The importance of ‘big data’ in biology is increasing as vast quantities of data are being produced from high-throughput experiments. Techniques such as DNA microarrays are providing a genome-wide picture of gene expression levels, allowing us to investigate the structure and interactions of gene networks in biological systems. Inference of gene regulatory network (GRN) is an underdetermined problem suited to Metaheuristic algorithms which can operate on limited information. Thus GRN inference offers a platform for investigations into data intensive sciences and large scale optimization problems. Here we examine the link between data intensive research and optimization problems for the reverse engineering of GRNs. Briefly, we detail the benefit of the data deluge and the study of ALife for modelling GRNs as well as their reconstruction. We discuss how metaheuristics can solve big data problems and the inference of GRNs offer real world problems for both areas of research. We overview some current reconstruction algorithms and investigate some modelling and computational limits of the inference processes and suggest some areas for development. Furthermore we identify links and synergies between optimization and big data, e.g., dynamic, uncertain and large scale optimization problems, and discuss the potential benefit of multi- and many-objective optimization. We stress the importance of data integration techniques in order to maximize the data available, particularly for the case of inferring GRNs from microarray data. Such multi-disciplinary research is vital as biology is rapidly becoming a quantitative, data intensive science.