Maojo et al methods in biomedical imaging. For example, QDs generate signals of long duration that can be used to improve image quality. Advanced informatics methods could then link tis- sue banks with images of tissues and histology results to provide enhanced image annotation down to the nano level. The images could also be linked to electronic health records as needed. Modeling Molecular modeling and simulation techniques are central to systems biology research. In the nano world, they will be the key to the bridging of nano-level effects with aggregated, molecular, cellular, tissue, organ, and organismal system level effects. Quantum mechanics, molecular modeling, and simulation techniques provide the scientific basis for analyz- ing and understanding the basic physical, chemical, and bio- logical properties of nanoparticles and nanomaterials.99 Their results provide new insights about their specific interactions with biological systems. Computer-intensive methods like hybrid quantum mechanic and molecular dynamic simula- tions facilitate the characterization of nanoparticle properties. This also provides substantial information about biological phenomena related to the interactions between nanoparticles and physiological systems,100 exploring interactions at dif- ferent scales. Through these simulations, one can study the fundamental physico-chemical properties of nanoparticles. High-performance computing infrastructures – such as grid, cloud computing, or dedicated supercomputers – can help accelerate this work. Bewick et al101 have reported on the complexity arising from the inherent characteristics of natural biological systems. These include their heterogeneity, their multiscale space–time interactions, the noise generated in physical systems at the nanoscale, the strong coupling between processes that occur at different scales, and the challeng- ing dimensionality size of data needed to represent typical biological systems.101 Jaramillo-Botoro et al102 have suggested the typical components of a nanomedical model in terms of a five-level theoretical hierarchy: (1) quantum mechanics to determine electronic states, (2) force fields that result from averaging the electronic states and thereby obtaining atom-based forces, (3) simulations of atomic interactions based on force fields, (4) mesoscale or course-grained descriptions that average over many atoms, and (5) continuum mechanics using distributed properties for membranes, cells, tissues, and organs. As in other biomedical and engineering areas, if insufficient complexity is assumed, the descriptions will not 3874 submit your manuscript | www.dovepress.com Dovepress Dovepress be adequate or accurate for representing the phenomena. However, if too much complexity is assumed, mathematical models may prove too complex to be useful from a practical perspective. According to Bewick et al,101 the challenges for nanobiosystem modeling are twofold. First, we must develop improved techniques for dealing with nanoscale biological complexity from a computational perspective. Second, we need to understand the role of biophysical and chemical complexity itself in nanobiosystem behavior.103 In this regard, there is no Protein Data Bank-like entity to develop, validate, and curate nanoparticle or nanomaterial structures – except the Collaboratory for Structural Nanobiology.104 This modeling cannot be done without several different layers of structural modeling. It is not possible to discover structure–property relations unless we already know the structural motifs of interest, for which researchers need to build enough knowledge across different platforms. Thus, modeling must be linked to what we have mentioned already regarding the need to establish networks of researchers. The latter can collaborate by sharing both structural and predictive models, which will be essential to accelerate research and translation to clinical practice.81 Data and text mining for nanomedical research Over the past decade, the authors have carried out an extensive examination of text mining research, particularly for bioinformatics problems.105–107 We later discuss some examples of the authors’ research in this area. Using data mining approaches, researchers try to extract, confirm, or discover new knowledge from large databases. Data mining was traditionally used in large medical and biological databases to generate or confirm research hypotheses. Their application has been funda- mental for extracting knowledge patterns – for example, decision trees or association rules – either for descriptive or predictive tasks. Most of these tasks are based on inductive approaches. In the context of nanobiology, for instance, it is possible to use various mining strategies for predicting the potential effects of engineered nanomaterials (ENMs) based on their chemical properties, their mechanisms of action and the biological pathways involved. This requires standardized data collection and warehousing of very diverse sets of data and metadata types and formats.81 This means that adequate informatics infrastructure will be essential to facilitate storage and data sharing of mul- tilevel data related to research, development, translation, and regulation. International Journal of Nanomedicine 2012:7