Showing papers by "Alfonso Rodríguez-Patón published in 2016"

A new improved and extended version of the multicell bacterial simulator gro

Abstract: gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use and provide new functionalities. The new version of gro is between one and two orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 20 minutes. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

Sensitivity analysis of Repast computational ecology models with R/Repast

Abstract: Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual-based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom-up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in-silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.

Analyzing Repast Symphony models in R with RRepast package

Abstract: In order to produce dependable results, the output of models must be carefully evaluated and compared to the experimental data. One of the main goals of analyzing a model is the understanding the effect of input factors on the model output. This task is carried out using a methodology known as sensitivity analysis. The analysis of Individual-based Models is hindered by the lack of simple tools allowing a complete and throughout evaluation without much effort. This kind of model tends to have a high level of complexity and the manual execution of a large experimental setup is generally not a feasible choice. Thus, it is required that model evaluation should ideally be simple and robust without demanding a high level of knowledge from modelers. In this work we present the RRepast, an open source GNU R package for executing, calibrating and analyzing Repast Symphony models directly from the R environment.

BioBlocks: Programming protocols in biology made easier

Abstract: The methods to execute biological experiments are evolving. Affordable fluid handling robots and on-demand biology enterprises are making automating entire experiments a reality. Automation offers the benefit of high-throughput experimentation, rapid prototyping and improved reproducibility of results. However, learning to automate and codify experiments is a difficult task as it requires programming expertise. Here, we present a web-based visual development environment called BioBlocks for describing experimental protocols in biology. It is based on Google9s Blockly and Scratch, and requires little or no experience in computer programming to automate the execution of experiments. The experiments can be specified, saved, modified and shared between multiple users in an easy manner. BioBlocks is open-source and can be customized to execute protocols on local robotic platforms or remotely i.e. in the cloud. It aims to serve as a 9de facto9 open standard for programming protocols in Biology.

EvoPER-An R package for applying evolutionary computation methods in the parameter estimation of individual-based models implemented in Repast

Abstract: Individual-based models are complex and they normally have an elevated number of input parameters which must be tuned in order to reproduce the experimental or observed data as accurately as possible. Hence one of the weakest points of such kind of models is the fact that rarely the modeler has the enough information about the correct values or even the acceptable range for the input parameters. Therefore, several parameter combinations must be checked to find an acceptable set of input factors minimizing the deviations of simulated and observed data. In practice, most of the times, is computationally unfeasible to traverse the complete search space to check all parameter combination in order to find the best of them. That is precisely the kind of combinatorial problem suitable for evolutionary computation techniques. In this work we present the EvoPER, an R package for simplifying the parameter estimation using evolutionary computation techniques. The current version of EvoPER includes implementations of PSO, SA and ACO algorithms for parameter estimation of models generated with the open source agent-based modeling toolkit Repast.