Network models in drug discovery and regenerative medicine.

Network motifs and modelling paradigms are attracting increasing attention as modelling tools in drug design and development, and in regenerative medicine. There is a gradual but inexorable convergence between these hitherto disparate disciplines. This review summarizes some very recent work in these areas, leading to an understanding of the complementary roles networks play and factors driving this convergence: network paradigms can be excellent ways of modelling and understanding drug molecules and their action, an understanding of the robustness and vulnerabilities of biological targets may improve the efficacy of drug design and discovery, drug design has an increasingly large role to play in directing stem cell properties, stem cell regulatory networks can be modelled in useful ways using network models at a reasonable level of scale, and the network tools of drug design are also very useful for the design of biomaterials used in regenerative medicine.

[1]  Julio Caballero,et al.  2D Autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks. , 2006, Bioorganic & medicinal chemistry.

[2]  David A Winkler,et al.  Application of neural networks to large dataset QSAR, virtual screening, and library design. , 2002, Methods in molecular biology.

[3]  D. Knight,et al.  A new approach to the rationale discovery of polymeric biomaterials. , 2007, Biomaterials.

[4]  Henry Yang,et al.  Mechanisms controlling embryonic stem cell self-renewal and differentiation. , 2006, Critical reviews in eukaryotic gene expression.

[5]  D. Knight,et al.  Prediction of Fibrinogen Adsorption for Biodegradable Polymers: Integration of Molecular Dynamics and Surrogate Modeling. , 2007, Polymer.

[6]  David A Winkler,et al.  Neural networks as robust tools in drug lead discovery and development , 2004, Molecular biotechnology.

[7]  Daniel A. Lidar,et al.  Is the Geometry of Nature Fractal? , 1998, Science.

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

[9]  Wei Sun,et al.  Application of Bayesian Regularized BP Neural Network Model for Trend Analysis, Acidity and Chemical Composition of Precipitation in North Carolina , 2006 .

[10]  D Sardari,et al.  Applications of artificial neural network in AIDS research and therapy. , 2002, Current pharmaceutical design.

[11]  R. Rojas,et al.  Glass transition temperature prediction of polymers through the mass-per-flexible-bond principle. , 2007, Polymer.

[12]  Johann Gasteiger,et al.  Neural networks and genetic algorithms in drug design , 2001 .

[13]  Igor V. Tetko,et al.  Virtual Computational Chemistry Laboratory – Design and Description , 2005, J. Comput. Aided Mol. Des..

[14]  Jure Zupan,et al.  Neural networks in chemistry , 1993 .

[15]  István A. Kovács,et al.  How to design multi-target drugs , 2007, Expert opinion on drug discovery.

[16]  Julio Caballero,et al.  QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-alpha-phenylsulfonylacetamide derivatives. , 2007, Bioorganic & medicinal chemistry.

[17]  William J. Welsh,et al.  Using Surrogate Modeling in the Prediction of Fibrinogen Adsorption onto Polymer Surfaces , 2004, J. Chem. Inf. Model..

[18]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[19]  Mário A. T. Figueiredo Adaptive Sparseness for Supervised Learning , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Julio Caballero,et al.  Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks , 2006, Journal of molecular modeling.

[21]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[22]  J. Doyle,et al.  Reverse Engineering of Biological Complexity , 2002, Science.

[23]  Ting Chen,et al.  Ensemble Feature Selection: Consistent Descriptor Subsets for Multiple QSAR Models , 2007, J. Chem. Inf. Model..

[24]  Luke E. K. Achenie,et al.  A network model for gene regulation , 2007, Comput. Chem. Eng..

[25]  R. Sharma,et al.  Stem cell fate specification: role of master regulatory switch transcription factor PU.1 in differential hematopoiesis. , 2005, Stem cells and development.

[26]  Robin Lovell-Badge,et al.  The future for stem cell research , 2001, Nature.

[27]  Ş. Niculescu Artificial neural networks and genetic algorithms in QSAR , 2003 .

