Systems Biology Publications
PeTTSy: a computational tool for perturbation analysis of complex systems biology models
Mirela Domijan, Paul E.Brown, Boris V. Shulgin and David A. Rand
Multiplex PCR and Next Generation Sequencing for the Non-Invasive Detection of Bladder Cancer
Ward, D.G., Baxter, L., Gordon, N.S., Ott, S., Savage, R.S., Beggs, A.D., James, J.D., Lickiss, J., Green, S., Wallis, Y. and Wei, W.
Predicting chemoinsensitivity in breast cancer with ’omics/digital pathology data fusion
Richard S. Savage, Yinyin Yuan
The application of FAIMS gas analysis in Medical Diagnostics
J. A. Covington, M.P. van. der Schee, A.S.L. Edge, B. Boyle, R.S. Savage and R. P. Arasaradnam
Biomarkers of early stage osteoarthritis, rheumatoid arthritis and musculoskeletal health.
Usman Ahmed, Attia Anwar, Richard S. Savage, Matthew L. Costa, Nicola Mackay, Andrew Filer, Karim Raza, Richard A. Watts, Paul G. Winyard, Joanna Tarr, Richard C. Haigh, Paul J. Thornalley & Naila Rabbani
Non-Invasive Distinction of Non-Alcoholic Fatty Liver Disease using Urinary Volatile Organic Compound Analysis: Early Results
Ramesh P. Arasaradnam1,4, Michael McFarlane1, Emma Daulton2, Erik Westenbrink2, Nicola O'Connell1, Subiatu Wurie1, Chuka U. Nwokolo1, Karna D. Bardhan3, Richard S. Savage5, 6, James A. Covington2
Prediction of resistance to chemotherapy in ovarian cancer: a systematic review
Lloyd, Katherine L., Ian A. Cree, and Richard S. Savage
Image Based Validation of Dynamical Models for Cell Reorientation
Robert Lockley, Graham Ladds,Till Bretschneider
Article first published online: 9 DEC 2014 | DOI: 10.1002/cyto.a.22600
Adaptive Multivariate Global Testing
We present a methodology for dealing with recent challenges in testing global hypotheses using multivariate observations. The proposed tests target situations, often arising in emerging applications of neuroimaging, where the sample size n is relatively small compared with the observations’ dimension K. We employ adaptive designs allowing for sequential modifications of the test statistics adapting to accumulated data. The adaptations are optimal in the sense of maximizing the predictive power of the test at each interim analysis while still controlling the Type I error. Optimality is obtained by a general result applicable to typical adaptive design settings. Further, we prove that the potentially high-dimensional design space of the tests can be reduced to a low-dimensional projection space enabling us to perform simpler power analysis studies, including comparisons to alternative tests. We illustrate the substantial improvement in efficiency that the proposed tests can make over standard tests, especially in the case ofn smaller or slightly larger than K. The methods are also studied empirically using both simulated data and data from an EEG study, where the use of prior knowledge substantially increases the power of the test.
Student Publication, Systems Biology: The proliferating cell hypothesis
The proliferating cell hypothesis: a metabolic framework for Plasmodium growth and development
J. Enrique Salcedo-Sora, Eva Caamano-Gutierrez, Stephen A. Ward, Giancarlo A. Biagini
- Liverpool School of Tropical Medicine, Pembroke Place, Liverpool , L3 5QA, UK
- Warwick Systems Biology Centre, Senate House, University of Warwick, Coventry , CV4 7AL, UK
http://www.cell.com/trends/parasitology/fulltext/S1471-4922(14)00021-X
Bleb-driven chemotaxis of Dictyostelium cells
Evgeny Zatulovskiy, Richard Tyson, Till Bretschneider, and Robert R. Kay
Journal of Cell Biology, doi: 10.1083/jcb.201306147, 2014
Multiple copies of eukaryotic translation initiation factors in Brassica rapa facilitate redundancy, enabling diversification through variation in splicing and broad‐spectrum virus resistance
CF Nellist, W Qian, CE Jenner, JD Moore, S Zhang, X Wang, WH Briggs, GC Barker, R Sun and JA Walsh
The Plant Journal 77 (2), 261-268.
