C.P., Hong, F., Sims, C.E., Allbritton, N.L., Fontanilla, R.A., Moraru, I.I., Loew, L.M., Nuccitelli, R.: A wave of IP3 production accompanies the fertilization calcium wave in the egg of the frog, xenopus laevis: theoretical and experimental support. Cell Calcium 35(5), 433–447 (2004). https://doi.org/10.1016/j.ceca.2003.10.009

  13. Sun, G.-X., Wang, L.-J., Xiang, C., Qin, K.-R.: A dynamic model for intracellular calcium response in fibroblasts induced by electrical stimulation. Math. Biosci. 244(1), 47–57 (2013). https://doi.org/10.1016/j.mbs.2013.04.005

    Article  MathSciNet  Google Scholar 

  14. Manhas, N., Pardasani, K.: Modelling mechanism of calcium oscillations in pancreatic acinar cells. J. Bioenerg. Biomembr. 46(5), 403–420 (2014). https://doi.org/10.1007/s10863-014-9561-0

    Article  Google Scholar 

  15. Manhas, N., Sneyd, J., Pardasani, K.: Modelling the transition from simple to complex calcium oscillations in pancreatic acinar cells. J. Biosci. 39(3), 463–484 (2014). https://doi.org/10.1007/s12038-014-9430-3

    Article  Google Scholar 

  16. Naik, P.A., Pardasani, K.R.: One dimensional finite element method approach to study effect of ryanodine receptor and serca pump on calcium distribution in oocytes. J. Multiscale Model. 5(2), 1350007 (2013). https://doi.org/10.1142/S1756973713500078

    Article  ADS  MathSciNet  Google Scholar 

  17. Naik, P.A., Pardasani, K.R.: One dimensional finite element model to study calcium distribution in oocytes in presence of vgcc, ryr and buffers. J. Med. Imaging Health Inform. 5(3), 471–476 (2015). https://doi.org/10.1166/jmihi.2015.1431

    Article  Google Scholar 

  18. Naik, P.A., Pardasani, K.R.: Three-dimensional finite element model to study effect of ryr calcium channel, er leak and serca pump on calcium distribution in oocyte cell. Int. J. Comput. Methods 16(01), 1850091 (2019). https://doi.org/10.1142/S0219876218500913

    Article  MathSciNet  Google Scholar 

  19. Naik, P.A., Pardasani, K.R.: Finite element model to study calcium distribution in oocytes involving voltage gated calcium channel, ryanodine receptor and buffers. Alexandr. J. Med. 52(1), 43–49 (2016). https://doi.org/10.1016/j.ajme.2015.02.002

    Article  Google Scholar 

  20. Kotwani, M., Adlakha, N., Mehta, M.: Finite element model to study the effect of buffers, source amplitude and source geometry on spatio-temporal calcium distribution in fibroblast cell. J. Med. Imaging Health Inform. 4(6), 840–847 (2014). https://doi.org/10.1166/jmihi.2014.1328

    Article  Google Scholar 

  21. Tewari, V., Tewari, S., Pardasani, K.: A model to study the effect of excess buffers and na+ ions on ca2+ diffusion in neuron cell. Int. J. Bioeng. Life Sci. 5(4), 251–256 (2011). https://doi.org/10.5281/zenodo.1054988

    Article  Google Scholar 

  22. Jha, A., Adlakha, N.: Analytical solution of two dimensional unsteady state problem of calcium diffusion in a neuron cell. J. Med. Imaging Health Inform. 4(4), 547–553 (2014). https://doi.org/10.1166/jmihi.2014.1282

    Article  Google Scholar 

  23. Jha, A., Adlakha, N.: Two-dimensional finite element model to study unsteady state calcium diffusion in neuron involving er leak and serca. Int. J. Biomath. 8(1), 1550002 (2015). https://doi.org/10.1142/S1793524515500023