[28]  Aris Floratos,et al.  QSAR in grossly underdetermined systems : Opportunities and issues Regression , 2001 .

[29]  W. Tong,et al.  Quantitative structure‐activity relationship methods: Perspectives on drug discovery and toxicology , 2003, Environmental toxicology and chemistry.

[30]  Gisbert Schneider,et al.  Neural networks are useful tools for drug design , 2000, Neural Networks.

[31]  Leilei Pan,et al.  Intelligent Computation of Moisture Content in Shrinkable Biomaterials , 2007 .

[32]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[33]  P. Robson,et al.  Transcriptional Regulation of Nanog by OCT4 and SOX2* , 2005, Journal of Biological Chemistry.

[34]  H Ichikawa,et al.  Neural networks applied to pharmaceutical problems. I. Method and application to decision making. , 1989, Chemical & pharmaceutical bulletin.

[35]  G. Church,et al.  Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae , 2001, Nature Genetics.

[36]  N. Goldenfeld,et al.  Coarse-graining of cellular automata, emergence, and the predictability of complex systems. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  A. Barabasi,et al.  Human disease classification in the postgenomic era: A complex systems approach to human pathobiology , 2007, Molecular systems biology.

[38]  Jens Sadowski,et al.  The Use of Self-organizing Neural Networks in Drug Design , 2002 .

[39]  J. Gasteiger,et al.  Neural networks as data mining tools in drug design , 2003 .

[40]  D. Manallack,et al.  Neural networks in drug discovery: Have they lived up to their promise? , 1999 .

[41]  Stuart A. Kauffman,et al.  The ensemble approach to understand genetic regulatory networks , 2004 .

[42]  Julio Caballero,et al.  Modeling of acetylcholinesterase inhibition by tacrine analogues using Bayesian-regularized Genetic Neural Networks and ensemble averaging , 2006, Journal of enzyme inhibition and medicinal chemistry.

[43]  Bruno Bienfait Applications of High-Resolution Self-Organizing Maps to Retrosynthetic and QSAR Analysis , 1994, J. Chem. Inf. Comput. Sci..

[44]  David A. Winkler,et al.  Neural networks in ADME and toxicity prediction , 2004 .

[45]  R. Ozawa,et al.  A comprehensive two-hybrid analysis to explore the yeast protein interactome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[46]  R. García-Domenech,et al.  Some new trends in chemical graph theory. , 2008, Chemical reviews.

[47]  E. Rothenberg,et al.  Transcriptional regulation of lymphocyte lineage commitment , 1999, BioEssays : news and reviews in molecular, cellular and developmental biology.

[48]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[49]  David W. Opitz,et al.  Use of Statistical and Neural Net Approaches in Predicting Toxicity of Chemicals , 2000, J. Chem. Inf. Comput. Sci..

[50]  Walter Cedeño,et al.  On the Use of Neural Network Ensembles in QSAR and QSPR , 2002, J. Chem. Inf. Comput. Sci..

[51]  J. Doyle,et al.  Bow Ties, Metabolism and Disease , 2022 .

[52]  Russell Greiner,et al.  Learning Bayesian Belief Network Classifiers: Algorithms and System , 2001, Canadian Conference on AI.

[53]  R. Cramer,et al.  Recent advances in comparative molecular field analysis (CoMFA). , 1989, Progress in clinical and biological research.

[54]  Doyle Knight,et al.  QSAR Models for the Analysis of Bioresponse Data from Combinatorial Libraries of Biomaterials , 2005 .

[55]  Maykel Pérez González,et al.  Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches. , 2006, Bioorganic & medicinal chemistry.

[56]  Janet Wiles,et al.  A Gene Network Model for Developing Cell Lineages , 2005, Artificial Life.

[57]  F. Burden,et al.  A quantitative structure--activity relationships model for the acute toxicity of substituted benzenes to Tetrahymena pyriformis using Bayesian-regularized neural networks. , 2000, Chemical research in toxicology.

[58]  Julio Caballero,et al.  QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties , 2007, Journal of molecular modeling.

