Francisco J. Romero Campero

          Research Fellow Juan de la Cierva
         Group on Natural Computing                                                            
         Higher Technical School of Computer Engineering
         Faculty of Mathematics

         University of Seville  


I have contributed to the development of the following software:

  • ChlamyNET, a Chlamydomonas reinhardtii Gene Co-expression Network: Chlamydomonas reinhardtii is a reference model organism for algal genomics and physiological studies. It is especially interesting to study the evolution of regulatory pathways between algae and higher plants. Additionally, it has recently gained attention as a potential source for biodiesel production. The recent accumulation of RNA-seq data allows researchers to study in detail its transcriptome under different physiological conditions. Up to now these studies had remained fragmented, making necessary integrative approaches based on systems biology. Here we present ChlamyNET a web-based tool for the exploration of a gene co-expression network representing the Chlamydomonas transcriptome under relevant physiolocial conditions.

  • An Arabidopsis thaliana Genome-wide gene co-expression network enriched with physiological data from metabolic processes and the floral transition. This webpage provides an online resource to explore a gene co-expression network integrating microarray and physiological data related to metabolic processes and the photoperiodic control of the floral transition.

  • Infobiotics Workbench is a computational framework implementing a synergy between executable biology, multi-compartmental stochastic simulations, formal model analysis and structural/parameter model optimisation for computational systems and synthetic biology. It provides a user-friendly front-end allowing the modeller to design in-silico experiments, analyse and visualise results using its four components:
    • A modelling language based on P systems which allows modular and parsimonious multi-cellular model development including geometric information.
    • A multi-compartmental stochastic simulator based on Gillespie’s Stochastic Simulation Algorithm for multi-cellular systems.
    • Formal model analysis using the stochastic model checkers PRISM and MC2 for the study of temporal and spatial model properties.
    • Structural and parameter model optimisation using evolutionary algorithms to automatically generate models whose dynamics match specified targets.