Skip to main content
SpringerLink
Log in

Design and development of cancer regulatory system by modeling electrical network of gene

  • Technical Paper
  • Published:
Microsystem Technologies Aims and scope Submit manuscript

Abstract

An electrical network system for prostate gene is realized using individual amino acid model to reduce the computational load of existing gene system. A control strategy is also incorporated along with the reduced gene system to control the progression of cancer gene. The amino acids Tyrosine, Phenylalanine, Glutamine and Methionine are treated as inhibitor and Tryptophan as booster element in the cancer regulatory system, which control the growth of cancer. The system performance is analyzed by measuring percentage of error with frequency. The accuracy of the model is tested on 35 gene samples associated with prostate cells and achieved 96.7 % accuracy. The main focus of the paper is to identify the cancerous and healthy prostate genes, and regulate the specific amino acid for inhibiting the growth and metastasis of prostate cancer. The electrical response of the model is truly matched with biological characteristics beyond the frequency of 50 kHz.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Abbreviations

DNA:

Deoxyribo nucleic acid

RNA:

Ribo nucleic acid

NCBI:

National center for biotechnology information

CGAP:

Cancer genome anatomy project

SISO:

Single input single output

RL:

Load resistance

MOR:

Model order reduction

References

  • Alfinito E, Pennetta C, Reggiani L (2008) A network model to investigate structural and electrical properties of proteins. Nanotechnol 19:1–25

    Article  Google Scholar 

  • Anastassiou D (2001) Genomic signal processing. IEEE Signal Process Mag 18:8–20

    Article  Google Scholar 

  • Barman S, Roy M, Biswas S, Saha S (2011) Prediction of cancer cell using digital signal processing. Ann Fac Eng Hunedoaras. Int J Eng 9:91–95

    Google Scholar 

  • Cancer Genome Anatomy Project. http://cgap.nci.nih.gov. Accessed 2 May 2014

  • Daryanani G, Resh J (1969) Foster distributed-lumped network synthesis. IEEE Trans Circuit Theory 16:429–434

    Article  Google Scholar 

  • Das D, Mitra CK (2011) Signal in the promoter regions of several cancerous genes. Indian J Biochem Biophys 48:14–21

    Google Scholar 

  • Fortuna L, Nunnari G, Gallo A (1992) Model order reduction techniques with applications in electrical engineering. Springer, London

    Book  Google Scholar 

  • Fu YM, Zhang H, Ding M, Li YQ, Fu X, Yu ZX, Meadows GG (2006) Selective amino acid restriction targets mitochondria to induce apoptosis of androgen-independent prostate cancer cells. J Cell Physio 209:522–534

    Article  Google Scholar 

  • Fu YM, Lin H, Liu X, Fang W, Meadows GG (2010) Cell death of prostate cancer cells by specific amino acid restriction depends on alterations of glucose metabolism. J Cell Physio 224:491–500

    Article  Google Scholar 

  • Genecards. http://www.genecards.org. Accessed 24 Apr 2014

  • Glover K (1984) All optimal Hankel-norm approximations of linear multivariable systems and their L, ∞-error bounds. Int J Control 39:1115–1193

    Article  MathSciNet  MATH  Google Scholar 

  • Hodzic V, Newcomb RW (2007) Modeling of the electrical conductivity of DNA. IEEE Trans Circuits Syst I, Reg Papers 54:2360–2364. http://www.cancer.org/cancer/prostatecancer/detailedguide/prostate-cancer-key-statistics. Accessed 21 Apr 2014

    Article  Google Scholar 

  • Khadraoui S, Nounou HN, Nounou MN, Datta A, Bhattacharyya SP (2013) A measurement-based approach for designing reduced-order controllers with guaranteed bounded error. Int J Control 86:1586–1596

    Article  MathSciNet  MATH  Google Scholar 

  • Khammash M, El-Samad H (2004) Systems biology: from physiology to gene regulation. IEEE Control Syst Mag 24:62–76

    Article  MathSciNet  Google Scholar 

  • Knockaert L, Elizabeth RS (2014) Model order reduction of time-delay systems using a laguerre expansion technique. IEEE Trans Circuits Syst I, Reg Papers 61:1815–1823

    Article  Google Scholar 

  • Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132

    Article  Google Scholar 

  • Lengauer C, Kinzler KW, Vogelstein B (1998) Genetic instabilities in human cancers. Nature 396:643–649

