%%% %%% chris_wiggins.bib, %%% regularized by wiggins at Tue Nov 6 18:44:38 EST 2007 %%% %%% using bibreg, %%% (c) chris wiggins 2004 %%% chris.wiggins(at)gmail.com %%% %% strings: %% regular entries: @article{ 2008:genmod, year = {2007}, title = {A Bayesian Approach to Network Modularity}, author = {Jake M. Hofman and Chris H. Wiggins}, url = {http://arxiv.org/abs/0709.3512}, }, @article{ 2008:IET:mugler, journal = {IET Systems Biology (formerly IEE Proceedings - Systems Biology)}, author = {Andrew Mugler and Etay Ziv and Ilya Nemenman and Chris H. Wiggins}, title = {Form, Function, and Information Processing in Small Stochastic Biological Networks}, year = {2008}, }, @article{ 2008:IET:modularity, journal = {IET Systems Biology (formerly IEE Proceedings - Systems Biology)}, author = {Chris H. Wiggins}, title = {Biological Modularity: Functional, Topological, and Statistical}, year = {2008}, }, @article{ 2007:tasha, author = {T. N. Sims and T. J. Soos and H. S. Xenias and B. Dubin-Thaler and J. M. Hofman and J. C. Waite and T. O. Cameron and V. K. Thomas and R. Varma and C. H. Wiggins and M. P. Sheetz and D. R. Littman and M. L. Dustin}, editor = {2007/05/22 09:00}, title = {{O}pposing effects of {PKC}theta and {WAS}p on symmetry breaking and relocation of the immunological synapse.}, journal = {Cell.}, volume = {129}, number = {4}, pages = {773-85}, address = {Molecular Pathogenesis Program, Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, NY 10016, USA.}, month = {May 18}, year = {2007}, 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. . .}, keywords = {2007/05/22 09:00}, }, @article{ 2007:david:DBN, title = {Benchmarking of Dynamic Bayesian Networks Inferred From Stochastic Time-Series Data}, author = {Lawrence David and Chris H. Wiggins}, journal = {Annals of The New York Academy of Sciences}, year = {2007}, }, @article{ 2007:crut, year = {2007}, author = {A. Crut and D. A. Koster and R. Seidel and C. H. Wiggins and N. H. Dekker}, editor = {2007/07/12 09:00}, title = {{F}ast dynamics of supercoiled {DNA} revealed by single-molecule experiments.}, journal = {Proc Natl Acad Sci U S A.}, volume = {104}, number = {29}, pages = {11957-62. Epub 2007 Jul 10.}, address = {Kavli Institute of Nanoscience, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands.}, month = {Jul 17}, year = {2007}, 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.}, keywords = {DNA, Superhelical/*chemistry/*metabolism Deoxyribonuclease I/metabolism Nucleic Acid Conformation Rotation 2007/08/31 09:00}, reference = {0 (DNA, Superhelical) EC 3. 1. 21. 1 (Deoxyribonuclease I)} }, @article{ 2007:bsp, author = {Ziv, Etay AND Nemenman, Ilya AND Wiggins, Chris H.}, journal = {PLoS ONE}, publisher = {Public Library of Science}, title = {Optimal Signal Processing in Small Stochastic Biochemical Networks}, year = {2007}, month = {Oct}, volume = {2}, url = {http://dx.doi.org/10.1371%2Fjournal.pone.0001077}, pages = {e1077}, 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.}, number = {10} eprint = {q-bio/0612041}, }, @article{ 2007:bjd, year = {2007}, title = {Quantification of Cell Movement Reveals Distinct Types of Edge Motility During Cell Spreading}, author = {Benjamin J. Dubin-Thaler and Jake M. Hofman and Harry Xenias and Ingrid Spielman and Anna V. Shneidman and Lawrence A. David and Hans-Gunther Dobereiner and Chris H. Wiggins and Michael P. Sheetz}, }, @article{ 2007:anshul:NYAS, title = {Learning regulatory programs that accurately predict differential expression with MEDUSA}, author = {Anshul Kundaje and Steve Lianoglou and Xuejing Li and David Quigley and Marta Arias and