Discovery of a family of gamma-aminobutyrate ureas via rational derepression of a silent bacterial gene cluster
J.D. Sidda, L. Song, V. Poon, M. Al-Bassam, O. Lazos, M.J. Buttner, G.L. Challis and C. Corre
Bayesian Hierarchical Clustering for Studying Cancer Gene Expression Data with Unknown Statistics
Sirinukunwattana K., Savage R., Bari M., Snead D., Rajpoot N., 2013, PLOS ONE
Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data.
The implementation of GBHC is available at https://sites.google.com/site/gaussianbhc/
Network balance via CRY signalling controls the Arabidopsis circadian clock over ambient temperatures
P.D. Gould*, N. Ugarte*, M. Domijan*, M.J. Costa, J. Foreman, D. McGregor, S. Penfield, D.A. Rand, A. Hall, K. Halliday, A.J. Millar, (2013) , Mol. Syst. Biol. 9(650). DOI:10.1038/msb.2013.7 (F1000prime recommended)
Extracting regulator activity profiles by integration of de novo motifs and expression data: characterizing key regulators of nutrient depletion responses in Streptomyces coelicolor
Iqbal M, Mast Y, Amin R, Hodgson DA; STREAM Consortium, Wohlleben W, Burroughs NJ.
Nucleic Acids Res. (2012) 40 (12): 5227-39. doi: 10.1093/nar/gks205
Determining transcriptional regulator activities is a major focus of systems biology, providing key insight into regulatory mechanisms and co-regulators. For organisms such as Escherichia coli, transcriptional regulator binding site data can be integrated with expression data to infer transcriptional regulator activities. However, for most organisms there is only sparse data on their transcriptional regulators, while their associated binding motifs are largely unknown. Here, we address the challenge of inferring activities of unknown regulators by generating de novo (binding) motifs and integrating with expression data. We identify a number of key regulators active in the metabolic switch, including PhoP with its associated directed repeat PHO box, candidate motifs for two SARPs, a CRP family regulator, an iron response regulator and that for LexA. Experimental validation for some of our predictions was obtained using gel-shift assays. Our analysis is applicable to any organism for which there is a reasonable amount of complementary expression data and for which motifs (either over represented or evolutionary conserved) can be identified in the genome.
Making open data work for plant scientists
Leonelli, S., Smirnoff, N., Moore, J., Cook, C. and Bastow, R. (2013) Journal of Experimental Botany 09/2013
MEME-LaB: motif analysis in clusters
Brown, P., Baxter, L., Hickman, R., Beynon, J., Moore, J.D. and Ott, S. (2013) Bioinformatics, doi: 10.1093/bioinformatics/btt248.
A Local Regulatory Network Around Three NAC Transcription Factors in Stress Responses and Senescence in Arabidopsis leaves
Hickman, R., Hill, C., Penfold, C.A., Breeze, E., Bowden, L., Moore, J.D., Zhang, P., Jackson, A., Cooke, E., Bewicke-Copley, F., Mead, A., Beynon, J., Wild, D.L., Denby, K.J., Ott, S. and Buchanan-Wollaston, V. (2013). The Plant Journal, 04/2013.
Quantitative analysis of human ras localization and function in the fission yeast Schizosaccharomyces pombe
Bond M, Croft W, Tyson R, Bretschneider T, Davey J, Ladds G.
Yeast. 2013 Apr;30(4):145-56. doi: 10.1002/yea.2949. Epub 2013 Mar 20.
