Editorial Computational and Bioinformatics Techniques for Immunology Francesco Pappalardo, 1 Vladimir Brusic, 2 Filippo Castiglione, 3 and Christian Schönbach 2 1 Department of Drug Sciences, University of Catania, 95125 Catania, Italy 2 School of Science and Technology, Nazarbayev University, Astana 010000, Kazakhstan 3 Istituto Applicazioni del Calcolo “M. Picone”, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy Correspondence should be addressed to Francesco Pappalardo; fp@francescopappalardo.net Received 22 July 2014; Accepted 22 July 2014 Copyright © Francesco Pappalardo et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computational immunology and immunological bioinfor- matics are well-established and rapidly evolving research fields. Whereas the former aims to develop mathematical and/or computational methods to study the dynamics of cellular and molecular entities during the immune response [1–4], the latter targets proposing methods to analyze large genomic and proteomic immunological-related datasets and derive (i.e., predict) new knowledge mainly by statistical inference and machine learning algorithms. Since immunology provides key information about basic mechanisms in a number of related diseases, it represents the most critical target for medical intervention. ere- fore an advance in either computational or bioinformatics immunology research field has the potential to pave the way for improvement of human health through better patient- specific diagnostics and optimized immune treatment. In this special issue, we take an interest from mathe- maticians, bioinformaticians, computational scientists, and engineers together with experimental immunologists, to present and discuss latest developments in different subareas ranging from modeling and simulation to machine learn- ing predictions and their application to basic and clinical immunology. Of the possible directions for development in immune- informatics special interest is raising for models focusing on innate-adaptive immune response activation, immune senes- cence, and multiscale and multiorgan models of immune- related diseases and for models accounting for cell trafficking in lymph nodes and/or in the lymphatic mesh as in “Modeling biology spanning different scales: an open challenge” by F. Castiglione et al. Exploring the connections between classical mathemat- ical modeling (at different scales) and bioinformatics pre- dictions of omic scope along with specific aspects of the immune system in combination with concepts and methods like computer simulations, mathematics and statistics for the discovery, design, and optimization of drugs, vaccines, and other immunotherapies represents a hot topic in computa- tional biology and systems medicine [5, 6]. e review from F. Castiglione et al. calls attention to the importance of the different time-space scale involved in biological phenomena and in particular in the immune system. It dissects the problem and discusses various tech- niques that have been developed in scientific areas other than computational biology. In their paper S. Jarrah et al. illustrate a simple ODE model to investigate the role of the immune response in mus- cle degeneration and regeneration in the mdx mouse model of Duchenne muscular dystrophy. eir model suggests that the immune response contributes substantially to the muscle degeneration and regeneration processes and predicts in a certain parameter range a permanent immune activation damaging muscle fibers. In the paper contributed by T. Clancy and E. Hovig, the authors propose a new method to integrate expression profiles and protein-protein interaction (PPI) data. Bioin- formatics techniques are used to study differential protein interaction mechanisms across the entire immune cell lin- eages and the transcriptional activators and modules and are Hindawi Publishing Corporation BioMed Research International Article ID 263189