%% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk.sourceforge.net/ %% Created for chris wiggins at 2013-03-24 17:28:54 -0400 %% Saved with string encoding Unicode (UTF-8) @article{2013:JW, Author = {\postdoc{Jan-Willem Van de Meent}, \student{Jonathan Bronson}, Ruben Gonzalez, \me{Chris Wiggins}}, Title = {Learning biochemical kinetic models from single-molecule data with hierarchically-coupled hidden Markov models}, Year = {2013}} @article{2011:raj2011identifying, Author = {\student{Raj, A}. and \postdoc{Dewar, M.} and Palacios, G. and Rabadan, R. and \me{Wiggins, C.H.}}, Journal = {PloS one}, Number = {12}, Pages = {e27631}, Publisher = {Public Library of Science}, Title = {Identifying Hosts of Families of Viruses: A Machine Learning Approach}, Volume = {6}, Year = {2011}} @article{2012:Dewar-2012-IEEE, Author = {\postdoc{Dewar, M}. and \me{Wiggins, C}. and Wood, F.}, Doi = {10.1109/LSP.2012.2184795}, Issn = {1070-9908}, Journal = {Signal Processing Letters, IEEE}, Keywords = {Markov processes;inference mechanisms;black-box inference procedure;discrete state-duration random variables;discrete state-indicator variable;explicit state duration distributions;explicit-state-duration hidden Markov models;implicit geometric state duration distribution;inference techniques;per-state duration distributions;tuning-parameter free;Bayesian methods;Computational complexity;Estimation;Hidden Markov models;Inference algorithms;Materials;Random variables;Bayesian explicit duration hidden Markov model;Bayesian hidden semi-Markov model;Monte Carlo methods;forward-backward algorithm;slice sampling}, Number = {4}, Pages = {235-238}, Title = {Inference in Hidden Markov Models with Explicit State Duration Distributions}, Volume = {19}, Year = {2012}, Bdsk-Url-1 = {http://dx.doi.org/10.1109/LSP.2012.2184795}} @article{2010:jonathan11graphical, Author = {\student{Jonathan Bronson} and \student{Jake Hofman} and Jingyi Fei and Ruben Gonzalez and \me{Chris H. Wiggins}}, Issn = {1471-2105}, Journal = {BMC Bioinformatics}, Publisher = {BioMed Central Ltd}, Title = {{Graphical models for inferring single molecule dynamics}}, Volume = {11}, Year = {2010}} @article{2010:walczak2010analytic, Author = {Walczak, A.M. and \student{Mugler, A.} and \me{Wiggins, C.H.}}, Journal = {Arxiv preprint arXiv:1005.2648}, Title = {{Analytic methods for modeling stochastic regulatory networks}}, Year = {2010}} @article{2011:wiggins2009form, Author = {\student{Andrew Mugler} and \student{Boris Grinshpun} and \student{R. Franks} and \me{Chris H Wiggins}}, Issn = {0027-8424}, Journal = {Proceedings of the National Academy of Sciences}, Number = {2}, Pages = {446}, Publisher = {National Acad Sciences}, Title = {{Statistical method for revealing form-function relations in biological networks}}, Volume = {108}, Year = {2011}} @article{2010:mugler2010information, Author = {\student{Mugler, A.} and Walczak, A.M. and \me{Wiggins, C.H.}}, Issn = {1079-7114}, Journal = {Physical review letters}, Number = {5}, Pages = {58101}, Publisher = {American Physical Society}, Title = {{Information-Optimal Transcriptional Response to Oscillatory Driving}}, Volume = {105}, Year = {2010}} @article{2010:li2010learning, Author = {\student{Li, X}. and Panea, C. and \me{Wiggins, C.H}. and Reinke, V. and Leslie, C.}, Issn = {1553-734X}, Journal = {PLoS computational biology}, Number = {4}, Pages = {137--144}, Publisher = {Public Library of Science San Francisco, USA}, Title = {{Learning +++graph-mer+++ motifs that predict gene expression trajectories in development}}, Volume = {6}, Year = {2010}} @article{2009:vbfret:pnas, Author = {Fei, J. and \student{Jonathan E. Bronson} and \student{Jake M. Hofman} and Srinivas, R.L. and \me{Chris H. Wiggins} and Gonzalez, R.L.}, Journal = {Proceedings of the National Academy of Sciences}, Number = {37}, Pages = {15702}, Publisher = {National Acad Sciences}, Title = {{Allosteric collaboration between elongation factor G and the ribosomal L1 stalk directs tRNA movements during translation}}, Volume = {106}, Year = {2009}} @article{2009:vbfret:bj, Author = {\student{Jonathan E. Bronson} and Jingyi Fei and \student{Jake M. Hofman} and Ruben L. Gonzalez and \me{Chris H. Wiggins}}, Issn = {0006-3495}, Journal = {Biophysical Journal}, Number = {12}, Pages = {3196--3205}, Publisher = {Elsevier}, Title = {Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data}, Url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0907.3156}, Volume = {97}, Year = {2009}, Bdsk-Url-1 = {http://www.citebase.org/abstract?id=oai:arXiv.org:0907.3156}} @article{2009:specmark:pre, Author = {\student{Andrew Mugler} and Aleksandra M. Walczak and \me{Chris H. Wiggins}}, Eid = {041921}, Journal = {Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)}, Keywords = {cellular biophysics; eigenvalues and eigenfunctions; genetics; molecular biophysics; probability; stochastic processes}, Myurldoi = {10.1103/PhysRevE.80.041921}, Number = {4}, Numpages = {19}, Pages = {041921}, Publisher = {APS}, Title = {Spectral solutions to stochastic models of gene expression with bursts and regulation}, Url = {http://link.aps.org/abstract/PRE/v80/e041921}, Volume = {80}, Year = {2009}, Bdsk-Url-1 = {http://link.aps.org/abstract/PRE/v80/e041921}} @article{2008:infospec, Address = {Los Alamitos, CA, USA}, Author = {\student{Anil Raj} and {\me{Chris H. Wiggins}}}, Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, Masid = {12547887}, Myurl = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.124}, Myurldoi = {10.1109/TPAMI.2009.124}, Pages = {988--995}, Publisher = {IEEE Computer Society}, Title = {An Information-Theoretic