Software

• Enables sub-pixel (nanometer-scale) dynamic tracking of fluorescently-labeled dynamic polymers with simultaneous end-structure measurement for multi-filament polymers, such as microtubules.
• Demonstrated ~36nm measurement accuracy in vivo using LLCPK1 epithelial cells expressing GFP-tubulin (Demchouk et al., 2011) and ~15nm accuracy for in vitro purified tubulin (Gardner et al., 2011).
• A subsequent modification enables the code to obtain similar measurements from cells expressing fluorescently-labeped end-binding (EB) proteins (Seetapun et al., 2012).
• Supported version contains modifications to accommodate a variety of experimental conditions, and is provided as two Matlab files (function and call script) and support manual (.pdf), and is compatible with multiple data types using the Bio-Formats reader and Tiffread function. 

Current supported version

Prahl L.S., Castle B.T., Gardner M.K., Odde D.J. "Quantitative analysis of microtubule self-assembly kinetics and tip structure," Methods in Enzymology, 2014, 540:35-52. 

TipTracker_v3

Original publication

Demchouk A., Gardner M.K., and Odde D.J. “Microtubule tip tracking and tip structures at the nanometer scale using digital fluorescence microscopy,” Cellular and Molecular Bioengineering, 2011, 4:192-204. 

Tip tracker1_2    Tiffread

• Sub-pixel tracking of retrograde actin flows in cells expressing fluorescently-labeled actin or untransfected cells imaged by phase contrast (Chan & Odde, 2008).
• Analysis code reads kymographs (.tiff) of cell-edge actin features and measures flow speed of user-specified features using a 2D cross-correlation technique.
• Code is provided as a compressed folder containing the FlowTracker function, analysis script, and associated documentation. 

Reference publication

Chan C.E. and Odde D.J. “Traction dynamics of filopodia on compliant substrates,” Science, 2008, 322:1687-91. 

FlowTrack_v2
 

• Tracking software designed to measure positions of fluorescent microspheres from image stacks with sub-pixel accuracy using a Gaussian fit function.
• BeadTrack was developed to obtain spatiotemporal dynamics (~1-10Hz) of neuron traction forces from 200nm diameter fluorescent beads embedded in polyacrylamide gels (Chan & Odde, 2008).
• Code is provided as a compressed folder containing the BeadTracker function and associated documentation. 

Reference publication

Chan C.E. and Odde D.J. “Traction dynamics of filopodia on compliant substrates,” Science, 2008, 322:1687-91. 

BeadTrack_v2

• This plugin is designed to facilitate comparisons between stochastic simulations of cellular processes and digital fluorescence images obtained in cells by applying estimated imaging system blur and noise to simulated data.
• Model convolution is similar process to image deconvolution, which can fail to accurately measure the underlying fluorophore distribution under certain circumstances (Gardner et al., 2010).
• This technique is designed to provide a direct "apples to apples" statistical comparison of theory and experiment, such as done previously in studies of chromosome congression in mitotic budding yeast (Gardner et al., 2008).
• Downloadable plugin is supported by ImageJ. 

Reference publication

Gardner M.K., and Odde D.J. “Stochastic simulation and graphic visualization of mitotic processes,” Methods, 2010, 51:251-256. 

Model Convolution Plugin
 

• The cell migration simulator is based on the motor-clutch model for stiffness sensitive cell traction force generation.
• CMSv1.0 extends this model to incorporate actin-based protrusion dynamics, cytoskeletal compliance, mass conservation, and force balances to reproduce cell polarization and random motility in a 2D compliant microenvironment.
• CMSv1.0 has been featured in recent publications where it replicates cell migration behaviors in microenvironments of varying stiffness (Bangasser et al., Nat. Comm., 2017) and adhesiveness (Klank et al,. Cell Rep., 2017).
• The simulation code contains four Matlab files: the base code as a function (cms2Dv1_0.m), shell scripts for standard and paralell computing (cms2D_shell.m and cms2D_par_shell.m), and an analysis code (analyzeCMSoutputv1_0.m). A readme file is also included in the download folder.

Reference publications

Bangasser B.L., Shamsan G.A., Chan C.E., Opoku K.N., Tüzel E., Schlichtmann B.W., Kasim J.A., Fuller B.J., McCullough B.R., Rosenfeld S.S, andOdde D.J. "Shifting the optimal stiffness for cell migration." Nature Communications, 2017, 8: 15313. 

Cms v1.0

Kolade Adebowale, Ze Gong, Jay C. Hou, Katrina M. Wisdom, Damien Garbett, Hong-pyo Lee, Sungmin Nam, Tobias Meyer, David J. Odde, Vivek B. Shenoy, Ovijit Chaudhuri, "Enhanced substrate stress relaxation promotes filopodia-mediated cell migration," Nature Materials, 2021.

