BioBlocks is a web-based visual editor for describing experiments in Biology. It is based on Google’s Blockly and MIT Scratch. Experiments described in BioBlocks are automatically translated to machine specific code of a compatible hardware platform for automated execution of the experiment. An English translation and a graphical protocol workflow of the experiment are also generated. BioBlocks aims to remove the programming bottleneck so that biologists (non-programmers) can take advantage of automation in their work. Our goal in LIA is to make programming of Biological protocols simpler and fun!
Please find below some tutorials video which explain BioBlocks.
TUTORIAL 1: Bioblocks Library Description
TUTORIAL 2: Bioblocks Interface Explanation
TUTORIAL 3: BioBlocks Example Protocol
Preprint available on Biorxiv: A new improved and extended version of the multicell bacterial simulator gro
BactoSIM is mainly focused on in silico simulation of computational biology and ecology of bacterial conjugation. It is based on spatially explicit individual-based (or agent based) models.
EVOPER and R/REPAST
- Prestes García, A and Rodríguez-Patón, A — evoper: Evolutionary Parameter Estimation for ’Repast Simphony’ Models. https://CRAN.R-project. org/package=evoper (software).
The EvoPER, Evolutionary Parameter Estimation for ‘Repast Simphony’ Agent-Based framework (<https://repast.github.io/>), provides optimization driven parameter estimation methods based on evolutionary computation techniques which could be more efficient and require, in some cases, fewer model evaluations than other alternatives relying on experimental design.
- Prestes García, A and Rodríguez-Patón, A — rrepast: Running ’Repast Simphony’ models inside R environment. https://CRAN.R-project.org/ package=rrepast (software).
An R and Repast integration tool for running individual-based (IbM) simulation models developed using ‘Repast Simphony’ Agent-Based framework directly from R code. This package integrates ‘Repast Simphony’ models within R environment, making easier the tasks of running and analyzing model output data for automated parameter calibration and for carrying out uncertainty and sensitivity analysis using the power of R environment.