[59]  Guangjin Pan,et al.  Nanog and transcriptional networks in embryonic stem cell pluripotency , 2007, Cell Research.

[60]  H. Kitano A robustness-based approach to systems-oriented drug design , 2007, Nature Reviews Drug Discovery.

[61]  Ajay A unified framework for using neural networks to build QSARs. , 1993, Journal of medicinal chemistry.

[62]  E. Kawasaki,et al.  Comparisons between transcriptional regulation and RNA expression in human embryonic stem cell lines. , 2006, Stem cells and development.

[63]  D. Fell,et al.  The small world inside large metabolic networks , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[64]  H. Herrmann,et al.  Self-organized criticality on small world networks , 2001, cond-mat/0110239.

[65]  Bernhard O Palsson,et al.  The global transcriptional regulatory network for metabolism in Escherichia coli exhibits few dominant functional states. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[66]  D. Knight,et al.  Predicting fibrinogen adsorption to polymeric surfaces in silico: a combined method approach , 2005 .

[67]  Eric Mjolsness,et al.  Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks , 2006, PLoS Comput. Biol..

[68]  A. Hopkins Network pharmacology , 2007, Nature Biotechnology.

[69]  S. Pal,et al.  Bioinformatics in neurocomputing framework , 2005 .

[70]  John Finnigan The science of complex systems , 2005 .

[71]  D Husmeier,et al.  Reverse engineering of genetic networks with Bayesian networks. , 2003, Biochemical Society transactions.

[72]  John P. Overington,et al.  Can we rationally design promiscuous drugs? , 2006, Current opinion in structural biology.

[73]  A. Barabasi,et al.  A Protein–Protein Interaction Network for Human Inherited Ataxias and Disorders of Purkinje Cell Degeneration , 2006, Cell.

[74]  Dirk Husmeier,et al.  Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[75]  J. Topliss,et al.  Chance correlations in structure-activity studies using multiple regression analysis , 1972 .

[76]  Jiang Zhu,et al.  Molecular pathways regulating the self-renewal of hematopoietic stem cells. , 2004, Experimental hematology.

[77]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[78]  David J. Livingstone,et al.  The Use of Artificial Neural Networks in QSAR , 1992 .

[79]  M. Tyers,et al.  From large networks to small molecules. , 2004, Current opinion in chemical biology.

[80]  Dave Winkler The broader applications of neural and genetic modelling methods. , 2001, Drug discovery today.

[81]  Ying Liu,et al.  A Comparative Study on Feature Selection Methods for Drug Discovery , 2004, J. Chem. Inf. Model..

[82]  Johann Gasteiger,et al.  Self-organizing maps for identification of new inhibitors of P-glycoprotein. , 2007, Journal of medicinal chemistry.

[83]  Magdalena Zernicka-Goetz,et al.  Cleavage pattern and emerging asymmetry of the mouse embryo , 2005, Nature Reviews Molecular Cell Biology.

[84]  Riccardo Leonardi,et al.  Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach , 2007, J. Comput. Aided Mol. Des..

[85]  M G Ford,et al.  Selecting compounds for focused screening using linear discriminant analysis and artificial neural networks. , 2004, Journal of molecular graphics & modelling.

[86]  B. Gardiner,et al.  Transcriptional analysis of early lineage commitment in human embryonic stem cells , 2007, BMC Developmental Biology.

[87]  Masanori Arita,et al.  Scale-freeness and biological networks. , 2005, Journal of biochemistry.

[88]  Min Xu,et al.  Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-a case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake. , 2005, Journal of environmental sciences.

[89]  Ziding Zhang,et al.  Similarity networks of protein binding sites , 2005, Proteins.

[90]  F. Burden,et al.  New QSAR Methods Applied to Structure—Activity Mapping and Combinatorial Chemistry. , 1999 .

[91]  Frank R. Burden,et al.  Predictive Human Intestinal Absorption QSAR Models Using Bayesian Regularized Neural Networks , 2005 .

[92]  S. Orkin,et al.  Chipping away at the Embryonic Stem Cell Network , 2005, Cell.