Genetic regulation of glucoraphanin accumulation in Beneforté® broccoli
Traka, M. H., Saha, S., Huseby, S., Kopriva, S., Walley, P. G., Barker, G., Moore, J., Mero, G., van den Bosch, F., Constant, H., Kelly, L., Schepers, H., Boddupalli, S., and Mithen, R. F. (2013). Genetic regulation of glucoraphanin accumulation in Beneforté® broccoli. New Phytologist, doi: 10.1111/nph.12232
Accelerating Bayesian Hierarchical Clustering of Time Series Data with a Randomised Algorithm
Robert Darkins, Emma J Cooke, Zoubin Ghahramani, Paul D.W. Kirk, David L. Wild, Richard S. Savage, PLOS ONE (2013)
A temporal switch model for estimating transcriptional activity in gene expression
Dafyd J. Jenkins, Barbel Finkenstadt and David A. Rand. (2013) Bioinformatics (online ahead of print)
Bayesian correlated clustering to integrate multiple datasets.
Kirk, P., Griffin, J.E., Savage, R.S., Ghahramani, Z. and Wild, D.L. Bioinformatics (2012) 28 (24): 3290-3297.
Predicting protein β-sheet contacts using a maximum entropy based correlated mutation measure
Nikolas S. Burkoff, Csilla Várnai and David L. Wild. (2013) Bioinformatics (online ahead of print), 29(1).
Conserved noncoding sequences highlight shared components of regulatory networks in dicotyledonous plants
Laura Baxter, Aleksey Jironkin, Richard Hickman, Jay Moore, Christopher Barrington, Peter Krusche, Nigel P. Dyer, Vicky Buchanan-Wollaston, Alexander Tiskin, Jim Beynon, Katherine Denby, Sascha Ott (2012), Plant Cell, 24: 3949-3965
MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling
Kitchen, J.L., Moore, J.D., Palmer, S.A. and Allaby, R.G. (2012) MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling. BMC Bioinformatics, 13:287
Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis
Windram, O., Madhou, P., McHattie, S., Hill, C., Hickman, R., Cooke, E., Jenkins, D.J., Penfold, C.A., Baxter, L., Breeze, E., Kiddle, S.J., Rhodes, J., Atwell, S., Klieberstein, D.J., Kim Y-S., Stegle, O., Borgwardt, K., Zhang, C., Tabrett, A., Legaie, R., Moore, J., Finkenstadt, B., Wild, D.L., Mead, A., Rand, D., Beynon, J., Ott, S, Buchanan-Wollaston, V. and Denby, K. (2012) Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis. The Plant Cell, 24(9): 3530-3557.
Archaeogenomic evidence of punctuated genome evolution in Gossypium
Palmer, S.A., Clapham, A.J., Rose, P., Freitas, F.O., Owen, B.D., Beresford-Jones, D., Moore, J.D., Kitchen, J.L. and Allaby, R.G. (2012) Archaeogenomic evidence of punctuated genome evolution in Gossypium. Molecular Biology and Evolution 29 (8).
Evolution of resource and energy management in biologically realistic gene regulatory network models
Stekel DJ and Jenkins DJ (2012). In 'Evolutionary Systems Biology'. Series: Advances in Experimental Medicine and Biology 751, Springer. Chapter 14:301-328
Metabolic switches and adaptations deduced from the proteomes of Streptomyces coelicolor wild type and phoP mutant grown in batch culture
Thomas, L., Hodgson, D.A., Wentzel, A., Nieselt, K., Ellingsen, T.E., Moore. J., Morrissey, E.R., Legaie, R; The STREAM Consortium, Wohleben, W., Rodriguez-Garcia, A., Martin, J.F., Burroughs, N.J., Wellington, E.M., Smith, M.C. (2012) Mol. Cell Proteomics, 11(2): M111.013797.
Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-Throughput Microscopy at Low Temporal Resolution
Mike J. Downey, Danuta M. Jeziorska, Sascha Ott, T. Katherine Tamai, Georgy Koentges, Keith W. Vance, Till Bretschneider, 2011, PLoS ONE 6(12): e27886.