Derivation of Min-Cut Based Clustering}, Volume = {32}, Year = {2010}} @article{2009:WaMuWi, Abstract = {10.1073/pnas.0811999106 The past decade has seen great advances in our understanding of the role of noise in gene regulation and the physical limits to signaling in biological networks. Here, we introduce the spectral method for computation of the joint probability distribution over all species in a biological network. The spectral method exploits the natural eigenfunctions of the master equation of birth+++death processes to solve for the joint distribution of modules within the network, which then inform each other and facilitate calculation of the entire joint distribution. We illustrate the method on a ubiquitous case in nature: linear regulatory cascades. The efficiency of the method makes possible numerical optimization of the input and regulatory parameters, revealing design properties of, e.g., the most informative cascades. We find, for threshold regulation, that a cascade of strong regulations converts a unimodal input to a bimodal output, that multimodal inputs are no more informative than bimodal inputs, and that a chain of up-regulations outperforms a chain of down-regulations. We anticipate that this numerical approach may be useful for modeling noise in a variety of small network topologies in biology.}, Author = {Aleksandra M. Walczak and \student{Andrew Mugler} and \me{Chris H. Wiggins}}, Citeulike-Article-Id = {4291442}, Journal = {Proceedings of the National Academy of Sciences}, Keywords = {biophysics, frequency, gene\_regulation, network, noise, spectral, stochastic, transcriptional\_regulation}, Month = {April}, Myurldoi = {10.1073/pnas.0811999106}, Posted-At = {2009-04-08 17:57:52}, Priority = {2}, Title = {A stochastic spectral analysis of transcriptional regulatory cascades}, Url = {http://dx.doi.org/10.1073/pnas.0811999106}, Year = {2009}, Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.0811999106}} @article{2008:hofman:genmod, Author = {\student{Jake M. Hofman} and \me{Chris H. Wiggins}}, Eid = {258701}, Journal = {Physical Review Letters}, Myeprint = {0709.3512}, Myurldoi = {10.1103/PhysRevLett.100.258701}, Number = {25}, Numpages = {4}, Pages = {258701}, Publisher = {APS}, Title = {Bayesian Approach to Network Modularity}, Url = {http://arxiv.org/abs/0709.3512}, Volume = {100}, Year = {2008}, Bdsk-Url-1 = {http://arxiv.org/abs/0709.3512}} @article{2009:IET:mugler, Abstract = {We introduce a quantitative measure of the capacity of a small biological network to evolve. We apply our measure to a stochastic description of the experimental setup of Guet et al. (Science 296:1466, 2002), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. We take an information-theoretic approach, allowing the system to set parameters that optimize signal processing ability, thus enumerating each network's highest-fidelity functions. We find that all networks studied are highly evolvable by our measure, meaning that change in function has little dependence on change in parameters. Moreover, we find that each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without losing it along the way. This property further underscores the evolvability of the networks.}, Author = {\student{Andrew Mugler} and \student{Etay Ziv} and Ilya Nemenman and \me{Chris H. Wiggins}}, Citeulike-Article-Id = {3741413}, Eprint = {0811.2834}, Journal = {IET Systems Biology (formerly IEE Proceedings - Systems Biology)}, Keywords = {evolution, networks}, Month = {Nov}, Pages = {379}, Posted-At = {2008-12-03 17:31:39}, Priority = {2}, Title = {{Quantifying evolvability in small biological networks}}, Url = {http://arxiv.org/abs/0811.2834}, Volume = {3}, Year = {2009}, Bdsk-Url-1 = {http://arxiv.org/abs/0811.2834}} @article{2008:IET:mugler, Author = {\student{Andrew Mugler} and \student{Etay Ziv} and Ilya Nemenman and \me{Chris H. Wiggins}}, Journal = {IET Systems Biology (formerly IEE Proceedings - Systems Biology)}, Myeprint = {0805.1776}, Number = {5}, Pages = {203--205}, Title = {Serially-regulated biological networks fully realize a constrained set of functions}, Url = {http://arxiv.org/abs/0805.1776}, Volume = {2}, Year = {2008}, Bdsk-Url-1 = {http://arxiv.org/abs/0805.1776}} @article{2008:bjd, Abstract = {Actin-based cell motility and force generation are central to immune response, tissue development, and cancer metastasis, and understanding actin cytoskeleton regulation is a major goal of cell biologists. Cell spreading is a commonly used model system for motility experiments +++++++ spreading fibroblasts exhibit stereotypic, spatially-isotropic edge dynamics during a reproducible sequence of functional phases: 1) During early spreading, cells form initial contacts with the surface. 2) The middle spreading phase exhibits rapidly increasing attachment area. 3) Late spreading is characterized by periodic contractions and stable adhesions formation. While differences in cytoskeletal regulation between phases are known, a global analysis of the spatial and temporal coordination of motility and force generation is missing. Implementing improved algorithms for analyzing edge dynamics over the entire cell periphery, we observed that a single domain of homogeneous cytoskeletal dynamics dominated each of the three phases of spreading. These domains exhibited a unique combination of biophysical and biochemical parameters +++++++ a motility module. Biophysical characterization of the motility modules revealed that the early phase was dominated by periodic, rapid membrane blebbing; the middle phase exhibited continuous protrusion with very low traction force generation; and