    Article  Google Scholar 

  • Liu Y, Anderson BD (1989) Singular perturbation approximation of balanced systems. Int J Control 50:1379–1405

    Article  MathSciNet  MATH  Google Scholar 

  • Loeb KR, Loeb LA (2000) Significance of multiple mutations in cancer. Carcinogenesis 21:379–385

    Article  Google Scholar 

  • Marshall R (2010) Modeling DNA/RNA strings using resistor–capacitor (RC) ladder networks. Comput J 53:644–660

    Article  Google Scholar 

  • McClellan DA (2012) Detecting molecular selection on single amino acid replacements. Int J Bioinform Res Appl 8:67–80

    Article  Google Scholar 

  • National Cancer Institute. http://prevention.cancer.gov/funding/recently-funded/ca04004/1R01CA101035-01A1. Accessed 12 May 2014

  • National Center For Biotechnology Information. http://www.ncbi.nlm.nih.gov. Accessed 21 Apr 2014

  • Roy T, Barman S (2014b) Electrical system model realization to study genetic behavior of cancer and normal homo sapiens genes. In: 1st international conference on microelectronics, circuits and systems (micro) 2014. Kolkata, India, pp 25–32. ISBN: 81-85824-46-0

  • Roy T, Das S, Barman S (2014a) Electrical network modeling of amino acid string and its application in cancer cell prediction. In: Intelligent computing, networking, and informatics. Springer, India, pp 293–301. doi:10.1007/978-81-322-1665-0_28

  • Sampath G (2006) RLC(M) circuit models of protein structure: analysis, visualization, shape synthesis, and pattern matching. In: 40th annual conference on information sciences and systems. Princeton, NJ, pp 1623–1628

  • Schroecksnadel K, Winkler C, Fuith LC, Fuchs D (2005) Tryptophan degradation in patients with gynecological cancer correlates with immune activation. Cancer Lett 223:323–329

    Article  Google Scholar 

  • Shobha BN, Muniraj NJR (2014) Design, modeling and performance analysis of carbon nanotube with DNA strands as biosensor for prostate cancer. Microsys Technol 21:791–800

    Article  Google Scholar 

  • Skelton R, Oliveira M, Han J (2004) Systems modeling and model reduction. Invited chapter in the handbook of smart systems and materials, Institute of Physics

  • Stranzl T, Larsen MV, Lund O, Nielsen M, Brunak S (2012) The cancer exome generated by alternative mRNA splicing dilutes predicted HLA class I epitope density. PLoS One 7:1–8

    Article  Google Scholar 

  • Sucher R, Kurz K, Weiss G, Margreiter R, Fuchs D, Brandacher G (2010) IDO-mediated tryptophan degradation in the pathogenesis of malignant tumor disease. Int J Tryptophan Res 3:113–120

    Google Scholar 

  • The AP John Institute For Cancer Research. https://www.apjohncancerinstitute.org/caat-protocol. Accessed 24 June 2014

  • Uyttenhove C, Pilotte L, Théate I, Stroobant V, Colau D, Parmentier N et al (2003) Evidence for a tumoral immune resistance mechanism based on tryptophan degradation by indoleamine 2, 3-dioxygenase. Nat Med 9:1269–1274

    Article  Google Scholar 

  • Vaidyanathan PP (2004) Genomics and proteomics: a signal processor’s tour. IEEE Circuits Syst Mag 4:6–29

    Article  Google Scholar 

  • Varga A (1991) Balancing free square-root algorithm for computing singular perturbation approximations. In: Proc. CDC′91, Brighton, UK, pp 1062–1065

  • Vogelstein B, Kinzler KW (2004) Cancer genes and the pathways they control. Nat Med 10:789–799

    Article  Google Scholar 

Download references

Acknowledgments

The author T. Roy would like to thank University Grants Commission (UGC), India for providing her scholarship (No. F1-17.1/2012-13/RGNF-2012-13-SC-WES-18752) and also the authors thank Centre for Research in Nanoscience and Nanotechnology, University of Calcutta for providing research facilities.

Author information

Authors and Affiliations

  1. Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, 700009, India

    Tanusree Roy & Soma Barman

Authors
  1. Tanusree Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soma Barman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soma Barman.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, T., Barman, S. Design and development of cancer regulatory system by modeling electrical network of gene. Microsyst Technol 22, 2641–2653 (2016). https://doi.org/10.1007/s00542-015-2548-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00542-015-2548-x

Keywords

Search

Navigation