Patient-Specific Data Fusion Defines Prognostic Cancer Subtypes
2011 PLoS Computational Biology 7(10)
Independently evolved virulence effectors converge onto hubs in a plant immune system network
Mukhtar, M.S., Carvunis, A-R., Dreze, M., Epple, P., Steinbrenner, J., Moore, J., Tasan, M., Galli, M., Hao, T., Nishimura, M.T., Pevzner, S.J., Donovan, S.E., Ghamsari, L., Santhanam, B., Romero, V., Poulin, M.M., Gebreab, F., Gutierrez, B.J., Tam, S., Monachello, D., Boxem, M., Harbort, C.J., McDonald, N., Gai, L., Chen, H., He, Y., European Union Effectoromics Consrtium, Vandenhaute, J., Roth, F.P., Hill, D.E., Ecker, J.R., Vidal, M., Beynon, J., Braun, P., Dangl, J.L. (2011) Independently evolved virulence effectors converge onto hubs in a plant immune system network. Science 333(6042): 596-601.
Evidence for network evolution in an Arabidopsis interactome map
Arabidopsis Interactome Mapping Consortium (2011) Evidence for network evolution in an Arabidopsis interactome map. Science 333(6042): 601-607.
High-Resolution Temporal Profiling of Transcripts during Arabidopsis Leaf Senescence Reveals a Distinct Chronology of Processes and Regulation
Breeze, E., Harrison, E., McHattie, S., Hughes, L., Hickman, R., Hill, C., Kiddle, S., Kim, Y-S., Penfold, C.A., Jenkins, D., Zhang, C., Morris, K., Jenner, C., Jackson, S., Thomas, B., Tabrett, A., Legaie, R., Moore, J.D., Wild, D.L., Ott, S., Rand, D., Beynon, J., Denby, K., Mead, A. and Buchanan-Wollaston, V. (2011) The Plant Cell 23 (3): 873-894.
Nodal cis-regulatory elements reveal epiblast and primitive endoderm heterogeneity in the peri-implantation mouse embryo
Céline Granier, Vasily Gurchenkov, Aitana Perea-Gomez, Anne Camus, Sascha Ott, Costis Papanayotou, Julian Iranzo, Anne Moreau, John Reid, Georgy Koentges, Délara Sabéran-Djoneidi, Jérôme Collignon (2011) Developmental Biology, 349: 350-362
Recombinants between Deformed wing virus and Varroa destructor virus-1 may prevail in Varroa destructor-infested honeybee colonies
Moore, J., Jironkin, A., Chandler, D., Burroughs, N., Evans, D.J. and Ryabov, E.V. (2011) Recombinants between Deformed wing virus and Varroa destructor virus-1 may prevail in Varroa destructor-infested honeybee colonies. Journal of General Virology 92, 156-161.
Signatures of Adaptation to Obligate Biotrophy in the Hyaloperonospora arabidopsidis Genome
Laura Baxter, Sucheta Tripathy, Naveed Ishaque, Nico Boot, Adriana Cabra, Eric Kemen, Marco Thines, Audrey Ah-Fong, Ryan Anderson, Wole Badejoko, Peter Bittner-Eddy, Jeffrey L. Boore, Marcus C. Chibucos, Mary Coates, Paramvir Dehal, Kim Delehaunty, Suomeng Dong, Polly Downton, Bernard Dumas, Georgina Fabro, Catrina Fronick, Susan I. Fuerstenberg, Lucinda Fulton, Elodie Gaulin, Francine Govers, Linda Hughes, Sean Humphray, Rays H. Y. Jiang, Howard Judelson, Sophien Kamoun, Kim Kyung, Harold Meijer, Patrick Minx, Paul Morris, Joanne Nelson, Vipa Phuntumart, Dinah Qutob, Anne Rehmany, Alejandra Rougon-Cardoso, Peter Ryden, Trudy Torto-Alalibo, David Studholme, Yuanchao Wang, Joe Win, Jo Wood, Sandra W. Clifton, Jane Rogers, Guido Van den Ackerveken, Jonathan D. G. Jones, John M. McDowell, Jim Beynon, and Brett M. Tyler. Signatures of Adaptation to Obligate Biotrophy in the Hyaloperonospora arabidopsidis Genome. Science 330:6010 pp. 1549-1551
Interactive segmentation of clustered cells via geodesic commute distance and constrained density weighted Nyström method
Du, C.-J., Marcello, M., Spiller, D. G., White, M. R. H. and Bretschneider, T. , Interactive segmentation of clustered cells via geodesic commute distance and constrained density weighted Nyström method. Cytometry Part A, n/a. doi: 10.1002/cyto.a.20993, 2010
Quantitative analysis of regulatory flexibility under changing environmental conditions