    Article  MathSciNet  Google Scholar 

  24. Jha, A., Adlakha, N., Jha, B.K.: Finite element model to study effect of sodium-calcium exchangers and source geometry on calcium dynamics in a neuron cell. J. Mech. Med. Biol. 16(02), 1650018 (2016). https://doi.org/10.1142/S0219519416500184

    Article  Google Scholar 

  25. Jha, B.K., Adlakha, N., Mehta, M.: Two-dimensional finite element model to study calcium distribution in astrocytes in presence of excess buffer. Int. J. Biomath. 7(3), 1450031 (2014). https://doi.org/10.1142/S1793524514500314

    Article  MathSciNet  Google Scholar 

  26. Pathak, K., Adlakha, N.: Finite element model to study two dimensional unsteady state calcium distribution in cardiac myocytes. Alexandr. J. Med. 52(3), 261–268 (2016). https://doi.org/10.1016/j.ajme.2015.09.007

    Article  Google Scholar 

  27. Jagtap, Y., Adlakha, N.: Simulation of buffered advection diffusion of calcium in a hepatocyte cell. Math. Biol. Bioinform. 13(2), 609–619 (2018). https://doi.org/10.17537/2018.13.609

    Article  Google Scholar 

  28. Jagtap, Y., Adlakha, N.: Finite volume simulation of two dimensional calcium dynamics in a hepatocyte cell involving buffers and fluxes. Commun. Math. Biol. Neurosci. 2018, (2018). https://doi.org/10.28919/cmbn/3689

  29. Kotwani, M., Adlakha, N., Mehta, M.: Numerical model to study calcium diffusion in fibroblasts cell for one dimensional unsteady state case. Appl. Math. Sci. 6(102), 5063–5072 (2012)

    Google Scholar 

  30. Kotwani, M., Adlakha, N.: Modeling of endoplasmic reticulum and plasma membrane calcium uptake and release fluxes with excess buffer approximation (eba) in fibroblast cell. Intl. J. Comput. Mater. Sci. Eng. 6(1), 1750004 (2017). https://doi.org/10.1142/S204768411750004

    Article  Google Scholar 

  31. Naik, P.A., Pardasani, K.R.: 2d finite-element analysis of calcium distribution in oocytes. Net. Model. Anal. Health Inform. Bioinform. 7(1), 1–11 (2018). https://doi.org/10.1007/s13721-018-0172-2

    Article  Google Scholar 

  32. Joshi, H., Jha, B.K.: Fractional-order mathematical model for calcium distribution in nerve cells. Comput. Appl. Math. 39(2), 1–22 (2020). https://doi.org/10.1007/s40314-020-1082-3

    Article  MathSciNet  Google Scholar 

  33. Joshi, H., Jha, B.K.: Chaos of calcium diffusion in Parkinson’s infectious disease model and treatment mechanism via Hilfer fractional derivative. Math. Model. Numer. Simul. Appl. 1(2), 84–94 (2021). https://doi.org/10.53391/mmnsa.2021.01.008

    Article  Google Scholar 

  34. Bhardwaj, H., Adlakha, N.: Radial basis function based differential quadrature approach to study reaction diffusion of CA2+ in T lymphocyte. Int. J. Comput. Meth. (2022). https://doi.org/10.1142/S0219876222500591

  35. Vaughn, M.W., Kuo, L., Liao, J.C.: Estimation of nitric oxide production and reaction rates in tissue by use of a mathematical model. Am. J. Physiol. Heart Circ. Physiol. 274(6), 2163–2176 (1998). https://doi.org/10.1152/ajpheart.1998.274.6.H2163

    Article  Google Scholar 

  36. Kim, N.N., Villegas, S., Summerour, S.R., Villarreal, F.J.: Regulation of cardiac fibroblast extracellular matrix production by bradykinin and nitric oxide. J. Mol. Cell. Cardiol. 31(2), 457–466 (1999). https://doi.org/10.1006/jmcc.1998.0887

    Article  Google Scholar 

  37. Buerk, D.G., Barbee, K.A., Jaron, D.: Nitric oxide signaling in the microcirculation. Crit. Rev. Biomed. Eng. 39(5), (2011). https://doi.org/10.1615/critrevbiomedeng.v39.i5.40

