Model Identification and Parameter Estimation

[1]  J. Śmieja,et al.  Modeling epigenetic regulation of PRC1 protein accumulation in the cell cycle , 2015, Biology Direct.

[2]  Sanjay Tyagi,et al.  Profiling T Cell Activation Using Single-Molecule Fluorescence In Situ Hybridization and Flow Cytometry , 2015, The Journal of Immunology.

[3]  Jaroslaw Smieja,et al.  The meaning of sensitivity functions in signaling pathways analysis , 2014 .

[4]  Obi L. Griffith,et al.  SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution , 2014, PLoS Comput. Biol..

[5]  Anna Tourovskaia,et al.  Tissue-engineered microenvironment systems for modeling human vasculature , 2014, Experimental biology and medicine.

[6]  Roberto Bertolusso,et al.  Dynamic cross talk model of the epithelial innate immune response to double-stranded RNA stimulation: Coordinated dynamics emerging from cell-level noise (PLoS ONE 9, 4 (e93396) DOI:10.1371/journal.pone.0093396) , 2014 .

[7]  M. Cobleigh,et al.  Population pharmacokinetics of trastuzumab emtansine (T-DM1), a HER2-targeted antibody–drug conjugate, in patients with HER2-positive metastatic breast cancer: clinical implications of the effect of covariates , 2014, Cancer Chemotherapy and Pharmacology.

[8]  S. Diekmann,et al.  Biomolecular dynamics and binding studies in the living cell. , 2014, Physics of life reviews.

[9]  O. Chkrebtii,et al.  Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates. , 2013, Mathematical biosciences.

[10]  Trachette L. Jackson,et al.  A Mathematical Model of Cancer Stem Cell Driven Tumor Initiation: Implications of Niche Size and Loss of Homeostatic Regulatory Mechanisms , 2013, PloS one.

[11]  A. van den Berg,et al.  Three-dimensional co-cultures of human endothelial cells and embryonic stem cell-derived pericytes inside a microfluidic device. , 2013, Lab on a chip.

[12]  A. Brasier,et al.  Applications of selected reaction monitoring (SRM)-mass spectrometry (MS) for quantitative measurement of signaling pathways. , 2013, Methods.

[13]  Benjamin A. Shoemaker,et al.  Cancer Missense Mutations Alter Binding Properties of Proteins and Their Interaction Networks , 2013, PloS one.

[14]  David A. Rand,et al.  A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number , 2013, Bioinform..

[15]  D. Vavylonis,et al.  Molecular viewing of actin polymerizing actions and beyond: Combination analysis of single‐molecule speckle microscopy with modeling, FRAP and s‐FDAP (sequential fluorescence decay after photoactivation) , 2013, Development, growth & differentiation.

[16]  V. Vaska,et al.  Vancomycin therapeutics and monitoring: a contemporary approach , 2013, Internal medicine journal.

[17]  M. Khammash,et al.  Systematic Identification of Signal-Activated Stochastic Gene Regulation , 2013, Science.

[18]  D. Schmitter,et al.  Predicting stem cell fate changes by differential cell cycle progression patterns , 2013, Development.

[19]  Krzysztof Puszynski,et al.  Sensitivity analysis of deterministic signaling pathways models , 2012 .

[20]  D. Hedley,et al.  Emerging applications of flow cytometry in solid tumor biology. , 2012, Methods.

[21]  G. Drummen,et al.  Advanced Fluorescence Microscopy Techniques—FRAP, FLIP, FLAP, FRET and FLIM , 2012, Molecules.

[22]  K. Patel,et al.  Pharmacokinetic concepts revisited--basic and applied. , 2011, Current pharmaceutical biotechnology.

[23]  J. Banga,et al.  Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods , 2011, PloS one.

[24]  C. Sander,et al.  Predicting the functional impact of protein mutations: application to cancer genomics , 2011, Nucleic acids research.

[25]  Marek Kimmel,et al.  Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed , 2011, Human mutation.

[26]  Eugenia Corvera Poiré,et al.  Tumor Angiogenesis and Vascular Patterning: A Mathematical Model , 2011, PloS one.

