As the complexity of therapy-host interactions becomes increasingly apparent, the ability to predict the outcomes of interventions relies on a comprehensive dual approach of systematic measurement and integrative assembly of predictive computational models [1
]. This is reflective of a shift from a one-gene-per-disease mindset to understanding of multi-gene complex disease processes, where the outcome is dependent on a synthesis of the behavior of interrelated networks and systems as a whole [2
]. While systems biology has recently been applied to the study of diseases [3
] and specific molecules or pathways [4
], the scope of this review is to outline the potential for the application of systems biology practices to therapeutic design, and specifically to gene delivery. In particular we point out the advantages of this approach as regards personalized medicine. Systems biology principles have been applied only in part in the gene delivery arena so far, and by describing the approaches and opportunities, it is hoped that this review can spur further activity in this area.
Because the ‘systems’ designation is reflective of the complexity being studied rather than a specific limited set of tools, strict definitions are elusive; a recent analysis refers to Systems Biology studies as incorporating at least two of the following three characteristic components: High-throughput experiments; Computational models; and Bioinformatics [5
]. Systems Medicine is a subset of Systems Biology related to human therapeutics. Systems Biology studies are: (i) Quantitative.
Going beyond on/off, up/down, inhibit/repress representations of biological interactions [4
]. (ii) Integrative.
Compiling and analyzing data for multiple elements within the network and system, rather than a single readout [6
]. (iii) Spanning multiple scales.
Incorporating gene, message, protein and cell-level measurements and predictions, and possibly extended to tissue, organ and organism levels [8
]. (iv) Predictive.
Covering a sufficient fraction of the system to allow prediction of system behavior in untested states, and the ability to predict interventions to alter those states. Thus, systems biology approaches enable us to measure and predict multiple parameters simultaneously and under many conditions, giving a better picture of the state and dynamics of the system as a whole, rather than one specific element.
Gene therapy as it is currently practiced has failed to take advantage of the approaches of systems biology or systems medicine. Typically, patients receive treatments that are not based on their genomic or proteomic characteristics but rather on the up-regulation of the amount of a particular protein. As with other therapeutic approaches, empirically-derived maximum tolerated dose for a patient population, rather than maximally efficacious dose for the individual patient is used, which may not be the same, and thus the translation from pre-clinical animal model to human trial and to clinic is not optimal. Moreover, analysis of toxicity needs to consider both the effects of changes in protein concentration and the effects linked to the vector itself. Indeed, for plasmid DNA approaches the amount of DNA that can be delivered is often limited by the amount/cost of the vector, more so than the limiting effects of toxicity of the protein product.
Pre-clinical models strive to achieve homogeneity and reproducibility (e.g. using inbred strains of mice), while disease heterogeneity among patients that may manifest in heterogeneous activation of different genes, and in differential expression of multiple ligands, receptors and regulatory molecules may render certain therapies unsuccessful for some patients but effective for others. Taking advantage of systems biology can help to increase the success rate of translation. Possible reasons for failure of clinical trials include inappropriate selection of pre-clinical animal models, rendering the scale-up less accurate [10
]; systems biology can help to identify the key drivers of therapeutic success, indicating which correlations between animal and human are most relevant. In target selection, systems biology can help identify targets (e.g. ligands, receptors or transcription factors) which are most likely to be effective for the broadest number of patients or patient subpopulations [11
]. A systems approach also allows for comparison among multiple therapies targeting the same pathways, whether through delivery of genes, siRNA, shRNA or miRNA. Developing predictive and quantitative biomarkers for gene therapy would allow determination of patient subpopulations that are most likely to benefit from a given therapy, allowing the clinical trial to be more selective and increase the significance of successful outcomes and optimize dosing and timing of the gene therapy regimen [13