High throughput experiments have a unique capacity to provide large amounts of data that holds the potential to dramatically improve our knowledge about biological processes. Nevertheless, this feature becomes a handicap when researchers try to extract relevant conclusions from the vast oceans of information. Anaxomics solves this issue by putting your results into context and highlighting biologically and clinically relevant phenomena.
High throughput techniques (such as microarrays or mass spectrometry), with their ability to generate enormous amounts of data, introduce new challenges for analysis and interpretation. Classical statistical treatments are useful to identify relevant changes in gene expression or protein activity, but fail at providing a global picture of the biological phenomena under study. For this reason, Anaxomics offers a complementary approach based on systems biology that is able to extract biologically and clinically relevant information from your results.
Therapeutic Performance Mapping System (TPMS) generates mathematical models that faithfully simulate human physiology by adjusting to the available biological knowledge. Data from high throughput experiments is integrated into these virtual systems, so that it can be evaluated while taking into account the global, complex relationships that occur inside living beings. The models encompass various organization levels, ranging from individual cell types to entire human populations, so each analysis is performed in its due context.
When high throughput data about different groups of subjects (cohorts) is submitted, TPMS generates specific mathematical that simulate the unique properties of each cohort. Then, it identifies mechanistic explanations that help you understand the differences between populations and highlight any related clinical issue, such as pathological manifestations or adverse events in case of drug treatment.
This innovative interpretation contextualizes your results in the frame of well-established scientific knowledge, instead of analyzing it as a single, isolated experiment. In this manner, your own research is complemented and refined using results from past studies.