  38. Bolotina, V.M., Najibi, S., Palacino, J.J., Pagano, P.J., Cohen, R.A.: Nitric oxide directly activates calcium-dependent potassium channels in vascular smooth muscle. Nature 368(6474), 850–853 (1994). https://doi.org/10.1038/368850a0

    Article  ADS  Google Scholar 

  39. Manhas, N., Pardasani, K.R.: Mathematical model to study IP3 dynamics dependent calcium oscillations in pancreatic acinar cells. J. Med. Imaging Health Inform. 4(6), 874–880 (2014). https://doi.org/10.1166/jmihi.2014.1333

  40. Singh, N., Adlakha, N.: A mathematical model for interdependent calcium and inositol 1, 4, 5-trisphosphate in cardiac myocyte. Netw. Model. Anal. Health Inform. Bioinform. 8(1), 1–15 (2019). https://doi.org/10.1007/s13721-019-0198-0

    Article  Google Scholar 

  41. Jagtap, Y., Adlakha, N.: Numerical study of one-dimensional buffered advection-diffusion of calcium and IP3 in a hepatocyte cell. Netw. Model. Anal. Health Inform. Bioinform. 8(1), 1–9 (2019). https://doi.org/10.1007/s13721-019-0205-5

  42. Kothiya, A., Adlakha, N.: Model of calcium dynamics regulating IP3 and ATP production in a fibroblast cell. Adv. Syst. Sci. Appl. 22(3), 106–125 (2022). https://doi.org/10.25728/assa.2022.22.3.1219

  43. Pawar, A., Pardasani, K.R.: Effects of disorders in interdependent calcium and IP3 dynamics on nitric oxide production in a neuron cell. Eur. Phys. J. Plus 137(5), 1–19 (2022). https://doi.org/10.1140/epjp/s13360-022-02743-2

  44. Pawar, A., Pardasani, K.R.: Effect of disturbances in neuronal calcium and IP3 dynamics on β-amyloid production and degradation. Cogn. Neurodyn. 1–18 (2022). https://doi.org/10.1007/s11571-022-09815-0

  45. Kothiya, A.B., Adlakha, N.: Cellular nitric oxide synthesis is affected by disorders in the interdependent calcium and IP3 dynamics during cystic fibrosis disease. J. Biol. Phys. 1–26 (2023). https://doi.org/10.1007/s10867-022-09624-w

  46. Pawar, A., Pardasani, K.R.: Mechanistic insights of neuronal calcium and IP3 signaling system regulating ATP release during ischemia in progression of Alzheimer’s disease. Eur. Biophys. J. 1–21 (2023). https://doi.org/10.1007/s00249-023-01660-1

  47. Vaishali, Adlakha, N.: Model of calcium dynamics regulating IP3, ATP and insulin production in a pancreatic β-cell. Acta Biotheor. 72(1), 2 (2024). https://doi.org/10.1007/s10441-024-09477-x

  48. Bhardwaj, H., Adlakha, N.: Model to study interdependent calcium and IP3 distribution regulating nfat production in T lymphocyte. J. Mech. Med. Biol. (2023). https://doi.org/10.1142/S0219519423500550

  49. Jagtap, Y., Adlakha, N.: Numerical model of hepatic glycogen phosphorylase regulation by nonlinear interdependent dynamics of calcium and IP3. Eur. Phys. J. Plus 138(5), 1–13 (2023). https://doi.org/10.1140/epjp/s13360-023-03961-y

  50. Pawar, A., Pardasani, K.R.: Computational model of calcium dynamics-dependent dopamine regulation and dysregulation in a dopaminergic neuron cell. Eur. Phys. J. Plus 138(1), 1–19 (2023). https://doi.org/10.1140/epjp/s13360-023-03691-1

    Article  Google Scholar 

  51. Pawar, A., Pardasani, K.R.: Study of disorders in regulatory spatiotemporal neurodynamics of calcium and nitric oxide. Cogn. Neurodyn. 1–22 (2022). https://doi.org/10.1007/s11571-022-09902-2

