Therefore, rather than committing to the first, perhaps most obvious interpretation, systems biology approach searches for all possible explanations of a given observation and utilizes a new perturbation experiment to distinguish between alternatives.
Clearly, a single experiment embodied by a single interventional trial will not provide a complete understanding of a given disease. Rather, different clinical trials will generate partially overlapping, but mostly complementary information that, when integrated on the bioinformatics level 11 , 45 , can ultimately reveal the true nature of the disease process in a manner analogous to how assembled pieces of the jigsaw puzzle reveal the underlying image.
This innovative clinical trial randomizes patients with newly diagnosed breast cancer to different treatment arms based on the analysis of patient-specific tumor biomarker profiles in relationship to accumulating knowledge from the ongoing analyses of outcomes of previously enrolled patients. The efficacy data are processed and incorporated in real time to generate an optimized predictive scheme for the next set of enrolled patients.
At the same time, the outcomes from different studied treatments are compared to each other so that ineffectual or toxic drugs can be abandoned and substituted with novel agents. I-SPY-2 TRIAL can be viewed as a compendium of multiple trials, which all benefit from the shared infrastructure and know-how biomarker analyses, mathematical modeling , providing societal value way beyond determination of the efficacy of a single agent.
The trial is sponsored by the Biomarker Consortium, a unique partnership between the Foundation for NIH FNIH and a large number of pharmaceutical companies, academic medical centers, and patient advocacy groups. As such, it represents the prime example of the creative funding solution s we advocated for in the previous section.
Given the scope and complexity of the systems under study, integration of knowledge from different sources will almost certainly require both biological e. Investigators can then draw on these data and the correlations derived from bioinformatics analyses both to better understand causal mechanisms and to guide the selection and design of future interventions. Although in this paper, we advocate implementation of systems biology methods to clinical trial methodology as a tool to gain predictive understanding of disease processes, we need to acknowledge that methods of systems biology will have much broader application to all of clinical medicine.
Systems biology-derived informatics that goes beyond the current paradigm of statistically based bioinformatics 53 , 58 , are an absolute prerequisite for personalized or precision medicine For example, although we currently utilize in clinical practice a large number of validated laboratory measurements, we judge the abnormality of these measurements independently of each other. However, they do not represent independent values, as many of them actually belong to shared functional modules.
If instead, we applied an understanding of the protocols that underlie relationships between these dynamic biomarkers in a living system to interpretation of laboratory results i. The predictive model should be able to pin-point laboratory error if obtained values in one biomarker are incompatible with obtained values in remaining biomarkers that are part of the same functional network or specify the type of homeostatic failure that is capable of producing the obtained results. We have little doubt that such methodology will be the basis of future diagnostic processes.
Furthermore, once we can model diseases computationally as an integrated, ongoing, and evolving process, we will also gain the ability to more effectively treat individual patients. Due to advances in molecular diagnostics, it is now well-appreciated that defects in different genes can lead to phenotypically similar disease expression Similarly, many neurodegenerative diseases have both polygenic common and monogenic rare disease variants.
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Indeed, failure of the regulation of one functional network can have multiple different causes and it is likely that optimal therapies may likewise differ depending on causal element s. Validated molecular, cellular and functional biomarkers will be able to pin-point specific types of regulatory failure that underlies disease expression in a particular subject from whom these measurements were obtained. For example, we envision that in the not so distant future, development of new, more sensitive, and cell-specific biomarkers of intrathecal inflammation will be able to select those patients with progressive MS who will benefit from novel immunomodulatory therapies.
Such biomarkers will also facilitate development of these novel treatments and serve as guidance for the treating clinician who is monitoring their efficacy in clinic. We will gain individualized prognostic insight, and, above all, we will be able to rationally select and optimize treatment combinations for this particular patient. Systems biology is not an approach in which the mindless application of powerful technologies can compensate for the lack of creative thinking.
From creative design of clinical protocols, accompanying functional assays and computational algorithms for data analyses, to imaginative data integration and reduction 60 , thoughtful adaptation of knowledge from previously identified protocols to new regulatory modules and biological systems, it is clear that success of systems biology requires pioneering visionaries as much as highly collaborative teams. While technical developments provide opportunities, conceptual advances are the true drivers of the progress.
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