the late phase was characterized by global periodic contractions and high force generation. Biochemically, each motility module exhibited a different distribution of the actin-related protein VASP, while inhibition of actin polymerization revealed different dependencies on barbed-end polymerization. In addition, our whole-cell analysis revealed that many cells exhibited heterogeneous combinations of motility modules in neighboring regions of the cell edge. Together, these observations support a model of motility in which regions of the cell edge exhibit one of a limited number of motility modules that, together, determine the overall motility function. Our data and algorithms are publicly available to encourage further exploration. }, Author = {Benjamin J. Dubin-Thaler and \student{Jake M. Hofman} and Harry Xenias and Ingrid Spielman and Anna V. Shneidman and \student{Lawrence A. David} and Hans-Gunther Dobereiner and \me{Chris H. Wiggins} and Michael P. Sheetz}, Journal = {PLoS ONE}, Month = {Nov}, Number = {11}, Pages = {e3735}, Publisher = {Public Library of Science}, Title = {Quantification of Cell Edge Velocities and Traction Forces Reveals Distinct Motility Modules during Cell Spreading}, Volume = {3}, Year = {2008}} @article{2007:tasha, Abstract = {The immunological synapse (IS) is a junction between the T cell and antigen-presenting cell and is composed of supramolecular activation clusters (SMACs). No studies have been published on naive T cell IS dynamics. Here, we find that IS formation during antigen recognition comprises cycles of stable IS formation and autonomous naive T cell migration. The migration phase is driven by PKCtheta, which is localized to the F-actin-dependent peripheral (p)SMAC. PKCtheta(-/-) T cells formed hyperstable IS in vitro and in vivo and, like WT cells, displayed fast oscillations in the distal SMAC, but they showed reduced slow oscillations in pSMAC integrity. IS reformation is driven by the Wiscott Aldrich Syndrome protein (WASp). WASp(-/-) T cells displayed normal IS formation but were unable to reform IS after migration unless PKCtheta was inhibited. Thus, opposing effects of PKCtheta and WASp control IS stability through pSMAC symmetry breakin. . .}, Address = {Molecular Pathogenesis Program, Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, NY 10016, USA.}, Author = {T. N. Sims and T. J. Soos and H. S. Xenias and B. Dubin-Thaler and \student{Jake M. Hofman} and J. C. Waite and T. O. Cameron and V. K. Thomas and R. Varma and \me{C. H. Wiggins} and M. P. Sheetz and D. R. Littman and M. L. Dustin}, Editor = {2007/05/22 09:00}, Journal = {Cell.}, Keywords = {2007/05/22 09:00}, Month = {May 18}, Number = {4}, Pages = {773-85}, Title = {{O}pposing effects of {PKC}theta and {WAS}p on symmetry breaking and relocation of the immunological synapse.}, Volume = {129}, Year = {2007}} @article{2007:david:DBN, Author = {\student{Lawrence A. David} and \me{Chris H. Wiggins}}, Journal = {Annals of The New York Academy of Sciences}, Number = {1 Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference}, Pages = {90--101}, Publisher = {Blackwell Synergy}, Title = {Benchmarking of Dynamic {B}ayesian Networks Inferred from Stochastic Time-Series Data}, Volume = {1115}, Year = {2007}} @article{2007:crut, Abstract = {The dynamics of supercoiled DNA play an important role in various cellular processes such as transcription and replication that involve DNA supercoiling. We present experiments that enhance our understanding of these dynamics by measuring the intrinsic response of single DNA molecules to sudden changes in tension or torsion. The observed dynamics can be accurately described by quasistatic models, independent of the degree of supercoiling initially present in the molecules. In particular, the dynamics are not affected by the continuous removal of the plectonemes. These results set an upper bound on the hydrodynamic drag opposing plectoneme removal, and thus provide a quantitative baseline for the dynamics of bare DNA.}, Address = {Kavli Institute of Nanoscience, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands.}, Author = {A. Crut and D. A. Koster and R. Seidel and \me{C. H. Wiggins} and N. H. Dekker}, Editor = {2007/07/12 09:00}, Journal = {Proc Natl Acad Sci U S A.}, Keywords = {DNA, Superhelical/*chemistry/*metabolism Deoxyribonuclease I/metabolism Nucleic Acid Conformation Rotation 2007/08/31 09:00}, Month = {Jul 17}, Myepub = {Epub 2007 Jul 10.}, Number = {29}, Pages = {11957-62.}, Reference = {0 (DNA, Superhelical) EC 3. 1. 21. 1 (Deoxyribonuclease I)}, Title = {{F}ast dynamics of supercoiled {DNA} revealed by single-molecule experiments.}, Volume = {104}, Year = {2007}} @article{2007:bsp, Abstract = {We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the cross-talk phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive.}, Author = {\student{Etay Ziv} AND Ilya Nemenman AND \me{Chris H. Wiggins}}, Journal = {PLoS ONE}, Month = {Oct}, Myeprint = {q-bio/0612041}, Myurl = {http://dx.doi.org/10.1371%2Fjournal.pone.0001077}, Number = {10}, Pages = {e1077}, Publisher = {Public Library of Science}, Title = {Optimal Signal Processing in Small Stochastic Biochemical Networks}, Volume = {2}, Year = {2007}} @article{2007:anshul:NYAS, Author = {Anshul Kundaje and Steve Lianoglou and Xuejing Li and David Quigley and Marta Arias and \me{Chris H. Wiggins} and Li Zhang and Christina Leslie}, Journal = {Annals of The New York Academy of Sciences}, Pages = {178--202}, Title = {Learning regulatory programs that accurately predict differential expression with {MEDUSA}}, Volume = {1115}, Year = {2007}} @inproceedings{2007:amp, Author = {Amy Rebecca Gansell and \advisee{Irene K. Tamaru} and Aleks Jakulin and \me{Chris H. Wiggins}}, Booktitle = {Digital Discovery: Exploring New Frontiers in Human Heritage. CAA 2006. Computer Applications and Quantitative Methods in Archaeology. Proceedings of the 34th Conference, Fargo, United States, April 2006}, Editors = {Jeffrey T. Clark and Emily M. Hagemeister}, Isbn = {ISBN 978-963-8046-90-1}, Publisher = {Archaeolingua}, Title = {Predicting Regional Classification of {L}evantine Ivory Sculptures: A Machine Learning Approach}, Year = {2007}} @article{2006:tobias, Author = {Tobias Munk and Oskar Hallatschek and \me{Chris H. Wiggins} and Erwin Frey}, Journal = {Physical Review E}, Number = {4}, Pages = {041911}, Title = {Dynamics of semiflexible polymers in a flow field}, Volume = {74}, Year = {2006}} @article{2006:kundaje, Abstract = {BACKGROUND: We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences (\"motifs\") in the gene's regulatory region and the expression levels of regulators such as transcription factors in the experiment (\"parents\"). GeneClass formulates the learning task as a classification problem--predicting 1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternatin. . .}, Address = {Department of Computer Science, Columbia University, New York, NY 10027, USA.}, Author = {A. Kundaje and \student{M. Middendorf} and M. Shah and \me{C. H. Wiggins} and Y. Freund and C. Leslie}, Editor = {2006/05/26 09:00}, Journal = {BMC Bioinformatics.}, Keywords = {Algorithms Amino Acid Motifs Binding Sites Computational Biology/*methods Data Interpretation, Statistical Databases, Protein Fungal Proteins/chemistry Gene Expression Profiling/*methods *Gene Expression Regulation Heat-Shock Proteins/metabolism Molecular Chaperones/chemistry Oligonucleotide Array Sequence Analysis/methods Research Support, N. I. H. , Extramural Research Support, U. S. Gov't, Non-P. H. S. Saccharomyces cerevisiae/metabolism 2006/07/01 09:00}, Month = {Mar 20}, Pages = {S5.}, Reference = {0 (Fungal Proteins) 0 (Heat-Shock Proteins) 0 (Molecular Chaperones)}, Title = {{A} classification-based framework for predicting and analyzing gene regulatory response.}, Volume = {7 Suppl 1}, Year = {2006}} @article{2006:koster, Abstract = {Most analyses of single-molecule experiments consist of binning experimental outcomes into a histogram and finding the parameters that optimize the fit of this histogram to a given data model. Here we show that such an approach can introduce biases in the estimation of the parameters, thus great care must be taken in the estimation of model parameters from the experimental data. The bias can be particularly large when the observations themselves are not statistically independent and are subjected to global constraints, as, for example, when the iterated steps of a motor protein acting on a single molecule must not exceed the total molecule length. We have developed a maximum-likelihood analysis, respecting the experimental constraints, which allows for a robust and unbiased estimation of the parameters, even when the bias well exceeds 100\%. We demonstrate the potential of the method for a number of single-molecule experiments, focus. . .}, Address = {Kavli Institute of Nanoscience, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands.}, Author = {Daniel A. Koster and \me{Chris H. Wiggins} and Nynke H. Dekker}, Editor = {2006/01/28 09:00}, Journal = {Proc Natl Acad Sci U S A.}, Keywords = {2006/01/28 09:00}, Pages = {1750-1755}, Title = {Multiple events on single molecules: Unbiased estimation in single-molecule biophysics.}, Volume = {104}, Year = {2006}} @article{2006:hgd, Abstract = {We have monitored active movements of the cell circumference on specifically coated substrates for a variety of cells including mouse embryonic fibroblasts and T cells, as well as wing disk cells from fruit flies. Despite having different functions and being from multiple phyla, these cell types share a common spatiotemporal pattern in their normal membrane velocity; we show that protrusion and retraction events are organized in lateral waves along the cell membrane. These wave patterns indicate both spatial and temporal long-range periodic correlations of the actomyosin gel.}, Address = {Department of Biological Sciences, Columbia University, New York, New York 10027, USA.}, Author = {H. G. Dobereiner and B. J. Dubin-Thaler and \student{Jake M. Hofman} and H. S. Xenias and T. N. Sims and G. Giannone and M. L. Dustin and \me{C. H. Wiggins} and M. P. Sheetz}, Editor = {2006/08/16 09:00}, Journal = {Phys Rev Lett.}, Keywords = {2006/08/16 09:00}, Month = {Jul 21}, Myepub = {Epub 2006 Jul 20.}, Number = {3}, Pages = {038102.}, Title = {{L}ateral membrane waves constitute a universal dynamic pattern of motile cells.}, Volume = {97}, Year = {2006}} @article{2006:erratum, Abstract = {R-Ras, an atypical member of the Ras subfamily of small GTPases, enhances integrin-mediated adhesion and signaling through a poorly understood mechanism. Dynamic analysis of cell spreading by total internal reflection fluorescence (TIRF) microscopy demonstrated that active R-Ras lengthened the duration of initial membrane protrusion, and promoted the formation of a ruffling lamellipod, rich in branched actin structures and devoid of filopodia. By contrast, dominant-negative R-Ras enhanced filopodia formation. Moreover, RNA interference (RNAi) approaches demonstrated that endogenous R-Ras contributed to cell spreading. These observations suggest that R-Ras regulates membrane protrusions through organization of the actin cytoskeleton. Our results suggest that phospholipase Cepsilon (PLCepsilon) is a novel R-Ras effector mediating the effects of R-Ras on the actin cytoskeleton and membrane protrusion, because R-Ras was co-precipitated w. . .