Experimental design and statistical analysis should be dealt with rigorously, as they play essential roles in discovery and validation in systems biology and medicine . Study design is often the weakest point of complex molecular studies in systems biology and medicine. For example, patients with a disease such as ovarian cancer may be compared with normal controls to discern aberrant regulation of pathways. If controls are not carefully selected to be comparable with patients demographically and in other covariates (age, sex, income, social class), then differences observed may be attributable to factors other than the disease.
Researchers are often unduly optimistic about sample sizes required to show differences, and they fail to consider many confounding effects. Interindividual variability in humans can be large, often the largest effect in a study. This provides an avenue for exploration of individual effects, leading to personalized medicine, but also can make detection of differences across subjects quite difficult.
High-throughput technologies have introduced new challenges to experimental design and interpretation of results. Avoiding false positives may result in difficulties in identifying true positive. Standard approaches to correcting for multiple-testing on datasets generated by global analysis, such as expression microarray, rely on the incorrect assumption that each value is independent of other values. More recent approaches do not fully resolve this problem . Greatly increasing sample sizes is generally impractical. A more practical approach is to make increased use of a priori biologic knowledge, either by trimming the list of analytes to a relatively small number for which the multiple-testing correction is modest, or by testing pathways or groups of genes . This is usually done not by testing every group of genes defined by a GO term or a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, but by selectively testing those thought to be of importance. Because this more-focused approach, in its effort to improve specificity, is ontology dependent, it may bear a subjective element as to the certainty of prior knowledge. It, therefore, also carries the risk of lacking sensitivity.
Addressing the previously mentioned challenges may have direct clinical implications. A frequent problem encountered by clinicians is that patients appearing to have the same disease may not respond to the same treatment. Some patients even experience severe adverse effects from the treatment. Variable treatment response is also one of the most important causes of the huge costs involved in drug development. Taken together, these cause both increased suffering and costs. Ideally, physicians should be able, routinely and noninvasively, to measure a few diagnostic biomarkers to personalize medication for each patient. At present, not enough knowledge exists about the causes for variable treatment responses in most common diseases. However, recent studies of genetic markers for response to treatment with anticoagulants indicate that personalized dosage may become a clinical reality within the next 5 to 10 years . The main problems involved in finding markers for personalized dosage are that each complex disease may involve altered interactions between hundreds or thousands of genes that can differ among patients. This heterogeneity may, in turn, depend on both genetic and environmental factors. In addition to this complexity, significant problems are involved in clinical research. Ideally, a study aiming to find markers for personalized medication would involve a known external cause, a key cell type, and a read-out, all of which can be studied experimentally in patient samples.
For most complex diseases, all of these factors are not readily available. It is therefore important to find model diseases, in which all those factors can be studied together in patient samples by using high-throughput technologies and systems biologic principles . Such model diseases might be used to develop and apply the methods required to find markers for personalized medicine.
It also has been suggested that the same methods might be applied to find markers to predict the risk of developing disease . If successful, this may lead to a new era of preventive medicine. Finally, the methods may be of great value for drug development. If it were possible to predict which patients respond to medication, this would result in increased efficacy and reduced risk of not being able to market drugs that have been developed at great cost. Conversely, delineation of patients that do not respond to a medication may help to develop new drugs for that specific subgroup. We suggest that acute inflammatory diseases, such as severe trauma, sepsis, and pancreatitis, might be very attractive test beds for the development of such methods. Similarly, chronic ailments, such as diabetes and other autoimmune disorders, meet several of the criteria mentioned earlier and are of prominent clinical and societal relevance.