Skip to main content

Computational Investigation of Consistency and Performance of the Biochemical Network of the Malaria Parasite, Plasmodium falciparum

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Abstract

Malaria has been a problem in the public health sector and Sub-saharan African. The most prevalent symptoms of this disease is caused by Plasmodium falciparum, a blood borne pathogen, there has also been a disclosure of resistance to anti malaria drugs in Pf. An intimate process of acquiring insight to an organism’s metabolism is to analyze her network topology deploying computational techniques.

In this research, the Flux Balance Analysis (FBA) of the metabolism of malaria parasite, Plasmodium falciparum that has been converted to a System Biology Markup Language (SBML) format in another work (Segun et al. 2014) was used to predict the metabolic activities and to investigate the consistency of the multi-compartment biochemical metabolic network of the parasite using FASIMU software.

With a projected output in view, a flux-balance computation was first deployed on an energy model and redox metabolism of the human red blood cells to learn the internal structure of FASIMU, this was a simpler model compared to Plasmodium falciparum model. The results of the analysis generated a file that consists of the flux values, reaction identifiers, equilibrium constants adopted and the concentration values. It was also discovered that transporters conveyed metabolites among the various cellular compartments of the organism. Further results of the flux balance analysis of the compartmented Plasmodium falciparum metabolic network generated a comprehensive list of target metabolites indispensable to the growth of the organism, which have been confirmed by recent literature. It is evident that the results generated from this research represent a significant step towards discovering drug targets.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Segun, A.F., et al.: Computational biology and bioinformatics in Nigeria. PLOS Comput. Biol. 10(4) (2014). https://doi.org/10.1371/journal.pcbi.1003516

    Article  Google Scholar 

  • Francis, E.G.C.: History of the discovery of the Malaria parasites and their vectors. Parasites Vectors BioMed Central Part Springer Nature 3(5) (2010). https://doi.org/10.1186/1756-3305-3-5

    Article  Google Scholar 

  • Afonso, A., et al.: Malaria parasites can develop stable resistance to artemisinin but lack mutations in candidate genes atp (encoding the sarcoplasmic and endoplasmic reticulum Ca2+ ATPase), tctp, mdr1, and cg10. Antimicrob. Agents Chemother. 50(2), 480–489 (2006)

    Article  Google Scholar 

  • Andreas, H., Sabrina, H., Hermann-Georg, H.: Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks. Biomedcentral Bioinform. Syst. Biol. 1, 23 (2007)

    Google Scholar 

  • Andreas, H., Sabrina, H., Andreas, G., Christoph, G., Hermann-Georg, H.: FASIMU: flexible software for flux-balance computation series in large metabolic networks. Biomedcentral Bioinform. 12, 28 (2011)

    Google Scholar 

  • Baird, J.K.: Effectiveness of antimalarial drugs. N. Engl. J. Med. 352, 1565–1577 (2005)

    Article  Google Scholar 

  • Baird, J.K.: Evidence and implications of mortality associated with acute Plasmodium vivax malaria. Clin. Microbiol. Rev. 26(1), 36–57 (2013)

    Article  MathSciNet  Google Scholar 

  • WHO: Update on malaria terminology. http://www.who.int/malaria/mpac/mpac-sept2015-terminology-annex2.pdf?ua=1

  • Chalancon, G., Kruse, K., Babu, M.M.: Metabolic networks, structure and dynamics. In: Dubitzky, W., Wolkenhauer, O., Cho, K.H., Yokota, H. (eds.) Encyclopedia of Systems Biology. Springer, New York (2013). https://doi.org/10.1007/978-1-4419-9863-7

    Chapter  Google Scholar 

  • Sturchler, D., Sturchler, M.P.: Global epidemiology of malaria. In: Schlagenhauf, P. (ed.) Travelers’ Malaria, 2nd edn, pp. 9–35. BC Decker, Hamilton (2008)

    Google Scholar 

  • Orth, J.D., Bernhard, P.: Systematizing the generation of missing metabolic knowledge. Biotechnol. Bioeng. 107(3) (2010). https://doi.org/10.1002/bit.22844

    Article  Google Scholar 

  • Dal’Molin, C.G., Quek, L., Palfreyman, R.W., Brumley, S.M.: AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol. 152(2) (2010). https://doi.org/10.1104/pp.109148817

  • Hutmacher, D.W., Loessner, D., Rizzi, S., Kaplan, D.L., Mooney, D.J., Clement, J.A.: Can tissue engineering concepts advance tumor biology research? NCBI Pub Med Trends Biotechnol. 28(3), 125–133 (2010). https://doi.org/10.1016/j.tibtech.2009.12.001

    Article  Google Scholar 

  • Lelievre, B., Catherine, D., Mikael, D.: Qualitative and Quantitative Analysis of Chemotherapy Preparations. Euro. J. Hosp. Pharm. 16(4), 33–38 (2010)

    Google Scholar 

  • NIH: National Expert Panel on Diagnosis and Management (2010)

