Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics

The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models.

[1]  Liesbet Geris,et al.  A Boolean network approach to developmental engineering , 2011 .

[2]  N. Plant,et al.  NUCLEAR RECEPTORS : THE CONTROLLING FORCE IN DRUG METABOLISM OF THE LIVER ? , 2022 .

[3]  S. Lamberts,et al.  Decreased ligand affinity rather than glucocorticoid receptor down-regulation in patients with endogenous Cushing's syndrome. , 2000, European journal of endocrinology.

[4]  F. Alimirah,et al.  Androgen receptor auto‐regulates its expression by a negative feedback loop through upregulation of IFI16 protein , 2006, FEBS letters.

[5]  Clare E. Giacomantonio,et al.  A Boolean Model of the Gene Regulatory Network Underlying Mammalian Cortical Area Development , 2010, PLoS Comput. Biol..

[6]  G. Stephanopoulos.  CHAPTER 1 – The Essence of Metabolic Engineering , 1998 .

[7]  Erwin P. Gianchandani,et al.  Correction: Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks , 2008, PLoS Computational Biology.

[8]  Aniruddha Datta,et al.  Using Boolean Logic Modeling of Gene Regulatory Networks to Exploit the Links Between Cancer and Metabolism for Therapeutic Purposes , 2016, IEEE Journal of Biomedical and Health Informatics.

[9]  J. Pascussi,et al.  Dexamethasone induces pregnane X receptor and retinoid X receptor-alpha expression in human hepatocytes: synergistic increase of CYP3A4 induction by pregnane X receptor activators. , 2000, Molecular pharmacology.

[10]  G. Stephanopoulos,et al.  Metabolic Engineering: Principles And Methodologies , 1998 .

[11]  B. Palsson,et al.  Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 , 1994, Applied and environmental microbiology.

[12]  Daniela Calvetti,et al.  Bayesian flux balance analysis applied to a skeletal muscle metabolic model. , 2007, Journal of theoretical biology.

[13]  B. Palsson,et al.  Regulation of gene expression in flux balance models of metabolism. , 2001, Journal of theoretical biology.

[14]  Nan Xiao,et al.  Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli , 2008, Bioinform..

[15]  P J Goss,et al.  Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[16]  N. Plant,et al.  Glucocorticoid-mediated induction of CYP3A4 is decreased by disruption of a protein: DNA interaction distinct from the pregnane X receptor response element. , 2002, Drug metabolism and disposition: the biological fate of chemicals.

[17]  V. Mootha,et al.  Metabolite Profiling Identifies a Key Role for Glycine in Rapid Cancer Cell Proliferation , 2012, Science.

[18]  Hans V. Westerhoff,et al.  Optimization of stress response through the nuclear receptor-mediated cortisol signalling network , 2013, Nature Communications.

[19]  Edward R. Dougherty,et al.  From Boolean to probabilistic Boolean networks as models of genetic regulatory networks , 2002, Proc. IEEE.

[20]  C. Gille,et al.  HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology , 2010, Molecular systems biology.

[21]  Ahmet Ay,et al.  Mathematical modeling of gene expression: a guide for the perplexed biologist , 2011, Critical reviews in biochemistry and molecular biology.

[22]  J. Cidlowski,et al.  Homologous down regulation of the glucocorticoid receptor: the molecular machinery. , 1993, Critical reviews in eukaryotic gene expression.

[23]  N. Plant,et al.  Transcriptional Regulation of the Human Pregnane-X Receptor , 2006, Drug metabolism reviews.

[24]  L. Moore,et al.  The Human Nuclear Xenobiotic Receptor PXR: Structural Determinants of Directed Promiscuity , 2001, Science.

[25]  Steffen Klamt,et al.  MUFINS: multi-formalism interaction network simulator , 2016, npj Systems Biology and Applications.

[26]  Shiew-Mei Huang,et al.  Predicting Drug–Drug Interactions: An FDA Perspective , 2009, The AAPS Journal.

[27]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[28]  S. Klamt,et al.  GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism , 2007, Genome Biology.