[93]  Daniel C. Weaver Applying data mining techniques to library design, lead generation and lead optimization. , 2004, Current opinion in chemical biology.

[94]  Tomoo Aoyama,et al.  Obtaining the correlation indices between drug activity and structural parameters using a neural network , 1991 .

[95]  F. Burden,et al.  Robust QSAR models using Bayesian regularized neural networks. , 1999, Journal of medicinal chemistry.

[96]  J Gasteiger,et al.  Checking the projection display of multivariate data with colored graphs. , 1997, Journal of molecular graphics & modelling.

[97]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[98]  Jiang Zhu,et al.  Hematopoietic cytokines, transcription factors and lineage commitment , 2002, Oncogene.

[99]  M. Xiong,et al.  Identification of genetic networks. , 2004, Genetics.

[100]  J. Rich,et al.  Malignant glioma drug discovery – targeting protein kinases , 2007, Expert opinion on drug discovery.

[101]  S. Strogatz Exploring complex networks , 2001, Nature.

[102]  David A Winkler,et al.  Modelling blood-brain barrier partitioning using Bayesian neural nets. , 2004, Journal of molecular graphics & modelling.

[103]  Igor V. Tetko,et al.  Data modelling with neural networks: Advantages and limitations , 1997, J. Comput. Aided Mol. Des..

[104]  E. Stüssi,et al.  Prediction of elasticity constants in small biomaterial samples such as bone. A comparison between classical optimization techniques and identification with artificial neural networks , 2004, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[105]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[106]  Xi Chen,et al.  Reciprocal Transcriptional Regulation of Pou5f1 and Sox2 via the Oct4/Sox2 Complex in Embryonic Stem Cells , 2005, Molecular and Cellular Biology.

[107]  Frank R Burden,et al.  Broad-based quantitative structure-activity relationship modeling of potency and selectivity of farnesyltransferase inhibitors using a Bayesian regularized neural network. , 2004, Journal of medicinal chemistry.

[108]  Frank R. Burden,et al.  Robust QSAR Models from Novel Descriptors and Bayesian Regularised Neural Networks , 2000 .

[109]  Ireneusz Zbicinski,et al.  Modelling of thermal degradation process dynamics of bioproducts using artificial neural networks , 1996 .

[110]  P. Robson,et al.  Oct4 and Sox2 Directly Regulate Expression of Another Pluripotency Transcription Factor, Zfp206, in Embryonic Stem Cells* , 2007, Journal of Biological Chemistry.

[111]  J N Weinstein,et al.  Neural network techniques for informatics of cancer drug discovery. , 2000, Methods in Enzymology.

[112]  R. Callard,et al.  From the top down: towards a predictive biology of signalling networks. , 2003, Trends in biotechnology.

[113]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[114]  Carsten Peterson,et al.  Random Boolean network models and the yeast transcriptional network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[115]  David A Winkler,et al.  Predictive Bayesian neural network models of MHC class II peptide binding. , 2005, Journal of molecular graphics & modelling.

[116]  A. Sharov,et al.  Dynamics of global gene expression changes during mouse preimplantation development. , 2004, Developmental cell.

[117]  J. Gasteiger,et al.  The beauty of molecular surfaces as revealed by self-organizing neural networks. , 1994, Journal of molecular graphics.

[118]  Frank R. Burden,et al.  Bayesian neural nets for modeling in drug discovery , 2004 .

[119]  Hsinchun Chen,et al.  Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining , 2007, Decis. Support Syst..

[120]  Julianne D. Halley,et al.  Toward a Rosetta stone for the stem cell genome: stochastic gene expression, network architecture, and external influences. , 2008, Stem cell research.

[121]  V. Plerou,et al.  Scale invariance and universality: organizing principles in complex systems , 2000 .

[122]  Gregory A Landrum,et al.  Building predictive ADMET models for early decisions in drug discovery. , 2004, Current opinion in drug discovery & development.

[123]  Paul Havlak,et al.  Scale-invariant structure of strongly conserved sequence in genomic intersections and alignments , 2006, Proceedings of the National Academy of Sciences.