Chris H. Wiggins and Li Zhang and Christina Leslie}, journal = {Annals of The New York Academy of Sciences}, year = {2007}, }, @article{ 2007:amp, year = {2007}, title = {Predicting Regional Classification of Levantine Ivory Sculptures: A Machine Learning Approach}, author = {Amy Rebecca Gansell and Irene K. Tamaru and Aleks Jakulin and Chris H. Wiggins}, }, @article{ 2006:tobias, title = {Dynamics of semiflexible polymers in a flow field}, pages = {041911}, year = {2006}, journal = {Physical Review E}, author = {Tobias Munk and Oskar Hallatschek and Chris H. Wiggins and Erwin Frey}, volume = {74}, number = {4}, }, @article{ 2006:kundaje, author = {A. Kundaje and M. Middendorf and M. Shah and C. H. Wiggins and Y. Freund and C. Leslie}, editor = {2006/05/26 09:00}, title = {{A} classification-based framework for predicting and analyzing gene regulatory response.}, journal = {BMC Bioinformatics.}, volume = {7 Suppl 1}, pages = {S5.}, address = {Department of Computer Science, Columbia University, New York, NY 10027, USA.}, month = {Mar 20}, year = {2006}, 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. . .}, 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}, reference = {0 (Fungal Proteins) 0 (Heat-Shock Proteins) 0 (Molecular Chaperones)} }, @article{ 2006:koster, editor = {2006/01/28 09:00}, title = {Multiple events on single molecules: Unbiased estimation in single-molecule biophysics.}, address = {Kavli Institute of Nanoscience, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands.}, 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. . .}, keywords = {2006/01/28 09:00}, year = {2006}, author = {Daniel A. Koster and Chris H. Wiggins and Nynke H. Dekker}, journal = {Proc Natl Acad Sci U S A.}, volume = {104}, pages = {1750-1755}, }, @article{ 2006:hgd, author = {H. G. Dobereiner and B. J. Dubin-Thaler and J. M. Hofman and H. S. Xenias and T. N. Sims and G. Giannone and M. L. Dustin and C. H. Wiggins and M. P. Sheetz}, editor = {2006/08/16 09:00}, title = {{L}ateral membrane waves constitute a universal dynamic pattern of motile cells.}, journal = {Phys Rev Lett.}, volume = {97}, number = {3}, pages = {038102. Epub 2006 Jul 20.}, address = {Department of Biological Sciences, Columbia University, New York, New York 10027, USA.}, month = {Jul 21}, year = {2006}, 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.}, keywords = {2006/08/16 09:00} }, @article{ 2006:erratum, author = {A. S. Ada-Nguema and H. Xenias and Jake M. Hofman and Chris H. Wiggins and M. P. Sheetz and P. J. Keely}, editor = {2006/03/16 09:00}, title = {{T}he small {GTP}ase {R}-{R}as regulates organization of actin and drives membrane protrusions through the activity of {PLC}-$\epsilon$}, journal = {J Cell Sci.}, pages = {4364.http://jcs.biologists.org/cgi/content/full/119/20/4364}, address = {Department of Pharmacology, University of Wisconsin-Madison, Madison, WI 53706, USA.}, month = {Apr 1}, year = {2006}, 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. . .}, 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}, 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)} }, @article{ 2006:cai, author = {Y. Cai and N. Biais and G. Giannone and M. Tanase and G. Jiang and J. M. Hofman and C. H. Wiggins and P. Silberzan and A. Buguin and B. Ladoux and M. P. Sheetz}, editor = {2006/08/22 09:00}, title = {{N}onmuscle myosin {IIA}-dependent force inhibits cell spreading and drives {F}-actin flow.}, journal = {Biophys J.}, volume = {91}, number = {10}, pages = {3907-20. Epub 2006 Aug 18.}, address = {Department of Biological Sciences, Columbia University, New York, New York, USA.