[27]  Nacho Molina,et al.  Mammalian Genes Are Transcribed with Widely Different Bursting Kinetics , 2011, Science.

[28]  Julian R. E. Davis,et al.  Dynamic Analysis of Stochastic Transcription Cycles , 2011, PLoS biology.

[29]  Cees Neef,et al.  Optimal Sampling Strategy Development Methodology Using Maximum A Posteriori Bayesian Estimation , 2011, Therapeutic drug monitoring.

[30]  Jun Yu,et al.  Nonsynonymous substitution rate (Ka) is a relatively consistent parameter for defining fast-evolving and slow-evolving protein-coding genes , 2011, Biology Direct.

[31]  M. Jamei,et al.  PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals. , 2010, Toxicology.

[32]  Thomas Peyret,et al.  A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals. , 2010, Toxicology and applied pharmacology.

[33]  Gregory B. Gloor,et al.  Estimating the evidence of selection and the reliability of inference in unigenic evolution , 2010, Algorithms for Molecular Biology.

[34]  S. Gaudet,et al.  Systematic calibration of a cell signaling network model , 2010, BMC Bioinformatics.

[35]  P. Bork,et al.  A method and server for predicting damaging missense mutations , 2010, Nature Methods.

[36]  M. Girolami,et al.  Inferring Signaling Pathway Topologies from Multiple Perturbation Measurements of Specific Biochemical Species , 2010, Science Signaling.

[37]  M. Rathinam,et al.  Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks. , 2010, The Journal of chemical physics.

[38]  Atsushi Miyawaki,et al.  Illuminating cell-cycle progression in the developing zebrafish embryo , 2009, Proceedings of the National Academy of Sciences.

[39]  T. Maiwald,et al.  Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood , 2009, Bioinform..

[40]  M. Khoo,et al.  Model-based assessment of cardiovascular autonomic control in children with obstructive sleep apnea. , 2009, Sleep.

[41]  M. Kimmel,et al.  A robust estimator of mutation rates. , 2009, Mutation research.

[42]  S. Tavtigian,et al.  In silico analysis of missense substitutions using sequence‐alignment based methods , 2008, Human mutation.

[43]  Mark A. Girolami,et al.  Bayesian inference for differential equations , 2008, Theor. Comput. Sci..

[44]  Jaroslaw Smieja,et al.  Model-based analysis of interferon-beta induced signaling pathway , 2008, Bioinform..

[45]  H. Rabitz,et al.  Diverse metabolic model parameters generate similar methionine cycle dynamics. , 2008, Journal of theoretical biology.

[46]  M. Kimmel,et al.  Heterozygous Screen in Saccharomyces cerevisiae Identifies Dosage-Sensitive Genes That Affect Chromosome Stability , 2008, Genetics.

[47]  Andrea Saltelli,et al.  An effective screening design for sensitivity analysis of large models , 2007, Environ. Model. Softw..

[48]  Krzysztof Fujarewicz,et al.  Adjoint Systems for Models of Cell Signaling Pathways and their Application to Parameter Fitting , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[49]  K. S. Brown,et al.  Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model. , 2007, IET systems biology.

[50]  Christopher R Myers,et al.  Extracting Falsifiable Predictions from Sloppy Models , 2007, Annals of the New York Academy of Sciences.

[51]  K. S. Brown,et al.  Universally Sloppy Parameter Sensitivities in Systems Biology Models , 2007, PLoS Comput. Biol..

[52]  N. Riel.  Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments , 2006, Briefings Bioinform..

[53]  Hong Wang,et al.  Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kappaB signalling pathway. , 2006, Molecular bioSystems.

[54]  D. Albertson,et al.  Gene amplification in cancer. , 2006, Trends in genetics : TIG.

[55]  K. S. Brown,et al.  Sloppy-model universality class and the Vandermonde matrix. , 2006, Physical review letters.

[56]  L. Wahl,et al.  Development of an unbiased statistical method for the analysis of unigenic evolution , 2006, BMC Bioinformatics.

[57]  Shunsuke Kato,et al.  Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods , 2006, Nucleic acids research.