Edwards, K. D., Akman, O. E., Knox, K., Lumsden, P. J., Thomson, A. W., Brown, P. E., Pokhilko, A., Kozma-Bognar, L., Nagy, F., Rand, D. A., & Millar, A. J. (2010). Molecular Systems Biology 6:424 doi:10.1038/msb.2010.81
De Novo Evolution of Complex, Global and Hierarchical Gene Regulatory Mechanisms
Dafyd J. Jenkins and Dov J. Stekel (2010). Journal of Molecular Evolution 71(2):128-140
Stochasticity Versus Determinism: Consequences for Realistic Gene Regulatory Network Modelling and Evolution
Dafyd J. Jenkins and Dov J. Stekel (2010). Journal of Molecular Evolution 70(2):215-231
On reverse engineering of gene interaction networks using time course data with repeated measurements
Discovering transcriptional modules by Bayesian data integration
Multiquantal release underlies the distribution of synaptic efficacies in the neocortex
Dynamics of populations and networks of neurons with voltage-activated and calcium-activated currents
Extracting nonlinear integrate-and-fire models from experimental data using dynamic I-V curves
Spike-train spectra and network response functions for non-linear integrate-and-fire neurons
Spike-triggered averages for passive and resonant neurons receiving filtered excitatory and inhibitory synaptic drive
Measurement and analysis of postsynaptic potentials using a novel voltage-deconvolution method
Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces
Badel L, Lefort S, Brette R, Petersen CC, Gerstner W and Richardson MJE. Journal of Neurophysiology 99: 656-666 (2008)
Inferring Transcriptional Networks Using Prior Biological Knowledge and Constrained State-Space Models
R/BHC: Fast Bayesian Hierarchical Clustering for Microarray Data
Computational approaches to the integration of gene expression, ChIP-chip and sequence data in the inference of gene regulatory networks
Reconstruction and stability of secondary structure elements in the context of protein structure prediction
A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series
Modeling and Visualizing Uncertainty in Gene Expression Clusters using Dirichlet Process Mixtures
An approach to pathway reconstruction using whole genome metabolic models and sensitive sequence searching
CRANKITE– A Fast Polypeptide Backbone Conformation Sampler
Comment on: Efficient Monte Carlo trial moves for polypeptide simulations
Enhancing data integration with text analysis to find proteins implicated in plant stress response
Hassani-Pak, K., Legaie, R., Canevet, C., van den Berg, H.A., Moore, J.D. and Rawlings, C. (2010) Journal of Integrative Bioinformatics 7:3.
Metabolic modeling and analysis of the metabolic switch in Streptomyces coelicolor
Mohammad T Alam, Maria E Merlo, The STREAM Consortium, David A Hodgson, Elizabeth MH Wellington, Eriko Takano, Rainer Breitling (2010). BMC Genomics 11:202.
The dynamic architecture of the metabolic switch in Streptomyces coelicolor
Nieselt, K., Battke, F., Herbig, A., Bruheim, P., Wentzel, A., Jakobsen, O.M., Sletta, H., Alam, M.T., Merlo, M.E., Moore, J., Omara,W., Morrissey, E.R., Juarez-Hermosillo, M., Rodríguez-García, A., Nentwich, M., Thomas, L., Legaie, R., Gaze, W.H., Challis, G.L., Jansen, R.C., Dijkhuizen, L., Rand, D.A., Wild, D.L., Bonin, M., Reuther, J., Wohlleben, W., Smith, M.C.M., Burroughs, N.J., Martín, J.F., Hodgson, D.A., Takano, E., Breitling, R., Ellingsen, T.E., Elizabeth M. H. Wellington, E.M.H. (2010). BMC Genomics 11:10. (Highly Accessed Paper)
Archaeogenetic Evidence of Ancient Nubian Barley Evolution from Six to Two-Row Indicates Local Adaptation
The CACTA transposon Bot1 played a major role in Brassica genome divergence and gene proliferation
Alix, K., Joets, J., Ryder, C.D., Moore, J., Barker, G.C., Bailey, J.P., King, G.J. and Heslop-Harrison, J.S. (2008). The Plant Journal 56: 1030-1044.