}, Address = {Department of Pharmacology, University of Wisconsin-Madison, Madison, WI 53706, USA.}, Author = {A. S. Ada-Nguema and H. Xenias and \student{Jake M. Hofman} and \me{Chris H. Wiggins} and M. P. Sheetz and P. J. Keely}, Editor = {2006/03/16 09:00}, Journal = {J Cell Sci.}, Keywords = {Actins/*metabolism Animals COS Cells Calcium/metabolism Cell Adhesion Cell Line, Transformed Cell Transformation, Viral Cercopithecus aethiops Chelating Agents/pharmacology Comparative Study Dose-Response Relationship, Drug Egtazic Acid/analogs \& derivatives/pharmacology Female Fluorescent Antibody Technique Fluorescent Dyes Green Fluorescent Proteins/metabolism Humans Mammary Glands, Human/cytology Microscopy, Fluorescence Models, Biological Phospholipase C/analysis/genetics/*metabolism Precipitin Tests Pseudopodia/*metabolism RNA Interference RNA, Small Interfering/metabolism Research Support, N. I. H. , Extramural Research Support, Non-U. S. Gov't ras Proteins/genetics/*metabolism 2006/06/06 09:00}, Month = {Apr 1}, Pages = {4364.http://jcs.biologists.org/cgi/content/full/119/20/4364}, Reference = {0 (Actins) 0 (Chelating Agents) 0 (Fluorescent Dyes) 0 (RNA, Small Interfering) 147336-22-9 (Green Fluorescent Proteins) 67-42-5 (Egtazic Acid) 7440-70-2 (Calcium) 85233-19-8 (1,2-bis(2-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid) EC 3. 1. 4. - (phospholipase C epsilon) EC 3. 1. 4. 3 (Phospholipase C) EC 3. 6. 5. 2 (ras Proteins)}, Title = {{T}he small {GTP}ase {R}-{R}as regulates organization of actin and drives membrane protrusions through the activity of {PLC}-$\epsilon$}, Year = {2006}} @article{2006:cai, Abstract = {Nonmuscle myosin IIA (NMM-IIA) is involved in the formation of focal adhesions and neurite retraction. However, the role of NMM-IIA in these functions remains largely unknown. Using RNA interference as a tool to decrease NMM-IIA expression, we have found that NMM-IIA is the major myosin involved in traction force generation and retrograde F-actin flow in mouse embryonic fibroblast cells. Quantitative analyses revealed that approximately 60\% of traction force on fibronectin-coated surfaces is contributed by NMM-IIA and approximately 30\% by NMM-IIB. The retrograde F-actin flow decreased dramatically in NMM-IIA-depleted cells, but seemed unaffected by NMM-IIB deletion. In addition, we found that depletion of NMM-IIA caused cells to spread at a higher rate and to a greater area on fibronectin substrates during the early spreading period, whereas deletion of NMM-IIB appeared to have no effect on spreading. The distribution of NMM-IIA wa. . .}, Address = {Department of Biological Sciences, Columbia University, New York, New York, USA.}, Author = {Y. Cai and N. Biais and G. Giannone and M. Tanase and G. Jiang and \student{Jake M. Hofman} and \me{C. H. Wiggins} and P. Silberzan and A. Buguin and B. Ladoux and M. P. Sheetz}, Editor = {2006/08/22 09:00}, Journal = {Biophys J.}, Keywords = {Actins/*physiology Animals Cell Movement/*physiology Cells, Cultured Fibroblasts/*physiology Mechanotransduction, Cellular/*physiology Mice Molecular Motor Proteins/*physiology Muscle, Skeletal/physiology Nonmuscle Myosin Type IIA/*physiology Stress, Mechanical 2006/12/14 09:00}, Month = {Nov 15}, Myepub = {Epub 2006 Aug 18.}, Number = {10}, Pages = {3907-20.}, Reference = {0 (Actins) 0 (Molecular Motor Proteins) EC 3. 6. 1. - (Nonmuscle Myosin Type IIA)}, Title = {{N}onmuscle myosin {IIA}-dependent force inhibits cell spreading and drives {F}-actin flow.}, Volume = {91}, Year = {2006}} @article{2006:aracne, Abstract = {BACKGROUND: Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide \"reverse engineering\" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. RESULTS: We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex t. . .}, Address = {Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.}, Author = {A. A. Margolin and I. Nemenman and K. Basso and \me{C. Wiggins} and G. Stolovitzky and Dalla R. Favera and A. Califano}, Editor = {2006/05/26 09:00}, Journal = {BMC Bioinformatics.}, Keywords = {Algorithms Animals B-Lymphocytes/metabolism Computational Biology/*methods Computer Simulation Gene Expression Profiling *Gene Expression Regulation Humans Models, Statistical Neural Networks (Computer) Oligonucleotide Array Sequence Analysis Phenotype Reproducibility of Results Research Support, N. I. H. , Extramural Software Transcription, Genetic 2006/07/01 09:00}, Month = {Mar 20}, Pages = {S7.}, Title = {{ARACNE}: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context.}, Volume = {7 Suppl 1}, Year = {2006}} @inproceedings{2005:medusa, Author = {\student{Manuel Middendorf} and Anshul Kundaje and Mihir Shah and Yoav Freund and \me{Chris H. Wiggins} and Christina S. Leslie}, Booktitle = {Proceedings of Ninth Annual International Conference on Research in Computational Molecular Biology (RECOMB 2005), special ``Lecture notes in Bioinformatics" from Springer-Verlag}, Editor = {Satoru Miyano}, Entrydate = {2006/02/07}, Journal = {Research In Computational Mol. Biology, Proc.