[ { "id": "authors:pkht7-03z29", "collection": "authors", "collection_id": "pkht7-03z29", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20131028-083534657", "type": "article", "title": "Using cellzilla for plant growth simulations at the cellular level", "author": [ { "family_name": "Shapiro", "given_name": "Bruce E.", "clpid": "Shapiro-B-E" }, { "family_name": "Meyerowitz", "given_name": "Elliot M.", "orcid": "0000-0003-4798-5153", "clpid": "Meyerowitz-E-M" }, { "family_name": "Mjolsness", "given_name": "Eric", "clpid": "Mjolsness-E-D" } ], "abstract": "Cellzilla is a two-dimensional tissue simulation platform for plant modeling utilizing Cellerator arrows. Cellerator describes biochemical interactions with a simplified arrow-based notation; all interactions are input as reactions and are automatically translated to the appropriate differential equations using a computer algebra system. Cells are represented by a polygonal mesh of well-mixed compartments. Cell constituents can interact intercellularly via Cellerator reactions utilizing diffusion, transport, and action at a distance, as well as amongst themselves within a cell. The mesh data structure consists of vertices, edges (vertex pairs), and cells (and optional intercellular wall compartments) as ordered collections of edges. Simulations may be either static, in which cell constituents change with time but cell size and shape remain fixed; or dynamic, where cells can also grow. Growth is controlled by Hookean springs associated with each mesh edge and an outward pointing pressure force. Spring rest length grows at a rate proportional to the extension beyond equilibrium. Cell division occurs when a specified constituent (or cell mass) passes a (random, normally distributed) threshold. The orientation of new cell walls is determined either by Errera's rule, or by a potential model that weighs contributions due to equalizing daughter areas, minimizing wall length, alignment perpendicular to cell extension, and alignment perpendicular to actual growth direction.", "doi": "10.3389/fpls.2013.00408", "pmcid": "PMC3797531", "issn": "1664-462X", "publisher": "Frontiers Research Foundation", "publication": "Frontiers in Plant Science", "publication_date": "2013-10", "volume": "4", "pages": "Art. No. 408" }, { "id": "authors:6a8zh-ta653", "collection": "authors", "collection_id": "6a8zh-ta653", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20120217-105727788", "type": "article", "title": "Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy", "author": [ { "family_name": "Young", "given_name": "Jonathan W.", "clpid": "Young-J-W" }, { "family_name": "Locke", "given_name": "James C. W.", "clpid": "Locke-J-C-W" }, { "family_name": "Altinok", "given_name": "Alphan", "clpid": "Altinok-A" }, { "family_name": "Rosenfeld", "given_name": "Nitzan", "clpid": "Rosenfeld-N" }, { "family_name": "Bacarian", "given_name": "Tigran", "clpid": "Bacarian-T" }, { "family_name": "Swain", "given_name": "Peter S.", "clpid": "Swain-P-S" }, { "family_name": "Mjolsness", "given_name": "Eric", "clpid": "Mjolsness-E-D" }, { "family_name": "Elowitz", "given_name": "Michael B.", "orcid": "0000-0002-1221-0967", "clpid": "Elowitz-M-B" } ], "abstract": "Quantitative single-cell time-lapse microscopy is a powerful method for analyzing gene circuit dynamics and heterogeneous cell behavior. We describe the application of this method to imaging bacteria by using an automated microscopy system. This protocol has been used to analyze sporulation and competence differentiation in Bacillus subtilis, and to quantify gene regulation and its fluctuations in individual Escherichia coli cells. The protocol involves seeding and growing bacteria on small agarose pads and imaging the resulting microcolonies. Images are then reviewed and analyzed using our laboratory's custom MATLAB analysis code, which segments and tracks cells in a frame-to-frame method. This process yields quantitative expression data on cell lineages, which can illustrate dynamic expression profiles and facilitate mathematical models of gene circuits. With fast-growing bacteria, such as E. coli or B. subtilis, image acquisition can be completed in 1 d, with an additional 1\u20132 d for progressing through the analysis procedure.", "doi": "10.1038/nprot.2011.432", "pmcid": "PMC4161363", "issn": "1754-2189", "publisher": "Nature Publishing Group", "publication": "Nature Protocols", "publication_date": "2012-01", "series_number": "1", "volume": "7", "issue": "1", "pages": "80-88" }, { "id": "authors:qx0vj-1fq02", "collection": "authors", "collection_id": "qx0vj-1fq02", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:HARploscb06", "type": "article", "title": "Connectivity in the yeast cell cycle transcription network : inferences from neural networks", "author": [ { "family_name": "Hart", "given_name": "Christopher E.", "clpid": "Hart-C-E" }, { "family_name": "Mjolsness", "given_name": "Eric", "clpid": "Mjolsness-E-D" }, { "family_name": "Wold", "given_name": "Barbara J.", "orcid": "0000-0003-3235-8130", "clpid": "Wold-B-J" } ], "abstract": "A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein: DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico \"mutation\" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that \"network-local discrimination\" occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes.", "doi": "10.1371/journal.pcbi.0020169", "pmcid": "PMC1761652", "issn": "1553-734X", "publisher": "Public