High resolution tracking of cell membrane dynamics in moving cells: An electrifying approach
R.A. Tyson, D.B.A. Epstein, K.I. Anderson, T. Bretschneider, Math. Model. Nat. Phenom., 5(1):34-55, 2010.
Transformation from spots to waves in a model of actin pattern formation
S. Whitelam, T. Bretschneider, N.J. Burroughs, Physical Review Letters, 102:198103, 2009.
Analysis of cell movement by simultaneous quantification of local membrane displacement and fluorescent intensities using Quimp2
L. Bosgraaf, P.J.M. van Haastert, T. Bretschneider, Cell Motility and the Cytoskeleton, 66(3):156-165, 2009.
The 3-Dimensional dynamics of actin waves, a model of cytoskeletal self-organization
T. Bretschneider, K. Anderson, M. Ecke, A. Müller-Taubenberger, B. Schroth-Diez, H.C. Ishikawa-Ankerhold, G. Gerisch, Biophys. J., 96, 2888-2900, 2009.
Using single fluorescent reporter gene to infer half-life of extrinsic noise and other parameters of gene expression.
Dual positive and negative regulation of GPCR signaling by GTP Hydrolysis.
B. Smith, C. Hill, L. Godfrey, D A Rand, H van den Berg, S Thornton, M Hodgkin, J Davey and G Ladds. Cellular Signalling 21 (209)1151-1160 doi:10.1016/j.cellsig.2009.03.00
Bayesian inference of biochemical kinetic parameters using the linear noise approximation.
Network control analysis for time-dependent dynamical states.
Pulsatile stimulation determines timing and specificity of NF-kappa B-dependent transcription.
Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana
Steven J. Kiddle, Oliver P. F. Windram, Stuart McHattie, Andrew Mead, Jim Beynon, Vicky Buchanan-Wollaston, Katherine J. Denby and Sach Mukherjee (2010) Bioinformatics 26 (3):355
Prediction of photoperiodic regulators from quantitative gene circuit models.
CWRML: representing crop wild relative conservation and use data in XML
Moore, J.D., Kell, S.P., Iriondo J, Ford-Lloyd, B. and Maxted, N. (2008) CWRML: representing crop wild relative conservation and use data in XML. BMC Bioinformatics 9:116.
Reversal of cell polarity and actin-myosin cytoskeleton reorganization under mechanical and chemical stimulation.
J. Dalous, E. Burghardt, A. Müller-Taubenberger, F. Bruckert, G. Gerisch, T. Bretschneider. Reversal of cell polarity and actin-myosin cytoskeleton reorganization under mechanical and chemical stimulation. Biophys. J., 94(3):1063-1074, 2008.
TreeMos: a high-throughput phylogenomic approach to find and visualise phylogenetic mosaicism.
Moore, J.D. and Allaby, R.G. (2008) TreeMos: a high-throughput phylogenomic approach to find and visualise phylogenetic mosaicism. Bioinformatics 24(5):717-718.
TreeMos is a novel high-throughput graphical analysis application that allows the user to search for phylogenetic mosaicism among one or more DNA or protein sequence multiple alignments and additional unaligned sequences.
Stochastic niche structure and diversity maintenance in the T cell repertoire.
The Malthusian parameter in microbial ecology and evolution: An optimal control treatment.
The development of insulin resistance in Type 2 diabetes: insights from knockout studies.
R. Pattaranit, H. A. van den Berg, D. Spanswick. The development of insulin resistance in Type 2 diabetes: insights from knockout studies. Sci Prog 91 (2008) 285-316.