  52. Pawar, A., Pardasani, K.R.: Simulation of disturbances in interdependent calcium and β-amyloid dynamics in the nerve cell. Eur. Phys. J. Plus 137(8), 1–23 (2022). https://doi.org/10.1140/epjp/s13360-022-03164-x

    Article  Google Scholar 

  53. Pawar, A., Pardasani, K.R.: Fractional order interdependent nonlinear chaotic spatiotemporal calcium and a β dynamics in a neuron cell. Phys. Scr. (2023). https://doi.org/10.1088/1402-4896/ace1b2

    Article  Google Scholar 

  54. Bhardwaj, H., Adlakha, N.: Fractional order reaction diffusion of calcium regulating nfat production in t lymphocyte. Int. J. Biomath. (2023). https://doi.org/10.1142/S1793524523500547

    Article  Google Scholar 

  55. Pawar, A., Pardasani, K.R.: Fractional-order reaction-diffusion model to study the dysregulatory impacts of superdiffusion and memory on neuronal calcium and IP3 dynamics. Eur. Phys. J. Plus 138(9), 1–17 (2023). https://doi.org/10.1140/epjp/s13360-023-04410-6

  56. Kothiya, A., Adlakha, N.: Simulation of biochemical dynamics of calcium and plc in fibroblast cell. J. Bioenerg. Biomembr. 1–21 (2023). https://doi.org/10.1007/s10863-023-09976-5

  57. Kothiya, A., Adlakha, N.: Impact of interdependent Ca2+ and IP3 dynamics on ATP regulation in a fibroblast model. Cell Biochem. Biophys. 1–17 (2023). https://doi.org/10.1007/s12013-023-01177-6

  58. Kothiya, A., Adlakha, N.: Computational investigations of the Ca2+ and TGF-β dynamics in fibroblast cells. Eur. Phys. J. Plus 138(10), 1–21 (2023). https://doi.org/10.1140/epjp/s13360-023-04508-x

  59. Kothiya, A., Adlakha, N.: Mathematical model for system dynamics of (Ca2+) and dopamine in a fibroblast cell. J. Biol. Syst. 1–28 (2024). https://doi.org/10.1142/S0218339024500177

  60. Pawar, A., Pardasani, K.R.: Computational model of interacting system dynamics of calcium, IP3 and β-amyloid in ischemic neuron cells. Phys. Scr. 99(1), 015025 (2023). https://doi.org/10.1088/1402-4896/ad16b5

    Article  ADS  Google Scholar 

  61. Pawar, A., Pardasani, K.R.: Modelling cross talk in the spatiotemporal system dynamics of calcium, IP3 and nitric oxide in neuron cells. Cell Biochem. Biophys. 1–17 (2024). https://doi.org/10.1007/s12013-024-01229-5

  62. Gibson, W.G., Farnell, L., Bennett, M.R.: A computational model relating changes in cerebral blood volume to synaptic activity in neurons. Neurocomputing 70(10–12), 1674–1679 (2007). https://doi.org/10.1016/j.neucom.2006.10.071

    Article  Google Scholar 

  63. Dupont, G., Swillens, S., Clair, C., Tordjmann, T., Combettes, L.: Hierarchical organization of calcium signals in hepatocytes: from experiments to models. Biochim. Biophys. Acta, Mol. Cell Res. 1498(2–3), 134–152 (2000). https://doi.org/10.1016/S0167-4889(00)00090-2

    Article  Google Scholar 

  64. Van Liew, H.D., Raychaudhuri, S.: Stabilized bubbles in the body: pressure-radius relationships and the limits to stabilization. J. Appl. Physiol. 82(6), 2045–2053 (1997). https://doi.org/10.1152/jappl.1997.82.6.2045