[58]  Krzysztof Fujarewicz,et al.  On fitting of mathematical models of cell signaling pathways using adjoint systems. , 2005, Mathematical biosciences and engineering : MBE.

[59]  Kwang-Hyun Cho,et al.  In silico identification of the key components and steps in IFN‐γ induced JAK‐STAT signaling pathway , 2005, FEBS letters.

[60]  Musa H. Asyali,et al.  Use of Meixner functions in estimation of Volterra kernels of nonlinear systems with delay , 2005, IEEE Transactions on Biomedical Engineering.

[61]  R. Eils,et al.  Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis , 2004, The Journal of cell biology.

[62]  Marek Kimmel,et al.  Mathematical model of NF- κB regulatory module , 2004 .

[63]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[64]  H. Crissman,et al.  Cytometry of the cell cycle: Cycling through history , 2004, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[65]  Anton Nekrutenko,et al.  Evolutionary dynamics of oncogenes and tumor suppressor genes: higher intensities of purifying selection than other genes. , 2003, Molecular biology and evolution.

[66]  A. Blasi,et al.  Model-based assessment of autonomic control in obstructive sleep apnea syndrome during sleep. , 2003, American journal of respiratory and critical care medicine.

[67]  P. Bork,et al.  Human non-synonymous SNPs: server and survey. , 2002, Nucleic acids research.

[68]  Pierre Marquet,et al.  Application of pharmacokinetic modelling to the routine therapeutic drug monitoring of anticancer drugs , 2002, Fundamental & clinical pharmacology.

[69]  M. Thattai,et al.  Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[70]  S. Henikoff,et al.  Predicting deleterious amino acid substitutions. , 2001, Genome research.

[71]  R Barbieri,et al.  Closed- versus open-loop assessment of heart rate baroreflex. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[72]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[73]  S Bompadre,et al.  Parameter Estimation in a Gompertzian Stochastic Model for Tumor Growth , 2000, Biometrics.

[74]  M Kimmel,et al.  Estimating clonal heterogeneity and interexperiment variability with the bifurcating autoregressive model for cell lineage data. , 1997, Mathematical biosciences.

[75]  M. Khoo,et al.  Estimation of chemoreflex loop gain using pseudorandom binary CO/sub 2/ stimulation , 1997, IEEE Transactions on Biomedical Engineering.

[76]  K. Chon,et al.  A dual-input nonlinear system analysis of autonomic modulation of heart rate , 1996, IEEE Transactions on Biomedical Engineering.

[77]  S. J. Deminoff,et al.  Unigenic evolution: a novel genetic method localizes a putative leucine zipper that mediates dimerization of the Saccharomyces cerevisiae regulator Gcr1p. , 1995, Genetics.

[78]  Albertus C. den Brinker,et al.  Meixner-like functions having a rational z-transform , 1995, Int. J. Circuit Theory Appl..

[79]  M. Khoo,et al.  Estimation of dynamic chemoresponsiveness in wakefulness and non-rapid-eye-movement sleep. , 1995, Journal of applied physiology.

[80]  M Kimmel,et al.  Fluctuation test for two-stage mutations: application to gene amplification. , 1994, Mutation research.

[81]  V. Marmarelis.  Identification of nonlinear biological systems using laguerre expansions of kernels , 1993, Annals of Biomedical Engineering.

[82]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[83]  B H Margolin,et al.  Differences in the rates of gene amplification in nontumorigenic and tumorigenic cell lines as measured by Luria-Delbrück fluctuation analysis. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[84]  M. Khoo.  Non-invasive tracking of peripheral ventilatory response to carbon dioxide. , 1989, International journal of bio-medical computing.

[85]  A. Malliani,et al.  Cardiovascular variability signals: towards the identification of a closed-loop model of the neural control mechanisms , 1988, IEEE Transactions on Biomedical Engineering.

[86]  J. Murnane,et al.  Association of high rate of recombination with amplification of dominant selectable gene in human cells , 1988, Somatic cell and molecular genetics.

[87]  Varshaver Nb,et al.  The mutational origin of serum independence in Chinese hamster cells in vitro. , 1983 .

