MathWorks is pleased to sponsor the 2016 iGEM competition. As an iGEM partner, MathWorks will provide complimentary software and technical support to all iGEM teams for use in the competition.​ ​Please complete the Software Request Form (must be completed by faculty advisor) to request the complimentary software kit. In order to learn more about our sponsorship and to request complimentary software for use in the competition, visit our iGEM competition page.

The complementary software kit includes the following 12 products:

  • 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

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.

Technical Mentoring

Can't find what you are looking for? Have a specific question about using MATLAB tools for your iGEM work? We are here to help. 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

Good luck with your projects!

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 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.