    Article  Google Scholar 

  65. Brown, S.-A., Morgan, F., Watras, J., Loew, L.M.: Analysis of phosphatidylinositol-4, 5-bisphosphate signaling in cerebellar Purkinje spines. Biophys. J. 95(4), 1795–1812 (2008). https://doi.org/10.1529/biophysj.108.130195

    Article  ADS  Google Scholar 

  66. Kavdia, M., Tsoukias, N.M., Popel, A.S.: Model of nitric oxide diffusion in an arteriole: impact of hemoglobin-based blood substitutes. American J. Physiol. Heart Circ. Physiol. 282(6), 2245–2253 (2002). https://doi.org/10.1152/ajpheart.00972.2001

    Article  Google Scholar 

  67. Gnegy, M.E., Erickson, R.P., Markovac, J.: Increased calmodulin in cultured skin fibroblasts from patients with cystic fibrosis. Biochem. Med. 26(3), 294–298 (1981). https://doi.org/10.1016/0006-2944(81)90004-1

    Article  Google Scholar 

  68. Shapiro, B.L., Feigal, R.J., Laible, N.J., Biros, M.H., Warwick, W.J.: Doubling time α-aminoisobutyrate transport and calcium exchange in cultured fibroblasts from cystic fibrosis and control subjects. Clin. Chim. Acta 82(1–2), 125–131 (1978). https://doi.org/10.1016/0009-8981(78)90035-9

    Article  Google Scholar 

  69. Öziş, T., Aksan, E., Özdeş, A.: A finite element approach for solution of Burgers’ equation. Appl. Math. Comput. 139(2–3), 417–428 (2003). https://doi.org/10.1016/S0096-3003(02)00204-7

    Article  MathSciNet  Google Scholar 

  70. Beckman, J.S.: Oxidative damage and tyrosine nitration from peroxynitrite. Chem. Res. Toxicol. 9(5), 836–844 (1996). https://doi.org/10.1021/tx9501445

    Article  Google Scholar 

Download references

Funding

None.

Author information

Authors and Affiliations

Authors

Contributions

The first author created Matlab code and the mathematical model, and performed numerical simulations. Both authors contributed to the final text version.

Corresponding author

Correspondence to Ankit Kothiya.

Ethics declarations

Ethics approval

The study is conceptual and quantitative, with no animal tests requiring ethical approval.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Shape function for each element;

$$\begin{aligned} u1^{(e)}=l_1^{(e)}+l_2^{(e)}x, \end{aligned}$$
(16)
$$\begin{aligned} v1^{(e)}=m_1^{(e)}+m_2^{(e)}x, \end{aligned}$$
(17)
$$\begin{aligned} u1^{(e)}=SS^T\times l^{(e)}, \, v1^{(e)}=SS^T\times m^{(e)}. \end{aligned}$$
(18)

where

$$\begin{aligned} SS^T=[1 \, \, \, x], \, l^{{(e)}^{T}}=[l_1^{(e)}\,l_2^{(e)}] \, and \, m^{{(e)}^T}=[m_1^{(e)}\,m_2^{(e)}]. \end{aligned}$$
(19)

From Eq. (18), we get

$$\begin{aligned} \bar{u1}^{(e)}=SS^{(e)}\times l^{(e)},\, and \, \bar{v1}^{(e)}=SS^{(e)}\times m^{(e)}. \end{aligned}$$
(20)

where

$$\begin{aligned} \bar{u1}^{(e)}= \begin{bmatrix} u1_i\\ u1_j \end{bmatrix}, \, \bar{v1}^{(e)}= \begin{bmatrix} v1_i\\ v1_j \end{bmatrix} \, and \, S^{(e)}= \begin{bmatrix} 1 &{} x_i\\ 1 &{} x_j \end{bmatrix}. \end{aligned}$$
(21)

From (20) we have

$$\begin{aligned} l^{(e)}=(M^{(e)})\times \bar{u1}^{(e)}\ \text{and}\ m^{(e)}=(M^{(e)})\times \bar{v1}^{(e)}. \end{aligned}$$
(22)

where

$$\begin{aligned} M^{(e)}=\frac{1}{SS^{(e)}}. \end{aligned}$$
(23)