[29]  W. Cannon.  ORGANIZATION FOR PHYSIOLOGICAL HOMEOSTASIS , 1929 .

[30]  N. Plant,et al.  A PXR-Mediated Negative Feedback Loop Attenuates the Expression of CYP3A in Response to the PXR Agonist Pregnenalone-16α-Carbonitrile , 2011, PloS one.

[31]  H Matsuno,et al.  Hybrid Petri net representation of gene regulatory network. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[32]  Koichi Kobayashi,et al.  Optimal Control of Gene Regulatory Networks with Effectiveness of Multiple Drugs: A Boolean Network Approach , 2013, BioMed research international.

[33]  Peter Kraft,et al.  A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer , 2014, Nature Genetics.

[34]  Oakley Rh,et al.  Homologous down regulation of the glucocorticoid receptor: the molecular machinery. , 1993 .

[35]  L. Wienkers,et al.  Predicting in vivo drug interactions from in vitro drug discovery data , 2005, Nature Reviews Drug Discovery.

[36]  Jan Schellenberger,et al.  Use of Randomized Sampling for Analysis of Metabolic Networks* , 2009, Journal of Biological Chemistry.

[37]  D. Fell,et al.  Fat synthesis in adipose tissue. An examination of stoichiometric constraints. , 1986, The Biochemical journal.

[38]  Erwin P. Gianchandani,et al.  Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks , 2008, PLoS Comput. Biol..

[39]  K Rowland-Yeo,et al.  Basic Concepts in Physiologically Based Pharmacokinetic Modeling in Drug Discovery and Development , 2013, CPT: pharmacometrics & systems pharmacology.

[40]  Ruslan Medzhitov,et al.  Homeostasis, Inflammation, and Disease Susceptibility , 2015, Cell.

[41]  Federico Wadehn,et al.  A multiscale, model-based analysis of the multi-tissue interplay underlying blood glucose regulation in type I diabetes , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[42]  Tanmoy Das,et al.  Engineering three-dimensional topological insulators in Rashba-type spin-orbit coupled heterostructures , 2013, Nature Communications.

[43]  B. Palsson,et al.  Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods , 2012, Nature Reviews Microbiology.

[44]  E. R. Kloet,et al.  Two receptor systems for corticosterone in rat brain: microdistribution and differential occupation. , 1985, Endocrinology.

[45]  Sui Huang.  Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery , 1999, Journal of Molecular Medicine.

[46]  J Bernadette Moore,et al.  Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease. , 2014, World journal of gastroenterology.

[47]  Ines Thiele,et al.  Model-based dietary optimization for late-stage, levodopa-treated, Parkinson’s disease patients , 2016, npj Systems Biology and Applications.

[48]  Zachary A. King,et al.  Constraint-based models predict metabolic and associated cellular functions , 2014, Nature Reviews Genetics.

[49]  Steffen Borchers,et al.  Integrating Cellular Metabolism into a Multiscale Whole-Body Model , 2012, PLoS Comput. Biol..

[50]  Xiaobo Zhou,et al.  An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data , 2011, Bioinform..

[51]  Yufei Xiao,et al.  A Tutorial on Analysis and Simulation of Boolean Gene Regulatory Network Models , 2009, Current genomics.

[52]  Gil McVean,et al.  The 100,000 Genomes Project Protocol , 2017 .

[53]  Richard Banks,et al.  Qualitatively modelling and analysing genetic regulatory networks: a Petri net approach , 2007, Bioinform..

[54]  B. Palsson,et al.  Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. , 2000, Journal of theoretical biology.

[55]  D. Fell,et al.  A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks , 2000, Nature Biotechnology.

[56]  S. Huang,et al.  Genomics, complexity and drug discovery: insights from Boolean network models of cellular regulation. , 2001, Pharmacogenomics.

[57]  Monika Heiner,et al.  JAK/STAT signalling--an executable model assembled from molecule-centred modules demonstrating a module-oriented database concept for systems and synthetic biology. , 2012, Molecular bioSystems.

[58]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.