}, month = {Nov 15}, year = {2006}, 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. . .}, 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}, reference = {0 (Actins) 0 (Molecular Motor Proteins) EC 3. 6. 1. - (Nonmuscle Myosin Type IIA)}, }, @article{ 2006:aracne, author = {A. A. Margolin and I. Nemenman and K. Basso and C. Wiggins and G. Stolovitzky and Dalla R. Favera and A. Califano}, editor = {2006/05/26 09:00}, title = {{ARACNE}: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context.}, journal = {BMC Bioinformatics.}, volume = {7 Suppl 1}, pages = {S7.}, address = {Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.}, month = {Mar 20}, year = {2006}, 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. . .}, 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} }, @inproceedings{ 2005:medusa, journal = {Research In Computational Mol. Biology, Proc.}, volume = {3500}, pages = {538--552}, year = {2005}, entrydate = {2006/02/07}, title = {Motif discovery through predictive modeling of gene regulation}, 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}, author = {Manuel Middendorf and Anshul Kundaje and Mihir Shah and Yoav Freund and Chris H. Wiggins and Christina S. Leslie}, ee={http://dx.doi.org/10.1007/11415770_41}, publisher = {Springer} }, @article{ 2005:matstat-pre, eprint = {cond-mat/0306610}, author = {E. Ziv and R. Koytcheff and M. Middendorf and C. Wiggins}, editor = {2005/02/09 09:00}, title = {Systematic identification of statistically significant network measures.}, journal = {Phys Rev E Stat Nonlin Soft Matter Phys.}, volume = {71(1 Pt 2)}, pages = {016110. Epub 2005 Jan 10.}, address = {College of Physicians and Surgeons, Columbia University, New York, New York 10027, USA.}, month = {Jan}, year = {2005}, 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. . .}, 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}, }, @article{ 2005:kundaje-module, author = {Anshul Kundaje and Manuel Middendorf and Feng Gao and Chris Wiggins and Christina Leslie}, issn = {1545-5963}, pages = {194--202}, doi = {http://dx.doi.org/10.1109/TCBB.2005.34}, publisher = {IEEE Computer Society Press}, address = {Los Alamitos, CA, USA}, journal = {{\it IEEE/ACM Transactions on Computational Biology and Bioinformatics}}, url = {http://www1.cs.columbia.edu/compbio/module-clust/}, editor = {2006/10/19 09:00}, title = {{C}ombining sequence and time series expression data to learn transcriptional modules.}, volume = {2}, number = {3}, month = {Jul-Sep}, year = {2005}, 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. . .}, keywords = {2006/10/19 09:00}, }, @article{ 2005:Ziv05:infomod, eprint = {q-bio/0411033}, author = {E. Ziv and M. Middendorf and C. H. Wiggins}, editor = {2005/05/21 09:00}, title = {Information-theoretic approach to network modularity.}, journal = {Physical Review E}, volume = {71(4 Pt 2)}, pages = {046117. Epub 2005 Apr 14.}, address = {College of Physicians \& Surgeons, Department of Biomedical Engineering, Columbia University, New York, New York 10027, USA.}, month = {Apr}, year = {2005}, 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 coauthors at the 2004 APS March Meeting.}, keywords = {2005/05/21 09:00} }, @article{ 2005:Middendorf:droso, eprint = {q-bio/0408010}, author = {M. Middendorf and E. Ziv and C. H. Wiggins}, editor = {2005/02/25 09:00}, title = {Inferring network mechanisms: the Drosophila melanogaster protein interaction network.}, journal = {Proc Natl Acad Sci U S A.}, volume = {102}, number = {9}, pages = {3192-7. Epub 2005 Feb 22.}, address = {Department of Physics, College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA.}, month = {Mar 1}, year = {2005}, 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.}, 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}, reference = {0 (Drosophila Proteins)}, url = {http://www.pnas.org/cgi/content/abstract/102/9/3192}, }, @article{ 2004:yardena-pulled, author = {Y. Bohbot-Raviv and W. Z. Zhao and M. Feingold and C. H. Wiggins and R. Granek}, editor = {2004/04/20 05:00}, title = {Relaxation dynamics of semiflexible polymers.}, journal = {Phys Rev Lett.}, volume = {92}, number = {9}, pages = {098101. Epub 2004 Mar 3.}, address = {Department of Materials and Interfaces, Weizmann Institute of Science, Rehovot 76100, Israel.}, month = {Mar 5}, year = {2004}, 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.}, 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}, reference = {9007-49-2 (DNA)} }, @article{ 2004:0411028, year = {2004}, eprint = {q-bio/0411028}, title = {Predicting Genetic Regulatory Response Using Classification}, author = {Manuel Middendorf and Anshul Kundaje and Chris Wiggins and Yoav Freund and Christina Leslie}, journal = {Bioinformatics}, volume = {20}, number = {suppl. 1}, pages = {i232-240}, 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 resultsareavailablefromhttp://www.cs.columbia.edu/compbio/geneclass}, }, @article{ 2004:0410036, year = {2004}, eprint = {q-bio/0410036}, title = {On the Reconstruction of Interaction Networks with Applications to Transcriptional Regulation}, author = {Adam A. Margolin and Ilya Nemenman and Chris Wiggins and Gustavo Stolovitzky and Andrea Califano}, editor = {G Chechik and C Leslie and G Ratsch and K Tsuda}, conference={NIPS'04 Computational Biology Workshop}, }, @article{ 2004:0406016, eprint = {q-bio/0406016}, author = {Manuel Middendorf and Anshul Kundaje and Chris Wiggins and Yoav Freund and Christina Leslie}, title = {Predicting genetic regulatory response using classification: {Yeast} stress response}, journal = {Regulatory Genomics}, volume = {3318}, pages = {1--13}, year = {2005}, entrydate = {2006/02/07}, }, @article{ 2004:0402017, month = {Nov 22}, 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. . .}, 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}, year = {2004}, eprint = {q-bio/0402017}, title = {Discriminative Topological Features Reveal Biological Network Mechanisms}, author = {Manuel Middendorf and Etay Ziv and Carter Adams and Jen Hom and Robin Koytcheff and Chaya Levovitz and Gregory Woods and Linda Chen and Chris Wiggins}, journal = {BMC Bioinformatics}, volume = {5}, pages = {181}, date = {Nov 22}, reference = {0 (Saccharomyces cerevisiae Proteins)}, editor = {2004/11/24 09:00}, address = {Department of Physics, Columbia University, New York, USA}, 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}, }, @article{ 2003:0307551, year = {2003}, eprint = {cond-mat/0307551}, title = {The stochastic spectral dynamics of bending and tumbling}, author = {Chris H. Wiggins and Alberto Montesi and Matteo Pasquali}, }, @article{ 2003:0307482, year = {2003}, eprint = {cond-mat/0307482}, title = {A Computational Study of Mixing Microchannel Flows}, author = {J. P. Bennett and C. H. Wiggins}, }, @inproceedings{ 2002:cw_Dresden, author = {Chris H. Wiggins and Loic Le Goff}, year = {2002}, chapter = {3}, booktitle = {Function and Regulation of Cellular Systems: Experiments and Models}, editor = {A. Deutsch and M. Falcke and J. Howard and W. Zimmermann}, title = {Biopolymer Dynamics}, publisher = {Birkhaeuser-Verlag}, }, @article{ 2002:0206031, year = {2003}, eprint = {physics/0206031}, title = {Process Pathway Inference via Time Series