[88]  R T Schimke,et al.  Relationship of amplified dihydrofolate reductase genes to double minute chromosomes in unstably resistant mouse fibroblast cell lines , 1981, Molecular and cellular biology.

[89]  R T Schimke,et al.  Loss and stabilization of amplified dihydrofolate reductase genes in mouse sarcoma S-180 cell lines , 1981, Molecular and cellular biology.

[90]  S. Yamashiro,et al.  Pseudorandom testing of ventilatory response to inspired carbon dioxide in man. , 1980, Journal of applied physiology: respiratory, environmental and exercise physiology.

[91]  C Cobelli,et al.  Quantitative Estimation of Beta Cell Sensitivity to Glucose in the Intact Organism: A Minimal Model of Insulin Kinetics in the Dog , 1980, Diabetes.

[92]  A. Berkenbosch,et al.  Artificial perfusion of the ponto-medullary region of cats. A method for separation of central and peripheral effects of chemical stimulation of ventilation. , 1979, Respiration physiology.

[93]  G D Swanson,et al.  Central and peripheral chemoreflex loop gain in normal and carotid body-resected subjects. , 1979, Journal of applied physiology: respiratory, environmental and exercise physiology.

[94]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[95]  G.D. Swanson,et al.  Biological signal conditioning for system identification , 1977, Proceedings of the IEEE.

[96]  George A. Bekey,et al.  System identification- an introduction and a survey , 1970 .

[97]  J Morrow,et al.  Genetic analysis of azaguanine resistance in an established mouse cell line. , 1970, Genetics.

[98]  Read Dj,et al.  A clinical method for assessing the ventilatory response to carbon dioxide. , 1967 .

[99]  J. Hill,et al.  An application of digital computers to linear system identification , 1964 .

[100]  F. Kao,et al.  Regulation of cardiac output in anesthetized dogs during induced muscular work. , 1954, The American journal of physiology.

[101]  A. Hodgkin,et al.  Measurement of current‐voltage relations in the membrane of the giant axon of Loligo , 1952, The Journal of physiology.

[102]  E. Starling,et al.  The regulation of the heart beat , 1914, The Journal of physiology.

[103]  L. Porter,et al.  Derivation of a novel G2 reporter system , 2014, Cytotechnology.

[104]  C. Barnes.  Importance of pharmacokinetics in the management of hemophilia , 2013, Pediatric blood & cancer.

[105]  J. Śmieja.  Dynamics, feedback loops and control in biology - from physiological to individual cell models , 2011 .

[106]  Alberto Marin-Sanguino,et al.  Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases. , 2011, Methods in enzymology.

[107]  H. Shimizu,et al.  Relationship between noise characteristics in protein expressions and regulatory structures of amino acid biosynthesis pathways. , 2010, IET systems biology.

[108]  Daniel E. Zak,et al.  Continuous-time identification of gene expression models. , 2003, Omics : a journal of integrative biology.

[109]  R. Berry,et al.  Model-based assessment of autonomic control in obstructive sleep apnea syndrome. , 2003, Sleep.

[110]  Richard B Berry,et al.  Effects of CPAP therapy on cardiovascular variability in obstructive sleep apnea: a closed-loop analysis. , 2002, American journal of physiology. Heart and circulatory physiology.

[111]  E. Berntorp,et al.  Pharmacokinetics of Coagulation Factors , 2001, Clinical pharmacokinetics.

[112]  R J Cohen,et al.  System identification of closed-loop cardiovascular control: effects of posture and autonomic blockade. , 1997, The American journal of physiology.

[113]  M. Khoo.  Estimation of Dynamic Chemoreflex Gain from Spontaneous Breathing Data , 1989 .

[114]  R N Bergman,et al.  Assessment of insulin sensitivity in vivo. , 1985, Endocrine reviews.

[115]  Mark A. Kramer,et al.  Sensitivity analysis of systems of differential and algebraic equations , 1985 .

[116]  Keith W. Vance,et al.  Article Title: Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-throughput Microscopy at Low Temporal Resolution Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-throughput Microscopy at Low Temporal Resolution , 2022 .