Library of Science", "publication": "PLoS Computational Biology", "publication_date": "2006-12", "series_number": "12", "volume": "2", "issue": "12", "pages": "1592-1607" }, { "id": "authors:mcj0g-63349", "collection": "authors", "collection_id": "mcj0g-63349", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20190909-133030018", "type": "article", "title": "A systems approach to morphogenesis in Arabidopsis thaliana: I. AGNS database", "author": [ { "family_name": "Omelyanchuk", "given_name": "N. A.", "clpid": "Omelyanchuk-N-A" }, { "family_name": "Mironova", "given_name": "V. V.", "clpid": "Mironova-V-V" }, { "family_name": "Zalevsky", "given_name": "E. M.", "clpid": "Zalevsky-E-M" }, { "family_name": "Shamov", "given_name": "I. S.", "clpid": "Shamov-I-S" }, { "family_name": "Poplavsky", "given_name": "A. S.", "clpid": "Poplavsky-A-S" }, { "family_name": "Podkolodny", "given_name": "N. L.", "clpid": "Podkolodny-N-L" }, { "family_name": "Ponomaryov", "given_name": "D. K.", "clpid": "Ponomaryov-D-K" }, { "family_name": "Nikolaev", "given_name": "S. V.", "clpid": "Nikolaev-S-V" }, { "family_name": "Mjolsness", "given_name": "E. D.", "clpid": "Mjolsness-E-D" }, { "family_name": "Meyerowitz", "given_name": "E. M.", "orcid": "0000-0003-4798-5153", "clpid": "Meyerowitz-E-M" }, { "family_name": "Kolchanov", "given_name": "N. A.", "clpid": "Kolchanov-N-A" } ], "abstract": "In systems biology, study of a complex and multicomponent system, such as morphogenesis, comprises accumulation of data on morphogenetic processes in databases, classification and logical analysis of this information, and computer simulation of the processes in question using the data accumulated and the results of their analysis. This paper describes realization of the first steps in a systems study of morphogenesis (annotating research papers, compiling information in a database, data systematization, and their logical analysis) by the example of Arabidopsis thaliana, a model object in plant molecular biology. The database AGNS (Arabidopsis GeneNet Supplementary; http://wwwmgs.bionet.nsc.ru/agns) contains the experimentally confirmed information from published papers on specific features of gene expression and phenotypes of wild-type, mutant, and transgenic A. thaliana plants. AGNS queries and logical data analysis with the aid of specially developed software makes it possible to model various morphogenetic processes from gene expression to functioning of gene networks and their contribution to the development of certain traits.", "doi": "10.1134/s0006350906070165", "issn": "0006-3509", "publisher": "Pleiades Publishing Ltd", "publication": "Biophysics", "publication_date": "2006-02", "series_number": "S1", "volume": "51", "issue": "S1", "pages": "75-82" }, { "id": "authors:ejhb4-je361", "collection": "authors", "collection_id": "ejhb4-je361", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20161202-133833294", "type": "article", "title": "Modeling the organization of the WUSCHEL expression domain in the shoot apical meristem", "author": [ { "family_name": "J\u00f6nsson", "given_name": "Henrik", "orcid": "0000-0003-2340-588X", "clpid": "J\u00f6nsson-H" }, { "family_name": "Heisler", "given_name": "Marcus", "orcid": "0000-0001-5644-8398", "clpid": "Heisler-M-G" }, { "family_name": "Reddy", "given_name": "G. Venugopala", "clpid": "Reddy-G-V" }, { "family_name": "Agrawal", "given_name": "Vikas", "clpid": "Agrawal-V" }, { "family_name": "Gor", "given_name": "Victoria", "clpid": "Gor-V" }, { "family_name": "Shapiro", "given_name": "Bruce E.", "clpid": "Shapiro-B-E" }, { "family_name": "Mjolsness", "given_name": "Eric", "clpid": "Mjolsness-E-D" }, { "family_name": "Meyerowitz", "given_name": "Elliot M.", "orcid": "0000-0003-4798-5153", "clpid": "Meyerowitz-E-M" } ], "abstract": "Motivation: The above-ground tissues of higher plants are generated from a small region of cells situated at the plant apex called the shoot apical meristem. An important genetic control circuit modulating the size of the Arabidopsis thaliana meristem is a feed-back network between the CLAVATA3 and WUSCHEL genes. Although the expression patterns for these genes do not overlap, WUSCHEL activity is both necessary and sufficient (when expressed ectopically) for the induction of CLAVATA3 expression. However, upregulation of CLAVATA3 in conjunction with the receptor kinase CLAVATA1 results in the downregulation of WUSCHEL. Despite much work, experimental data for this network are incomplete and additional hypotheses are needed to explain the spatial locations and dynamics of these expression domains. Predictive mathematical models describing the system should provide a useful tool for investigating and discriminating among possible hypotheses, by determining which hypotheses best explain observed gene expression dynamics. \n\nResults: We are developing a method using in vivo live confocal microscopy to capture quantitative gene expression data and create templates for computational models. We present two models accounting for the organization of the WUSCHEL expression domain. Our preferred model uses a reaction-diffusion mechanism in which an activator induces WUSCHEL expression. This model is able to organize the WUSCHEL expression domain. In addition, the model predicts the dynamical reorganization seen in