From Eqs. (22) and (18), we get

$$\begin{aligned} u1^{(e)}=SS^T \times M^{(e)} \, \bar{u1}^{(e)} \, and \, v1^{(e)}=SS^T\times M^{(e)} \, \bar{v1}^{(e)}. \end{aligned}$$
(24)

From Eqs. (1) and (8) we get

$$\begin{aligned} I_1^{(e)}=I_{ao1}^{(e)}-I_{bo1}^{(e)}+I_{co1}^{(e)}-I_{do1}^{(e)}+I_{eo1}^{(e)}-I_{fo1}^{(e)}-I_{go1}^{(e)}. \end{aligned}$$
(25)

where

$$\begin{aligned} I_{ao1}^{(e)}=\int _{x_{i}}^{x_{j}}\left[ \left[ \frac{\partial u1^{(e)}}{\partial x} \right] ^2\right] dx, \end{aligned}$$
(26)
$$\begin{aligned} I_{bo1}^{(e)}=\frac{d}{dx}\int _{x_{i}}^{x_{j}}\left[ \frac{u1^{(e)}}{D_{Ca} } \right] dx, \end{aligned}$$
(27)
$$\begin{aligned} I_{co1}^{(e)}=\frac{V_{IPR}}{D_{Ca}F_C}\int _{x_{i}}^{x_{j}}\left[ \alpha u1^{(e)}+\beta v1^{(e)}+\gamma \right] dx, \end{aligned}$$
(28)
$$\begin{aligned} I_{do1}^{(e)}=\frac{1}{D_{Ca}F_C}\int _{x_{i}}^{x_{j}}\left[ V_{sp}ku1^{(e)}-\eta +V_{PMCA}k1u1^{(e)}-\eta 1\right] dx, \end{aligned}$$
(29)
$$\begin{aligned} I_{eo1}^{(e)}=\frac{V_{leak}}{D_{Ca}F_C}\int _{x_{i}}^{x_{j}}\left[ [Ca^{2+}]_{ER}-u1^{(e)} \right] dx, \end{aligned}$$
(30)
$$\begin{aligned} I_{fo1}^{(e)}=\frac{K^+}{D_{Ca}}\int _{x_{i}}^{x_{j}}\left[ u1^{(e)}-[Ca^{2+}]_{\infty } \right] dx, \end{aligned}$$
(31)
$$\begin{aligned} I_{go1}^{(e)}=f^{(e)}\left[ \frac{\sigma }{D_{Ca}}\right] _{x=0}, \end{aligned}$$
(32)
$$\begin{aligned} I_2^{(e)}=I_{ao2}^{(e)}-I_{bo2}^{(e)}+I_{co2}^{(e)}-I_{do2}^{(e)}, \end{aligned}$$
(33)
$$\begin{aligned} I_{ao2}^{(e)}=\int _{x_{i}}^{x_{j}}\left[ \left[ \frac{\partial v1^{(e)}}{\partial x} \right] ^2\right] dx, \end{aligned}$$
(34)
$$\begin{aligned} I_{bo2}^{(e)}=\frac{d}{dx}\int _{x_{i}}^{x_{j}}\left[ \frac{v1^{(e)}}{D_{NO} } \right] dx, \end{aligned}$$
(35)
$$\begin{aligned} I_{co2}^{(e)}=\frac{V_{pro}}{D_{NO}}\int _{x_{i}}^{x_{j}}\left[ \mu u1^{(e)}+\tau \right] dx, \end{aligned}$$
(36)
$$\begin{aligned} I_{do2}^{(e)}=\frac{V_{deg}}{D_{NO}}\int _{x_{i}}^{x_{j}}\left[ v1^{(e)} \right] dx. \end{aligned}$$
(37)

Linearizing \([Ca^{2+}]\) and NO dynamics yields the values of \(\gamma\), k, \(\eta\), \(\mu\), \(\alpha\), \(\beta\), k1, \(\eta 1\), and \(\tau\).

