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Dive into the research topics where Eric Mjolsness is active.

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Featured researches published by Eric Mjolsness.


Bioinformatics | 2003

The systems biology markup language (SBML) : a medium for representation and exchange of biochemical network models

Michael Hucka; Andrew Finney; Herbert M. Sauro; Hamid Bolouri; John C. Doyle; Hiroaki Kitano; Adam P. Arkin; Benjamin J. Bornstein; Dennis Bray; Athel Cornish-Bowden; Autumn A. Cuellar; S. Dronov; E. D. Gilles; Martin Ginkel; Victoria Gor; Igor Goryanin; W. J. Hedley; T. C. Hodgman; J.-H.S. Hofmeyr; Peter Hunter; Nick Juty; J. L. Kasberger; A. Kremling; Ursula Kummer; N. Le Novere; Leslie M. Loew; D. Lucio; Pedro Mendes; E. Minch; Eric Mjolsness

MOTIVATION Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY The specification of SBML Level 1 is freely available from http://www.sbml.org/


IEEE Transactions on Pattern Analysis and Machine Intelligence | 2000

Fast and globally convergent pose estimation from video images

Chien Ping Lu; Gregory D. Hager; Eric Mjolsness

Determining the rigid transformation relating 2D images to known 3D geometry is a classical problem in photogrammetry and computer vision. Heretofore, the best methods for solving the problem have relied on iterative optimization methods which cannot be proven to converge and/or which do not effectively account for the orthonormal structure of rotation matrices. We show that the pose estimation problem can be formulated as that of minimizing an error metric based on collinearity in object (as opposed to image) space. Using object space collinearity error, we derive an iterative algorithm which directly computes orthogonal rotation matrices and which is globally convergent. Experimentally, we show that the method is computationally efficient, that it is no less accurate than the best currently employed optimization methods, and that it outperforms all tested methods in robustness to outliers.


Proceedings of the National Academy of Sciences of the United States of America | 2006

An auxin-driven polarized transport model for phyllotaxis

Henrik Jönsson; Marcus G. Heisler; Bruce E. Shapiro; Elliot M. Meyerowitz; Eric Mjolsness

Recent studies show that plant organ positioning may be mediated by localized concentrations of the plant hormone auxin. Auxin patterning in the shoot apical meristem is in turn brought about by the subcellular polar distribution of the putative auxin efflux mediator, PIN1. However, the question of what signals determine PIN1 polarization and how this gives rise to regular patterns of auxin concentration remains unknown. Here we address these questions by using mathematical modeling combined with confocal imaging. We propose a model that is based on the assumption that auxin influences the polarization of its own efflux within the meristem epidermis. We show that such a model is sufficient to create regular spatial patterns of auxin concentration on systems with static and dynamic cellular connectivities, the latter governed by a mechanical model. We also optimize parameter values for the PIN1 dynamics by using a detailed auxin transport model, for which parameter values are taken from experimental estimates, together with a template consisting of cell and wall compartments as well as PIN1 concentrations quantitatively extracted from confocal data. The model shows how polarized transport can drive the formation of regular patterns.


Pattern Recognition | 1998

New algorithms for 2D and 3D point matching : Pose estimation and correspondence

Steven Gold; Anand Rangarajan; Chien-Ping Lu; Suguna Pappu; Eric Mjolsness

Abstract A fundamental open problem in computer vision—determining pose and correspondence between two sets of points in space—is solved with a novel, fast, robust and easily implementable algorithm. The technique works on noisy 2D or 3D point sets that may be of unequal sizes and may differ by non-rigid transformations. Using a combination of optimization techniques such as deterministic annealing and the softassign , which have recently emerged out of the recurrent neural network/statistical physics framework, analog objective functions describing the problems are minimized. Over thirty thousand experiments, on randomly generated points sets with varying amounts of noise and missing and spurious points, and on hand-written character sets demonstrate the robustness of the algorithm.


Nature Protocols | 2012

Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy

Jonathan W. Young; James C. Locke; Alphan Altinok; Nitzan Rosenfeld; Tigran Bacarian; Peter S. Swain; Eric Mjolsness; Michael B. Elowitz

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 laboratorys 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 1–2 d for progressing through the analysis procedure.


international conference on computer graphics and interactive techniques | 1993

Animation of plant development

Przemyslaw Prusinkiewicz; Mark Hammel; Eric Mjolsness

The sequences of images used in Chapters 3 and 5 to illustrate the development of inflorescences and compound leaves suggest the possibility of using computer animation to visualize plant development. From a practical perspective, computer animation offers several advantages over traditional time-lapse photography.


Medical Image Analysis | 1997

A robust point-matching algorithm for autoradiograph alignment.

Anand Rangarajan; Haili Chui; Eric Mjolsness; Suguna Pappu; Lila Davachi; Patricia S. Goldman-Rakic; James S. Duncan

We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the one-to-one correspondences (or homologies) between point features extracted from the images and rejecting non-homologies as outliers. In this way, we attempt to account for the local, natural and artifactual differences between the autoradiograph slices. We have used the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated its robustness property using synthetically generated spatial mappings and provided an anecdotal visual comparison with the well-known iterated closest-point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.


Bioinformatics | 2003

Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations.

Bruce E. Shapiro; Andre Levchenko; Elliot M. Meyerowitz; Barbara J. Wold; Eric Mjolsness

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.


intelligent systems in molecular biology | 2005

Modeling the organization of the WUSCHEL expression domain in the shoot apical meristem

Henrik Jönsson; Marcus G. Heisler; G. Venugopala Reddy; Vikas Agrawal; Victoria Gor; Bruce E. Shapiro; Eric Mjolsness; Elliot M. Meyerowitz

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. RESULTS 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. AVAILABILITY 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. SUPPLEMENTARY INFORMATION http://www.computableplant.org/ and alternatively for a direct link to the page, http://computableplant.ics.uci.edu/bti1036 can be accessed.


Neural Computation | 1996

A novel optimizing network architecture with applications

Anand Rangarajan; Steven Gold; Eric Mjolsness

We present a novel optimizing network architecture with applications in vision, learning, pattern recognition, and combinatorial optimization. This architecture is constructed by combining the following techniques: (1) deterministic annealing, (2) self-amplification, (3) algebraic transformations, (4) clocked objectives, and (5) softassign. Deterministic annealing in conjunction with self-amplification avoids poor local minima and ensures that a vertex of the hypercube is reached. Algebraic transformations and clocked objectives help partition the relaxation into distinct phases. The problems considered have doubly stochastic matrix constraints or minor variations thereof. We introduce a new technique, softassign, which is used to satisfy this constraint. Experimental results on different problems are presented and discussed.

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Bruce E. Shapiro

California Institute of Technology

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Elliot M. Meyerowitz

California Institute of Technology

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David H. Sharp

Los Alamos National Laboratory

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Rebecca Castano

California Institute of Technology

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Victoria Gor

Jet Propulsion Laboratory

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N. A. Kolchanov

Russian Academy of Sciences

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Barbara J. Wold

California Institute of Technology

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