Abstract
The usefulness of mathematical models for the biological regulatory networks relies on the predictive capability of the models in order to suggest interesting hypotheses and suitable biological experiments. All mathematical frameworks dedicated to biological regulatory networks must manage a large number of abstract parameters, which are not directly measurable in the cell. The cornerstone to establish predictive models is the identification of the possible parameter values. Formal frameworks involve qualitative models with discrete values and computer-aided logic reasoning. They can provide the biologists with an automatic identification of the parameters via a formalization of some biological knowledge into temporal logic formulas. For pedagogical reasons, we focus on gene regulatory networks and develop a qualitative model of the detoxification of benzo[a]pyrene in human cells to illustrate the approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kier LB, Bonchev D, Buck GA (2005) Modeling biochemical networks: a cellular-automata approach. Chem Biodivers 2:233–243
Hoehme S, Drasdo D (2010) A cell-based simulation software for multicellular systems. Bioinformatics 26(20):2641–2642
Poudret M, Comet J-P, Le Gall P et al (2008) Topology-based abstraction of complex biological systems: application to the Golgi apparatus. Theory Biosci 127:79–88
Eungdamrong NJ, Iyengar R (2004) Modeling cell signaling networks. Biol Cell 96:355–362
Schuster S, Hilgetag C, Woods JH et al (2002) Elementary flux modes in biochemical reaction systems: algebraic properties, validated calculation procedure and example from nucleotide metabolism. J Math Biol 45:153–181
Thomas R, d’Ari R (1990) Biological feedback. CRC, Boca Raton
Bostrom CE, Gerde P, Hanberg A et al (2002) Cancer risk assessment, indicators, and guidelines for polycyclic aromatic hydrocarbons in the ambiant air. Environ Health Perspect 110(suppl 3):451–488
Phillips DH (1999) Polycyclic aromatic hydrocarbons in the diet. Mutat Res 443(1–2):39–147
Schmidt JV, Bradfield CA (1996) Ah receptor signaling pathways. Annu Rev Cell Dev Biol 12:55–89
Hankinson O (1995) The aryl hydrocarbon receptor complex. Annu Rev Pharmacol Toxicol 35:307–340
Gu YZ, Hogenesch JB, Bradfield CA (2000) The PAS superfamily: sensors of environmental and developmental signals. Annu Rev Pharmacol Toxicol 40:519–561
Nebert DW, Roe AL, Dieter MZ et al (2000) Role of the aromatic hydrocarbon receptor and [Ah] gene battery in the oxidative stress response, cell cycle control, and apoptosis. Biochem Pharmacol 59:65–85
Nebert DW, Dalton TP, Okey AB et al (2004) Role of aryl hydrocarbon receptor-mediated induction of the CYP1 enzymes in environmental toxicity and cancer. J Biol Chem 279(23):23847–23850
Nebert DW, Vasiliou V (2004) Analysis of the glutathione S-transferase (GST) gene family. Hum Genomics 1(6):460–464
Nioi P, Hayes JD (2004) Contribution of NAD(P)H:quinine oxidoreductase 1 to protection against carcinogenesis, and regulation of its gene by the Nrf2 basic-region leucine zipper and the arylhydrocarbon receptor basic helix–loop–helix transcription factors. Mutat Res 555(1–2):149–171
Yoshinari K, Okino N, Sato T et al (2006) Induction of detoxifying enzymes in rodent white adipose tissue by aryl hydrocarbon receptor agonists and antioxidants. Drug Metab Dispos 4:1081–1089
Wills LP, Zhu S, Willett KL et al (2009) Effect of CYP1A inhibition on the biotransformation of benzo[a]pyrene in two populations of Fundulus heteroclitus with different exposure histories. Aquat Toxicol 92(3):195–201
Parkinson A (1996) Biotransformation of xenobiotics. In: Klaassen CD (ed) Casarett and Doull’s toxicology: the basic science of poisons. McGraw-Hill, New York
Yang SK (1988) Stereoselectivity of cytochrome P-450 isozyemes and epoxide hydrolase in the metabolism of polycylic aromatic hydrocarbons. Biochem Pharmocol 37:61–70
Köhle C, Bock KW (2007) Coordinate regulation of phase I and II xenobiotic metabolism by the Ah receptor and Nrf2. Biochem Pharmacol 73:1853–1862
de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9(1):67–103
Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361
Tyson JJ, Othmer HG (1978) The dynamics of feedback control circuits in biochemical pathways. Prog Theor Biol 5:1–62
Bernot G, Comet J-P, Richard A et al (2004) Application of formal methods to biological regulatory networks: extending Thomas’ asynchronous logical approach with temporal logic. J Theor Biol 229(3):339–347
Khalis Z, Comet J-P, Richard A et al (2009) The SMBioNet method for discovering models of gene regulatory networks. Genes Genomes Genomics 3(special issue 1):15–22
Emerson EA (1990) Temporal and modal logic. In: Van Leeuwen J (ed) Handbook of theoretical computer science. MIT Press, Cambridge
Cimatti A, Clarke EM, Giunciglia EF et al (2002) NuSMV 2: An open source tool for symbolic model checking. In: Proceeding of international conference on computer-aided verification (CAV 2002), pp 27–31
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Bernot, G., Comet, JP., Faverney, C.Rd. (2013). Regulatory Networks. In: Reisfeld, B., Mayeno, A. (eds) Computational Toxicology. Methods in Molecular Biology, vol 930. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-059-5_9
Download citation
DOI: https://doi.org/10.1007/978-1-62703-059-5_9
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-058-8
Online ISBN: 978-1-62703-059-5
eBook Packages: Springer Protocols