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Revision as of 16:56, 25 January 2018
MathWorks
MathWorks is proud to celebrate its 10th year of sponsoring iGEM. From 90 teams in 2008 to over 300 in 2017, MathWorks has shown its unwavering support to iGEM teams, through their continued generosity of offering complimentary software and technical mentoring of our growing community.
FREE MATLAB Software License
Includes 12 products!
MathWorks is pleased to once again sponsor the iGEM competition. As an iGEM Partner, MathWorks will provide complimentary software and technical support to all iGEM 2018 teams for use in the competition.
The complimentary software kit is designed specifically to cater to the needs of iGEM teams with the following 12 products:
- MATLAB
- Simulink
- SimBiology
- Curve Fitting Toolbox
- Symbolic Math Toolbox
- Optimization Toolbox
- Global Optimization Toolbox
- Bioinformatics Toolbox
- Statistics and Machine Learning Toolbox
- Partial Differential Equation Toolbox
- Image Processing Toolbox
- Computer Vision Toolbox
Request Your Free Kit
To request your Free Software Kit, please complete the Software Request Form.
Must be completed by team leader or faculty advisor.
Getting Started!
Interested in using MATLAB for your in silico projects?
We have put together a few video tutorials to help you get started.
For more resources, check out the competition page, product pages, webinars, and additional tutorials.
We are here to help!
Can't find what you are looking for? Have a specific question about using MATLAB tools for your iGEM work?
For Technical Mentoring:
Feel free to contact Fulden Buyukozturk, SimBiology Technical Expert, to request assistance via email at fulden.buyukozturk@mathworks.com
Follow Fulden on Twitter @fulden_b
MATLAB and SimBiology in Past iGEMs
See how former iGEMers used MathWorks tools, such as MATLAB and SimBiology, for a variety of modeling and simulation projects. A few examples from previous years!
Team Imperial College
used SimBiology to model their gene circuit in order to optimize their design and to predict system behavior under different conditions, such as different promoter strength or copy number.
Team Sydney Australia
used SimBiology to develop a gene regulation pathway model of their ethylene biosensor and to run sensitivity analysis in order to identify processes that have the most significant impact on biosensor output.
Team TU Delft
modeled 3D printing of their bacterial biofilm using MATLAB in order to determine the factors that have a strong influence on the biofilm strength.
Team Oxford
built stochastic and deterministic models of genetic circuits in order to tackle environmental pollution by developing a device for the detection and degradation of the hazardous yet indispensable solvent dichloromethane (DCM).
Team KU Leuven
modelled the pathway leading to Methyl Salicylate (MeS) production and performed sensitivity analysis, in order to predict MeS production and find the rate limiting steps.
Team Carnegie Mellon
derived an ODE model and used it with experimental time-course data to estimate key parameters like transcriptional and translational efficiency.
Team Slovenia
performed parameter scans to better characterize the effects of the parameters space on the behavior their bistable system, Switch IT.