}, Myurl = {http://dx.doi.org/10.1007/11415770_41}, Pages = {538--552}, Publisher = {Springer}, Title = {Motif discovery through predictive modeling of gene regulation}, Volume = {3500}, Year = {2005}} @article{2005:matstat-pre, Abstract = {We present a graph embedding space (i. e. , a set of measures on graphs) for performing statistical analyses of networks. Key improvements over existing approaches include discovery of \"motif hubs\" (multiple overlapping significant subgraphs), computational efficiency relative to subgraph census, and flexibility (the method is easily generalizable to weighted and signed graphs). The embedding space is based on scalars, functionals of the adjacency matrix representing the network. Scalars are global, involving all nodes; although they can be related to subgraph enumeration, there is not a one-to-one mapping between scalars and subgraphs. Improvements in network randomization and significance testing--we learn the distribution rather than assuming Gaussianity--are also presented. The resulting algorithm establishes a systematic approach to the identification of the most significant scalars and suggests machine-learning techni. . .}, Address = {College of Physicians and Surgeons, Columbia University, New York, New York 10027, USA.}, Author = {\student{E. Ziv} and \advisee{R. Koytcheff} and \student{M. Middendorf} and \me{C. Wiggins}}, Editor = {2005/02/09 09:00}, Journal = {Phys Rev E Stat Nonlin Soft Matter Phys.}, Keywords = {Algorithms Artificial Intelligence Computational Biology/*methods Computer Simulation Escherichia coli/physiology *Neural Networks (Computer) Normal Distribution Research Support, U. S. Gov't, Non-P. H. S. Research Support, U. S. Gov't, P. H. S. Saccharomyces cerevisiae/physiology 2005/05/20 09:00}, Month = {Jan}, Myeprint = {cond-mat/0306610}, Myepub = {Epub 2005 Jan 10.}, Pages = {016110.}, Title = {Systematic identification of statistically significant network measures.}, Volume = {71(1 Pt 2)}, Year = {2005}} @article{2005:kundaje-module, Abstract = {Our goal is to cluster genes into transcriptional modules--sets of genes where similarity in expression is explained by common regulatory mechanisms at the transcriptional level. We want to learn modules from both time series gene expression data and genome-wide motif data that are now readily available for organisms such as S. cereviseae as a result of prior computational studies or experimental results. We present a generative probabilistic model for combining regulatory sequence and time series expression data to cluster genes into coherent transcriptional modules. Starting with a set of motifs representing known or putative regulatory elements (transcription factor binding sites) and the counts of occurrences of these motifs in each gene's promoter region, together with a time series expression profile for each gene, the learning algorithm uses expectation maximization to learn module assignments based on both types of data. We a. . .}, Address = {Los Alamitos, CA, USA}, Author = {Anshul Kundaje and \student{Manuel Middendorf} and Feng Gao and \me{Chris Wiggins} and Christina Leslie}, Editor = {2006/10/19 09:00}, Journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, Keywords = {2006/10/19 09:00}, Month = {Jul-Sep}, Myissn = {1545-5963}, Myurldoi = {http://dx.doi.org/10.1109/TCBB.2005.34}, Number = {3}, Pages = {194--202}, Publisher = {IEEE Computer Society Press}, Title = {{C}ombining sequence and time series expression data to learn transcriptional modules.}, Url = {http://www1.cs.columbia.edu/compbio/module-clust/}, Volume = {2}, Year = {2005}, Bdsk-Url-1 = {http://www1.cs.columbia.edu/compbio/module-clust/}} @article{2005:Ziv05:infomod, Abstract = {Exploiting recent developments in information theory, we propose, illustrate, and validate a principled information-theoretic algorithm for module discovery and the resulting measure of network modularity. This measure is an order parameter (a dimensionless number between 0 and 1). Comparison is made with other approaches to module discovery and to quantifying network modularity (using Monte Carlo generated Erdo s-like modular networks). Finally, the network information bottleneck (NIB) algorithm is applied to a number of real world networks, including the ``social" network of co-authors at the 2004 APS March Meeting.}, Address = {College of Physicians \& Surgeons, Department of Biomedical Engineering, Columbia University, New York, New York 10027, USA.}, Author = {\student{E. Ziv} and \student{M. Middendorf} and \me{C. H. Wiggins}}, Editor = {2005/05/21 09:00}, Journal = {Physical Review E}, Keywords = {2005/05/21 09:00}, Month = {Apr}, Myeprint = {q-bio/0411033}, Myepub = {Epub 2005 Apr 14.}, Pages = {046117.}, Title = {Information-theoretic approach to network modularity.}, Volume = {71(4 Pt 2)}, Year = {2005}} @article{2005:Middendorf:droso, Abstract = {Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data subsampling) classified as a duplication-mutation-complementation network over preferential attachment, small-world, and a duplication-mutation mechanism without complementation. Systematic classification, rather than statistical study of specific properties, provides a discriminative approach to understand the design of complex networks.}, Address = {Department of Physics, College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA.}, Author = {\student{M. Middendorf} and \student{E. Ziv} and \me{C. H. Wiggins}}, Editor = {2005/02/25 09:00}, Journal = {Proc Natl Acad Sci U S A.}, Keywords = {Algorithms Animals Drosophila Proteins/*metabolism Drosophila melanogaster Protein Binding Research Support, U. S. Gov't, Non-P. H. S. Research Support, U. S. Gov't, P. H. S. 2005/04/14 09:00}, Month = {Mar 1}, Myeprint = {q-bio/0408010}, Myepub = {Epub 2005 Feb 22.}, Number = {9}, Pages = {3192-7.}, Reference = {0 (Drosophila Proteins)}, Title = {Inferring network mechanisms: the Drosophila melanogaster protein interaction network.