Cell2D (C++)

Sung Hoon Lee, Jay Hou, Archer Hamidzadeh, Sulaiman M. Yousafzai, Visar Ajeti, Hao Chang, Michael Murrell, David Odde,  Andre Levchenko. "A molecular clock controls periodically driven cell migration in confined spaces." Cell Systems (2022) 

Confined_migration_code
 

• Stochastic simulator for traction force generation based on the motor-clutch hypothesis, where actomyosin-based traction forces are transmitted to a compliant substrate through dynamic clutch bonds (Chan & Odde, 2008).
• Simulated cells are described by eight adjustable parameters — three for motors, five for clutches — plus a substrate of variable stiffness.
• The model captures a range of cellular mechanosensing behaviors by changing the operating parameters of motors and clutches (Bangasser et al., 2013).
• Simulation is provided as two separate Matlab codes. Running the simulation at a single stiffness (motorclutch1_1.m) enables analysis of cell traction dynamics by visualizing individual "load and fail" events (Chan & Odde, 2008).
• A second code (motorclutch2_3.m) outputs traction force and retrograde flow values over a range of substrate stiffness.
• A more recent version of the motor clutch model (Mekhdjian et al., 2017) is also available.

Reference publications

Bangasser B.L., Rosenfeld S.S., and Odde, D.J. "Determinants of maximal force transmission in a motor-clutch model of cell traction in a compliant microenvironment," Biophysical Journal, 2013, 105:581-592. 

motorclutch1_1    motorclutch2_3

Mekhdjian, A. H.; Kai, F. B.; Rubashkin, M. G.; Prahl, L. S.; Przybyla, L. M.; McGregor, A. L.; Bell, E. S.; Matthew Barnes, J.; DuFort, C. C.; Ou, G.; et al. "Integrin-Mediated Traction Force Enhances Paxillin Molecular Associations and Adhesion Dynamics That Increase the Invasiveness of Tumor Cells into a Three-Dimensional Extracellular Matrix". Molecular Biology of the Cell, 2017, 28(11):1467-1488. 

1D_motorclutch_model

S.J. Tan, A.C. Chang, C.M. Miller, S.M. Anderson, L.S. Prahl, D.J. Odde, A.R. Dunn, Regulation and dynamics of force transmission at individual cell-matrix adhesion bonds, Science Advances, 2020.

Cyclic6

Motor Clutch Model — Kelvin-Voigt substrate

MC1D_maxwellsub

xsub_kv_ode_noloop

• Monte Carlo simulator of microtubule assembly dynamics that can be used to predict microtubule (or other multi-filament) polymer assembly behaviors at the level of individual subunit addition and loss events (VanBuren et al., 2002).
• Adjustable parameters represent kinetic rates of tubulin dimer association and GTP hydrolysis, free energy values of lateral and longitudinal bonds between tubulin dimers, and concentration of soluble tubulin, and may be individually manipulated to tune simulated dynamic instability behaviors.
• Distributed code includes association rate penalties based on local tip structure (Castle & Odde, 2013) and 'pseudo-mechano-chemical' rules to simulate mechanical strain on bonds within the lattice (VanBuren et al., 2002; 2005).
• Model code is provided as two Matlab files - an initiation script and the simulator written as a function - plus associated documentation. 

Reference publication

VanBuren V., Odde D.J., and Cassimeris L.U. "Estimates of lateral and longitudinal energies within the microtubule lattice," Proceedings of the National Academy of Sciences USA, 2002, 99:6035-6040. Correction in 2004, 101:14989. 

simMTdynamics v1.2

• Monte Carlo simulation of microtubule dynamics and microtubule-kinetochore attachments during budding yeast mitosis.
• Allows the user to vary kinase/phosphatasse rate constants and microtubule dynamic instability parameters and test their effect on generating or resolving improper attachments to spindle poles. 

Reference publication

Tubman E.S., Biggins S., & Odde D.J. "Stochastic Modeling Yields a Mechanistic Framework for Spindle Attachment Error Correction in Budding Yeast Mitosis," Cell Systems, 2017, 4:645-650. 

ErrorCorrection_1

• The ‘Brownian Dynamics Tumor Simulator’ (BDTSv1.0) is a lattice-free, agent-based approach that simulates individual cells as spherical particles.
• Each cell obeys certain rules; they can migrate, grow and proliferate.
• The cell migration is modeled as Brownian motion and cell growth as two overlapping spheres which gradually pushed apart, while the total cell volume increases linearly.
• Two versions of BDTSv1.0 are included in the download file, one for overlapping and one for non-overlapping spheres. A readme file (README.txt) which describes each Matlab file is also included.

Reference publication

Klank R.L., Rosenfeld S. S., Odde D. J. "A Brownian Dynamics Tumor Progression Simulator with Application to Glioblastoma," Convergent Science Physical Oncology, 2018, 4:015001.

BDSims

Reference publication

Castle B. T., Carissa D., Hemmat M., Kline S., Tignanelli C., Rajasingham R. et al., "Biophysical modeling of the SARS-CoV-2 viral cycle reveals ideal antiviral targets," bioRxiv 

SARS-CoV-2_Biophysical_Model

Reference publication

Castle, Brian T., and David J. Odde. "Brownian dynamics of subunit addition-loss kinetics and thermodynamics in linear polymer self-assembly." Biophysical journal 105.11 (2013): 2528-2540.

BD_tubulin