    Google Scholar 

  • Becker, K., Friedrich, A.W., Lubritz, G., Weilert, M., Peters, G., Von Eiff, C.: Prevalence of genes encoding pyrogenic toxin superantigens and exfoliative toxins among strains of Staphylococcus aureus isolated from blood and nasal specimens. J. Clin. Microbiol. 41, 1434–1439 (2003)

    Article  Google Scholar 

  • Ginsburg, H.: Progress in in silico functional genomics: the malaria Metabolic Pathways database. Trends Parasitol. 22(6), 238–240 (2006)

    Article  Google Scholar 

  • Hayton, K., Su, X.Z.: Genetic and biochemical aspects of drug resistance in malaria parasites. Curr. Drug Targets Infect. Disord. 4(1), 1–10 (2004)

    Article  Google Scholar 

  • Raman, K., Chandra, N.: Flux balance analysis of biological systems: applications and challenges. Brief Bioinform. PubMed 10(4), 435–449 (2009). https://doi.org/10.1093/bib/bbp011

    Article  Google Scholar 

  • Huthmacher, C., Hoppe, A., Bulik, S., Holzhutter, H.-G.: Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis. Biomedcentral Bioinform. 4, 120 (2010)

    Google Scholar 

  • Hyde, J.E.: Drug-resistant malaria-an insight. Fed. Eur. Biochem. Soc. J. 274(18), 4688–4698 (2007)

    Google Scholar 

  • Jeffrey, D.O., Ines, T., Bernhard, P.: What is flux balance analysis? Nat. Biotechnol. 28(3), 245–248 (2010)

    Article  Google Scholar 

  • Klann, M., Lapin, A., Reuss, M.: Agent-based simulation of reactions in the crowded and structured intracellular environment: Influence of mobility and location of the reactants. Biomedcentral Syst. Biol. 5(1), 71 (2011)

    Google Scholar 

  • Krogstad, D.J.: Malaria as a reemerging disease. Epidemiol. Rev. 18, 77–79 (1996)

    Article  Google Scholar 

  • Kuntzer, J., et al.: BN++ - a biological information system. J. Intergr. Bioinform. 3(2), 34 (2006)

    Google Scholar 

  • Poolman, M.G., Assmus, H.E., Fell, D.A.: Applications of metabolic modelling to plant metabolism. J. Exp. Bot. 55, 1177–1186 (2004)

    Article  Google Scholar 

  • Sabrina, H., Andreas, H., Hermann-Georg, H.: Pruning genome-scale metabolic models to consistent and functional networks. Genome Inform. 18, 308–319 (2007)

    Google Scholar 

  • Schuster, S., Dandekar, T., Fell, D.: Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol. 17(2), 53–60 (1999)

    Article  Google Scholar 

  • Schuster, S., Fell, D.A., Dandekar, T.: A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat. Biotechnol. 18(3), 326–332 (2000)

    Article  Google Scholar 

  • Schuster, S., Holzutter, H.G.: Use of mathematical models for predicting the metabolic effect of large-scale enzyme activity alterations. Application to enzyme deficiencies of red blood cells. Eur. J. Biochem. 229(2), 403–418 (1995)

    Article  Google Scholar 

  • Vinay, S.K., Madhukar, S.D., Costas, D.M.: Optimization based automated curation of metabolic reconstructions. Biomedcentral Bioinform. 8, 212 (2007)

    Google Scholar 

  • World Health Organization: Global Burden of Disease. WHO Library Cataloguing-in-Publication Data (2004). ISBN 978 92 4 156371 0

    Google Scholar 

  • World Health Organization: Malaria cases and deaths in Africa World Malaria Report. WHO Library Cataloguing-in-Publication Data, Geneva (2008). ISBN 978 92 4 156371 0

    Google Scholar 

  • BioCyc. Summary of Plasmodium falciparum, Strain 3D7, version 16.5. BioCyc: Database Collection (2012). http://biocyc.org/PLASMO/organism-summary?object=PLASMO. Accessed 7 Feb 2013

  • BioMed Central: Flux Balance Analysis: FASIMU Software (2013). www.biomedcentral.com. Accessed July 2013

  • Look And Feel Java Platform SE 7. Oracle Documentation (2012). http://docs.oracle.com/javase/7/docs/api/javax/swing/LookAndFeel.html. Accessed 26 May 2012

  • NaTHNaC. Health information sheet on malaria (2010). http://www.nathnac.org/pro/factsheets/malaria.htm

  • Software Platforms for Systems Biology SBML.org, December 2010. http://sbml.org/Events/Workshops/The_1st_Workshop_on_Software_Platforms_ for_Systems_Biology

  • U.S. Department of Health and Human Services. National Institutes of Health National Institute of Allergy and Infectious Diseases NIH Publication No. 07-7139 (2007). www.niaid.nih.gov

  • Xampp. Version 1.8.2/PHP 5.4.27 (2013). https://www.apachefriends.org/download.html. Accessed 16 Mar 2013

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roseline Ogundokun .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Adebiyi, M. et al. (2019). Computational Investigation of Consistency and Performance of the Biochemical Network of the Malaria Parasite, Plasmodium falciparum. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24308-1_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24307-4

  • Online ISBN: 978-3-030-24308-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics