A randomized study of precision medicine that was based on genomic profiles found no improvement in survival rates, and fewer than 10% of patients who had advanced cancer had mutations that could be treated. This is the case despite the fact that next-generation sequencing has made it feasible to identify a diverse collection of mutations in a variety of cancer types. The current limitation of genomics-based personalized medicine is that there are not many effective and long-term treatment options available. This is due to the significant heterogeneity that exists at higher molecular biology information levels as well as the unknown intricate interactions that occur at those levels.
We made the assumption that most cancers, in particular, ought to be primarily regarded as signaling disorders rather than genetic diseases. This simplified ternary plot of the central dogma in molecular biology illustrates the amount of noise, the distance to the phenotypic level, and the availability of data. The technical or inherent (i.e., caused by the biological stochastic process) noise of the data has a cumulative impact on the accuracy of our predictions related to the next level up to the phenotype level in each level of molecular information.
As a result, in the paradigm of systems biomedicine, we place a greater emphasis on data that is available and is closer to the phenotype level.
In particular, for the purpose of rationally designing drug combinations, we develop an approach based on systems pharmacology. The specific goals are to (i) first predict the best drug combination regimens for related patient subclasses using network modeling (dry-lab experiments), (ii) evaluate and prioritize them in wet-lab experiments based on in vitro synergy, toxicity, and efficacy analysis, (iii) explore the molecular targets of the potent drug combinations based on PTM-centric thermal proteomics and metabolomics, and (iv) translate the findings of drug combinations into treatments.
In other words, our goal is to provide answers to major unmet needs in both society and the healthcare system by developing (i) treatments for cancer patients that are more effective and cause fewer side effects, and (ii) a molecular explanation of effective combinatorial therapy.
The primary domains of our investigation can be categorized into the following sections:
We are interested in formulating the complex behavior of both microscopic and macroscopic biological phenomena by utilizing a variety of network models.
We devise proteomic methods and put them to use in order to glean additional information from the primary functional units (proteins) that make up cellular systems.
We use targeted and untargeted metabolomics to provide orthogonal supportive evidence of protein functions.
We devise and implement a number of data mining techniques in order to gain more knowledge regarding biologically complex systems.