Analysis}, author = {Chris H. Wiggins and Ilya Nemenman}, journal = {journal of Experimental Mechanics}, volume = {43}, issue = {3}, pages = {361-370}, }, @article{ 2001:cw_lighthill, author = {Chris H. Wiggins}, year = {2001}, title = {Biopolymer mechanics: stability, dynamics, and statistics}, journal = {Mathematical Methods in the Applied Sciences}, volume = {24}, pages = {1325--1335}, }, @article{ 2001:cw_chainpaper, author = {A. Belmonte and M. J. Shelley and S. T. Eldakar and C. H. Wiggins}, editor = {2001/09/05 10:00}, title = {Dynamic patterns and self-knotting of a driven hanging chain.}, journal = {Phys Rev Lett.}, volume = {87}, number = {11}, pages = {114301. Epub 2001 Aug 24.}, address = {W. G. Pritchard Laboratories, Department of Mathematics, Pennsylvania State University, University Park, Pennsylvania 16802, USA.}, month = {Sep 10}, year = {2001}, 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.}, keywords = {2001/09/05 10:01} }, @inproceedings{ 2000:cw_IMACS, author = {Chris H. Wiggins}, year = {2000}, title = {Darboux's Frame and {S}chrodinger's Equation for Biopolymers}, booktitle = {Sixteenth IMACS World Congress 2000 on Scientific Computation, Applied Mathematics, and Simulation}, editor = {M. Deville and R. Owens}, ISBN = {3-9522075-1-9}, }, @inproceedings{ 1999:cw_santafeproceedings, author = {T. R. Powers and R. E. Goldstein and Chris H. Wiggins}, year = {1999}, title = {Supercoiling Bacterial Filaments}, booktitle = {Biological Physics: Third International Symposium}, editor = {H. Frauenfelder and G. Hummer and R. Garcia}, pages = {271}, }, @article{ 1998:9802084, year = {1998}, eprint = {cond-mat/9802084}, title = {The Viscous Nonlinear Dynamics of Twist and Writhe}, author = {Raymond E. Goldstein and Thomas R. Powers and Chris H. Wiggins}, journal = {Physical Review Letters}, volume = {80}, pages = {5232--5235}, }, @article{ 1998:9707346, eprint = {cond-mat/9707346}, title = {Flexive and Propulsive Dynamics of Elastica at Low Reynolds numbers}, author = {Chris H. Wiggins and Raymond E. Goldstein}, year = {1998}, journal = {Physical Review Letters}, volume = {80}, pages = {3879--3882}, }, @article{ 1998:9703244, eprint = {cond-mat/9703244}, month = {Feb}, year = {1998}, 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. . .}, 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}, reference = {0 (Actins)}, title = {Trapping and Wiggling: Elastohydrodynamics of Driven Microfilaments}, author = {Chris H. Wiggins and Daniel X. Riveline and Albrecht Ott and Raymond E. Goldstein}, journal = {Biophysical journal}, pages = {1043-1060}, number = {2}, editor = {1998/04/09}, volume = {74(2 Pt 1)}, address = {Department of Physics, Princeton University, New Jersey 08544, USA}, }, @article{ 1997:9704225, eprint = {cond-mat/9704225}, title = {Elastohydrodynamic study of actin filaments using fluorescence microscopy}, author = {D. Riveline and Chris H. Wiggins and A. Ott and Raymond E. Goldstein}, journal = {Physical Review E}, volume = {56}, year = {1997}, pages = {R1330-R1333}, }, @article{ 1995:wiggins:GRL, title = {Magma Migration and magmatic solitary waves in 3D}, author = {Chris Wiggins and M. Spiegelman}, journal = {Geophysical Research Lett.}, volume = {22}, date = {15 May 1995}, year = {1995}, pages = {1289--1292}, }, @article{ 1993:wiggins:TRD, title = {A Transition Radiation Detector which Features Accurate Tracking and d{E}/dx Particle Identification}, 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 C. Wiggins and W. Willis}, journal = {IEEE Transactions on Nuclear Science}, volume = {40}, pages = {153--157}, year = {1993}, },