}, Url = {http://www.pnas.org/cgi/content/abstract/102/9/3192}, Volume = {102}, Year = {2005}, Bdsk-Url-1 = {http://www.pnas.org/cgi/content/abstract/102/9/3192}} @article{2004:yardena-pulled, Abstract = {We study the relaxation dynamics of a semiflexible chain by introducing a time-dependent tension. The chain has one of its ends attached to a large bead, and the other end is fixed. We focus on the initial relaxation of the chain that is initially strongly stretched. Using a tension that is self-consistently determined, we obtain the evolution of the end-to-end distance with no free parameters. Our results are in good agreement with single molecule experiments on double stranded DNA.}, Address = {Department of Materials and Interfaces, Weizmann Institute of Science, Rehovot 76100, Israel.}, Author = {Y. Bohbot-Raviv and W. Z. Zhao and M. Feingold and \me{C. H. Wiggins} and R. Granek}, Editor = {2004/04/20 05:00}, Journal = {Phys Rev Lett.}, Keywords = {DNA/*chemistry *Models, Chemical Research Support, Non-U. S. Gov't Research Support, U. S. Gov't, Non-P. H. S. Thermodynamics 2004/05/20 05:00}, Month = {Mar 5}, Myepub = {Epub 2004 Mar 3.}, Number = {9}, Pages = {098101}, Reference = {9007-49-2 (DNA)}, Title = {Relaxation dynamics of semiflexible polymers.}, Volume = {92}, Year = {2004}} @article{2004:0411028, Abstract = {Motivation: Studying gene regulatory mechanisms in simple model organisms through analysis of high-throughput genomic data has emerged as a central problem in computational biology. Most approaches in the literature have focused either on finding a few strong regulatory patterns or on learning descriptive models from training data. However, these approaches are not yet adequate for making accurate predictions about which genes will be up- or down-regulated in new or held-out experiments. By introducing a predictive methodology for this problem, we can use powerful tools from machine learning and assess the statistical significance of our predictions. Results: We present a novel classification-based method for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences ( motifs') in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment ( parents'). Thus, our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. We convert the regression task of predicting real-valued gene expression measurements to a classification task of predicting 1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. The learning algorithm employed is boosting with a margin-based generalization of decision trees, alternating decision trees. This large-margin classifier is sufficiently flexible to allow complex logical functions, yet sufficiently simple to give insight into the combinatorial mechanisms of gene regulation. We observe encouraging prediction accuracy on experiments based on the Gasch S. cerevisiae dataset, and we show that we can accurately predict up- and down-regulation on held-out experiments. We also show how to extract significant regulators, motifs and motif-regulator pairs from the learned models for various stress responses. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks. Availability: The MLJava package is available upon request to the authors. Supplementary: Additional results are available from http://www.cs.columbia.edu/compbio/geneclass}, Author = {\student{Manuel Middendorf} and Anshul Kundaje and \me{Chris Wiggins} and Yoav Freund and Christina Leslie}, Journal = {Bioinformatics}, Myeprint = {q-bio/0411028}, Number = {suppl. 1}, Pages = {i232-240}, Title = {Predicting Genetic Regulatory Response Using Classification}, Volume = {20}, Year = {2004}} @article{2004:0406016, Author = {\student{Manuel Middendorf} and Anshul Kundaje and \me{Chris Wiggins} and Yoav Freund and Christina Leslie}, Entrydate = {2006/02/07}, Journal = {Regulatory Genomics}, Myeprint = {q-bio/0406016}, Pages = {1--13}, Title = {Predicting genetic regulatory response using classification: {Yeast} stress response}, Volume = {3318}, Year = {2005}} @article{2004:0402017, Abstract = {BACKGROUND: Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. RESULTS: We present a method to assess systematically which of a set of proposed netwo. . .}, Address = {Department of Physics, Columbia University, New York, USA}, Author = {\student{Manuel Middendorf} and \student{Etay Ziv} and \advisee{Carter Adams} and \advisee{Jen Hom} and \advisee{Robin Koytcheff} and \advisee{Chaya Levovitz} and \advisee{Gregory Woods} and Linda Chen and \me{Chris Wiggins}}, Date = {Nov 22}, Editor = {2004/11/24 09:00}, Journal = {BMC Bioinformatics}, Keywords = {Animals Caenorhabditis elegans/physiology Computational Biology/*methods Escherichia coli K12/genetics *Models, Biological Models, Genetic Models, Neurological Nerve Net/physiology *Neural Networks (Computer) Protein Interaction Mapping Research Support, U. S. Gov't, Non-P. H. S. Saccharomyces cerevisiae/physiology Saccharomyces cerevisiae Proteins/metabolism 2005/07/20 09:00}, Month = {Nov 22}, Myeprint = {q-bio/0402017}, Pages = {181}, Reference = {0 (Saccharomyces cerevisiae Proteins)}, Title = {Discriminative Topological Features Reveal Biological Network Mechanisms}, Volume = {5}, Year = {2004}} @inproceedings{2002:cw_Dresden, Author = {\me{Chris H. Wiggins} and Loic Le Goff}, Booktitle = {Function and Regulation of Cellular Systems: Experiments and Models}, Chapter = {3}, Editor = {A. Deutsch and M. Falcke and J. Howard and W. Zimmermann}, Publisher = {Birkhaeuser-Verlag}, Title = {Biopolymer Dynamics}, Year = {2002}} @article{2002:0206031, Author = {\me{Chris H. Wiggins} and Ilya Nemenman}, Issue = {3}, Journal = {Journal of Experimental Mechanics}, Myeprint = {physics/0206031}, Pages = {361-370}, Title = {Process Pathway Inference via Time Series Analysis}, Volume = {43}, Year = {2003}} @article{2001:cw_lighthill, Author = {\me{Chris H. Wiggins}}, Journal = {Mathematical Methods in the Applied Sciences}, Pages = {1325--1335}, Title = {Biopolymer mechanics: stability, dynamics, and statistics}, Volume = {24}, Year = {2001}} @article{2001:cw_chainpaper, Abstract = {When shaken vertically, a hanging chain displays a startling variety of distinct behaviors. We find experimentally that instabilities occur in tonguelike bands of parameter space, to swinging or rotating pendular motion, or to chaotic states. Mathematically, the dynamics are described by a nonlinear wave equation. A linear stability analysis predicts instabilities within the well-known resonance tongues; their boundaries agree very well with experiment. Full simulations of the 3D dynamics reproduce and elucidate many aspects of the experiment. The chain is also observed to tie knots in itself, some quite complex. This is beyond the reach of the current analysis and simulations.}, Address = {W. G. Pritchard Laboratories, Department of Mathematics, Pennsylvania State University, University Park, Pennsylvania 16802, USA.}, Author = {A. Belmonte and M. J. Shelley and S. T. Eldakar and \me{C. H. Wiggins}}, Editor = {2001/09/05 10:00}, Journal = {Phys Rev Lett.}, Keywords = {2001/09/05 10:01}, Month = {Sep 10}, Myepub = {Epub 2001 Aug 24.}, Number = {11}, Pages = {114301.}, Title = {Dynamic patterns and self-knotting of a driven hanging chain.}, Volume = {87}, Year = {2001}} @inproceedings{2000:cw_IMACS, Author = {\me{Chris H. Wiggins}}, Booktitle = {Sixteenth IMACS World Congress 2000 on Scientific Computation, Applied Mathematics, and Simulation}, Editor = {M. Deville and R. Owens}, Isbn = {3-9522075-1-9}, Title = {Darboux's Frame and {S}chrodinger's Equation for Biopolymers}, Year = {2000}} @inproceedings{1999:cw_santafeproceedings, Author = {T. R. Powers and R. E. Goldstein and \me{Chris H. Wiggins}}, Booktitle = {Biological Physics: Third International Symposium}, Editor = {H. Frauenfelder and G. Hummer and R. Garcia}, Pages = {271}, Title = {Supercoiling Bacterial Filaments}, Year = {1999}} @article{1998:9802084, Author = {Raymond E. Goldstein and Thomas R. Powers and \me{Chris H. Wiggins}}, Journal = {Physical Review Letters}, Myeprint = {cond-mat/9802084}, Pages = {5232--5235}, Title = {The Viscous Nonlinear Dynamics of Twist and Writhe}, Volume = {80}, Year = {1998}} @article{1998:9707346, Author = {\me{Chris H. Wiggins} and Raymond E. Goldstein}, Journal = {Physical Review Letters}, Myeprint = {cond-mat/9707346}, Pages = {3879--3882}, Title = {Flexive and Propulsive Dynamics of Elastica at Low {R}eynolds numbers}, Volume = {80}, Year = {1998}} @article{1998:9703244, Abstract = {We present an analysis of the planar motion of single semiflexible filaments subject to viscous drag or point forcing. These are the relevant forces in dynamic experiments designed to measure biopolymer bending moduli. By analogy with the \"Stokes problems\" in hydrodynamics (motion of a viscous fluid induced by that of a wall bounding the fluid), we consider the motion of a polymer, one end of which is moved in an impulsive or oscillatory way. Analytical solutions for the time-dependent shapes of such moving polymers are obtained within an analysis applicable to small-amplitude deformations. In the case of oscillatory driving, particular attention is paid to a characteristic length determined by the frequency of oscillation, the polymer persistence length, and the viscous drag coefficient. Experiments on actin filaments manipulated with optical traps confirm the scaling law predicted by the analysis and provide a new techn. . .}, Address = {Department of Physics, Princeton University, New Jersey 08544, USA}, Author = {\me{Chris H. Wiggins} and Daniel X. Riveline and Albrecht Ott and Raymond E. Goldstein}, Editor = {1998/04/09}, Journal = {Biophysical journal}, Keywords = {Actins/chemistry/physiology Biophysics/methods Elasticity Kinetics Mathematics Microfilaments/*physiology/ultrastructure Microtubules/physiology/ultrastructure Models, Biological Oscillometry Research Support, Non-U. S. Gov't Research Support, U. S. Gov't, Non-P. H. S. Viscosity 1998/04/09 00:01}, Month = {Feb}, Myeprint = {cond-mat/9703244}, Number = {2}, Pages = {1043-1060}, Reference = {0 (Actins)}, Title = {Trapping and Wiggling: Elastohydrodynamics of Driven Microfilaments}, Volume = {74(2 Pt 1)}, Year = {1998}} @article{1997:9704225, Author = {D. Riveline and \me{Chris H. Wiggins} and A. Ott and Raymond E. Goldstein}, Journal = {Physical Review E}, Myeprint = {cond-mat/9704225}, Pages = {R1330-R1333}, Title = {Elastohydrodynamic study of actin filaments using fluorescence microscopy}, Volume = {56}, Year = {1997}} @article{1995:wiggins:GRL, Author = {\me{Chris Wiggins} and M. Spiegelman}, Date = {15 May 1995}, Journal = {Geophysical Research Lett.}, Pages = {1289--1292}, Title = {Magma Migration and magmatic solitary waves in 3D}, Volume = {22}, Year = {1995}} @article{1993:wiggins:TRD, Author = {E. O'Brien and M. Bennett and V and Cherniatin and C. Y. Chi and A. Chikanian and B. Dolgoshein and S. Kumar and D. Lissauer and S. McCorkle and J. T. Mitchell and S. Nagamiya and V. Polychronakos and K. Pope and W. Sippach and H. Takai and M. Toy and D. Wang and Y. F. Wang and \me{C. Wiggins} and W. Willis}, Journal = {IEEE Transactions on Nuclear Science}, Pages = {153--157}, Title = {A Transition Radiation Detector which Features Accurate Tracking and d{E}/dx Particle Identification}, Volume = {40}, Year = {1993}}