Skip to main content
SpringerLink
Log in

Stoffwechsel modellieren und neue Antibiotika finden

  • Chapter
  • First Online:
Bioinformatik

  • 7139 Accesses

Zusammenfassung

Metabolische Modellierung erlaubt es, den Stoffwechsel im Detail zu analysieren. Biochemisches Wissen und Datenbanken wie KEGG bestimmen den Satz aller beteiligter Enzyme. Sodann kann man berechnen, welche Stoffwechselwege und Enzymketten die Metabolite in einem Netzwerk im Gleichgewicht halten (Flux-Balance-Analyse), welche davon auch nicht mehr zerlegbar sind (Elementarmodenanalyse) und welche davon ausreichen, um alle realen Stoffwechselsituationen durch die Kombination von wenigen reinen Flux-Moden darzustellen (extreme pathway analysis). Um die Flussstärke zu berechnen, braucht man weitere Daten, z. B. Genexpressionsdaten und Software (z. B. YANA-Programme). Weiterführende Analysen betrachten die metabolische Kontrolle (metabolische Kontrolltheorie) und beschreiben die Geschwindigkeiten (Kinetik) der beteiligten Enzyme genauer. Dies erlaubt, den Stoffwechsel besser zu beschreiben und zu verstehen, essentielle Gene und resultierende Antibiotika ebenso vorherzusagen wie Stoffwechselantworten, etwa beim Tumorwachstum.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 29.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Literatur

  • Bergmann FT, Sahle S, Zimmer C (2016) Piecewise parameter estimation for stochastic models in COPASI. Bioinformatics 32(10):1586–1588. doi:10.1093/bioinformatics/btv759 (PubMed PMID: 26787664)

  • Cecil A, Rikanović C, Ohlsen K et al (2011) Modelling antibiotic and cytotoxic effects of the dimeric isoquinoline IQ-143 on metabolism and its regulation in Staphylococcus aureus, Staphylococcus epidermidis and human cells. Genome Biol 12(3):R24

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cecil A, Ohlsen K, Menzel T et al (2015) Modelling antibiotic and cytotoxic isoquinoline effects in Staphylococcus aureus, Staphylococcus epidermidis and mammalian cells. Int J Med Microbiol 305(1):96–109

    Google Scholar 

  • Dandekar T, Fieselmann A, Majeed S et al (2014) Software applications toward quantitative metabolic flux analysis and modeling. Brief Bioinform 15(1):91–107. doi:10.1093/bib/bbs065

    Article  PubMed  Google Scholar 

  • Kamp A von, Schuster S (2006) Metatool 5.0: fast and flexible elementary modes analysis. Bioinformatics 22(15):1930–1931

    Article  Google Scholar 

  • Kent E, Hoops S, Mendes P (2012) Condor-COPASI: high-throughput computing for biochemical networks. BMC Syst Biol 6:91. doi:10.1186/1752-0509-6-91

    Article  PubMed  PubMed Central  Google Scholar 

  • Kühnel M, Mayorga LS, Dandekar T et al (2008) Modelling phagosomal lipid networks that regulate actin assembly. BMC Syst Biol 2:107. doi:10.1186/1752-0509-2-107

    Article  PubMed  PubMed Central  Google Scholar 

  • Mavrovouniotis ML, Stephanopoulos G, Stephanopoulos G (1990) Computer-aided synthesis of biochemical pathways. Biotechnol Bioeng 36:1119–1132

    Article  CAS  PubMed  Google Scholar 

  • Orth JD, Fleming RM, Palsson BØ (2010) Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide. EcoSal Plus 4(1). doi: 10.1128/ecosalplus.10.2.1

  • Schuster R, Schuster S (1993) Refined algorithm and computer program for calculating all non–negative fluxes admissible in steady states of biochemical reaction systems with or without some flux rates fixed. Comput Appl Biosci 9(1):79–85

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Schwarz R, Musch P, Kamp A von et al (2005) YANA – a software tool for analyzing flux modes, gene-expression and enzyme activities. BMC Bioinformatics 6:135 (PubMed PMID: 15929789; PubMed Central PMCID: PMC1175843)

    Google Scholar 

  • Schwarz R, Liang C, Kaleta C et al (2007) Integrated network reconstruction, visualization and analysis using YANAsquare. BMC Bioinformatics 8:313 (PubMed PMID: 17725829; PubMed Central PMCID: PMC2020486)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Bioinformatics, Universität Würzburg, Würzburg, Deutschland

    Thomas Dandekar & Meik Kunz

Authors
  1. Thomas Dandekar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meik Kunz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Dandekar .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer-Verlag GmbH Deutschland

About this chapter

Cite this chapter

Dandekar, T., Kunz, M. (2017). Stoffwechsel modellieren und neue Antibiotika finden. In: Bioinformatik. Springer Spektrum, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54698-7_4

Download citation

Publish with us

Policies and ethics

Search

Navigation