experiments where cells, including the WUSCHEL domain, are ablated, and it also predicts the spatial expansion of the WUSCHEL domain resulting from removal of the CLAVATA3 signal. \n\nAvailability: An extended description of the model framework and image processing algorithms can be found at http://www.computableplant.org, together with additional results and simulation movies.", "doi": "10.1093/bioinformatics/bti1036", "issn": "1367-4803", "publisher": "Oxford University Press", "publication": "Bioinformatics", "publication_date": "2005-06-01", "series_number": "S1", "volume": "21", "issue": "S1", "pages": "i232-i240" }, { "id": "authors:qvfqp-9e320", "collection": "authors", "collection_id": "qvfqp-9e320", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:HARnar05", "type": "article", "title": "A mathematical and computational framework for quantitative comparison and integration of large-scale gene expression data", "author": [ { "family_name": "Hart", "given_name": "Christopher E.", "clpid": "Hart-C-E" }, { "family_name": "Sharenbroich", "given_name": "Lucas", "clpid": "Sharenbroich-L" }, { "family_name": "Bornstein", "given_name": "Benjamin J.", "clpid": "Bornstein-B-J" }, { "family_name": "Trout", "given_name": "Diane", "clpid": "Trout-D" }, { "family_name": "King", "given_name": "Brandon", "clpid": "King-B-W" }, { "family_name": "Mjolsness", "given_name": "Eric", "clpid": "Mjolsness-E-D" }, { "family_name": "Wold", "given_name": "Barbara J.", "orcid": "0000-0003-3235-8130", "clpid": "Wold-B-J" } ], "abstract": "Analysis of large-scale gene expression studies usually begins with gene clustering. A ubiquitous problem is that different algorithms applied to the same data inevitably give different results, and the differences are often substantial, involving a quarter or more of the genes analyzed. This raises a series of important but nettlesome questions: How are different clustering results related to each other and to the underlying data structure? Is one clustering objectively superior to another? Which differences, if any, are likely candidates to be biologically important? A systematic and quantitative way to address these questions is needed, together with an effective way to integrate and leverage expression results with other kinds of large-scale data and annotations. We developed a mathematical and computational framework to help quantify, compare, visualize and interactively mine clusterings. We show that by coupling confusion matrices with appropriate metrics (linear assignment and normalized mutual information scores), one can quantify and map differences between clusterings. A version of receiver operator characteristic analysis proved effective for quantifying and visualizing cluster quality and overlap. These methods, plus a flexible library of clustering algorithms, can be called from a new expandable set of software tools called CompClust 1.0 (http://woldlab.caltech.edu/compClust/). CompClust also makes it possible to relate expression clustering patterns to DNA sequence motif occurrences, protein\u2013DNA interaction measurements and various kinds of functional annotations. Test analyses used yeast cell cycle data and revealed data structure not obvious under all algorithms. These results were then integrated with transcription motif and global protein\u2013DNA interaction data to identify G1 regulatory modules.", "doi": "10.1093/nar/gki536", "pmcid": "PMC1092273", "issn": "0305-1048", "publisher": "Oxford University Press", "publication": "Nucleic Acids Research", "publication_date": "2005-04", "series_number": "8", "volume": "33", "issue": "8", "pages": "2580-2594" }, { "id": "authors:85kx2-6xb46", "collection": "authors", "collection_id": "85kx2-6xb46", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20111027-080208726", "type": "article", "title": "Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations", "author": [ { "family_name": "Shapiro", "given_name": "Bruce E.", "clpid": "Shapiro-B-E" }, { "family_name": "Levchenko", "given_name": "Andre", "clpid": "Levchenko-A" }, { "family_name": "Meyerowitz", "given_name": "Elliot M.", "orcid": "0000-0003-4798-5153", "clpid": "Meyerowitz-E-M" }, { "family_name": "Wold", "given_name": "Barbara J.", "orcid": "0000-0003-3235-8130", "clpid": "Wold-B-J" }, { "family_name": "Mjolsness", "given_name": "Eric D.", "clpid": "Mjolsness-E-D" } ], "abstract": "Cellerator describes single and multi-cellular signal transduction networks (STN) with a compact, optionally palette-driven, arrow-based notation to represent biochemical reactions and transcriptional activation. Multi-compartment systems are represented as graphs with STNs embedded in each node. Interactions include mass-action, enzymatic, allosteric and connectionist models. Reactions are translated into differential equations and can be solved numerically to generate predictive time courses or output as systems of equations that can be read by other programs. Cellerator simulations are fully extensible and portable to any operating system that supports Mathematica, and can be indefinitely nested within larger data structures to produce highly scaleable models.", "doi": "10.1093/bioinformatics/btg042", "issn": "1367-4803", "publisher": "Oxford University Press", "publication": "Bioinformatics", "publication_date": "2003-03-22", "series_number": "5", "volume": "19", "issue": "5", "pages": "677-678" } ]