Team:Groningen/Glossary

From 2009.igem.org

G

Gvp genes

gas vescicle polycitronic genes

I

Iteration

[http://www.upedu.org/ UPEDU] uses iterations as the smallest element of time on which the Team:Groningen/Project_Plan is based. Specifically, there is an iteration plan for each iteration. The idea behind an iteration is that during an iteration all artifacts (of all disciplines) of the project are brought one step close to completion (although there will obviously be differences in emphasis during the life cycle of the project). This is in contrast to waterfall methods, where every discipline is brought to completion sequentially (so first all the requirements are finalized, then the analysis and design work is done, then the implementation, etc.).

M

Method of lines

Is used to convert a partial differential equation to a system of ordinary differential equations. For example, if you have a distribution that changes over time (described by a partial differential equation) you can discretize the distribution in space and create an ordinary differential equation for each cell.

O

Ordinary Differential Equation (ODE)

Equation describing a function by relating it's derivative (in time) to the function itself.

P

Partial Differential Equation (PDE)

Equation describing the behaviour of a function of multiple variables along a certain variable by relating it's derivative (in that variable) to the function itself (and possible derivatives in other variables).

Phase

[http://www.upedu.org/ UPEDU] recognizes four phases in each project, the Inception, the Elaboration, the Construction and the Transition. All of these phases have their own set of milestones and are subdivided in iterations.

S

Stochastic Differential Equation (SDE)

A differential equation with a term involving a special random variable (the standard Wiener process). These can be solved in much the same way as normal differential equations, except that they (obviously) will not have exactly the same behaviour each time they are simulated.

Stochastic Simulation Algorithm (SSA) / Gillespie model / Continuous-Time Markov Model (CTMM)

Instead of describing reactions using (ordinary) differential equations it is also possible to explicitly simulate each and every reaction event stochastically. This is completely different from using a stochastic differential equation, as it does not explicitly add noise but simply randomly chooses a sequence of possible (reaction) events. This is considered a very high quality simulation technique, but also quite slow if lots of reagents are present, see chapter 17 of Schwartz2008.

T

Tau-leap algorithm

This is a faster version of SSA that processes multiple reactions at the same time when possible (when the concentrations are high enough). Note that there are also other techniques for improving SSA's performance (see chapter 17 of Schwartz2008).