$$\begin{aligned} \frac{dI_1}{d\bar{u1}^{(e)}}=\sum _{e=1}^N\left[ \bar{l^{(e)}}\frac{dI_1^{(e)}}{d\bar{u1}^{(e)}}\bar{L^{(e)}}^{T}\right] =0, \end{aligned}$$
(38)
$$\begin{aligned} \frac{dI_2}{d\bar{v1}^{(e)}}=\sum _{e=1}^N\left[ \bar{L^{(e)}}\frac{dI_2^{(e)}}{d\bar{v1}^{(e)}}\bar{L^{(e)}}^{T}\right] =0. \end{aligned}$$
(39)

where

$$\begin{aligned} \frac{dI_1^{(e)}}{d\bar{u1}^{(e)}}=\frac{dI_{ao1}^{(e)}}{d\bar{u1}^{(e)}}+\frac{d}{dt}\left[ \frac{dI_{bo1}^{(e)}}{d\bar{u1}^{(e)}}\right] +\frac{dI_{co1}^{(e)}}{d\bar{u1}^{(e)}}+\frac{dI_{do1}^{(e)}}{d\bar{u1}^{(e)}}-\frac{dI_{eo1}^{(e)}}{d\bar{u1}^{(e)}}-\frac{dI_{fo1}^{(e)}}{d\bar{u1}^{(e)}}, \end{aligned}$$
(40)
$$\begin{aligned} \frac{dI_2^{(e)}}{d\bar{v1}^{(e)}}=\frac{dI_{ao2}^{(e)}}{d\bar{v1}^{(e)}}+\frac{d}{dt}\left[ \frac{dI_{bo2}^{(e)}}{d\bar{v1}^{(e)}}\right] +\frac{dI_{co2}^{(e)}}{d\bar{v1}^{(e)}}+\frac{dI_{d2}^{(e)}}{d\bar{v1}^{(e)}}-\frac{dI_{eo2}^{(e)}}{d\bar{v1}^{(e)}}, \end{aligned}$$
(41)
$$\begin{aligned} \overline{M}^{(el)}= \begin{bmatrix} 0 &{} 0\\ . &{} .\\ 0 &{} 0\\ 1 &{} 0\\ 0 &{} 1\\ 0 &{} 0\\ . &{} .\\ 0 &{} 0 \end{bmatrix}, \overline{u}=\begin{bmatrix} u_{1}\\ u_{2}\\ u_{3}\\ .\\ .\\ .\\ u_{20}\\ u_{21} \end{bmatrix} and \; \overline{v}=\begin{bmatrix} v_{1}\\ v_{2}\\ v_{3}\\ .\\ .\\ .\\ v_{20}\\ v_{21} \end{bmatrix}, \end{aligned}$$
(42)
$$\begin{aligned} \begin{bmatrix} A \end{bmatrix}_{42 \times 42} \begin{bmatrix} \begin{bmatrix} \frac{\partial \bar{u}}{\partial t} \end{bmatrix}_{21 \times 1}\\ \begin{bmatrix} \frac{\partial \bar{v}}{\partial t} \end{bmatrix}_{21 \times 1} \end{bmatrix}+\begin{bmatrix} B \end{bmatrix}_{42 \times 42} \begin{bmatrix} \begin{bmatrix} \bar{u} \end{bmatrix}_{21 \times 1}\\ \begin{bmatrix} \bar{v} \end{bmatrix}_{21 \times 1} \end{bmatrix}=\begin{bmatrix} F \end{bmatrix}_{42 \times 1}. \end{aligned}$$
(43)

FEM and Crank-Nicolson methods are applied to the matrices A, B, and F. The resulting system of equations is solved by the Gaussian elimination method.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kothiya, A., Adlakha, N. Regulatory disturbances in the dynamical signaling systems of \(Ca^{2+}\) and NO in fibroblasts cause fibrotic disorders. J Biol Phys 50, 229–251 (2024). https://doi.org/10.1007/s10867-024-09657-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10867-024-09657-3

Keywords

Navigation