Santiago Schnell

By | | No Comments

Dr. Schnell works at the interface between biophysical chemistry, mathematical and computational biology, and pathophysiology. As an independent scientist, his primary research interest is to use mathematical, computational and statistical methods to design or select optimal procedures and experiments, and to provide maximum information by analyzing biochemical data. His laboratory deals with the following topics:

(i) Development and implementation of mathematical, computational, and statistical methods to identify and characterize reaction mechanisms.

(ii) Investigate and test performance design of experiments or standards to quantify, interpret and analyze biochemical data.

(iii) Development of new algorithms and software to analyze biochemical data.

The key objective of my research is to create suitable standards and appropriate support of standards leading to reproducible results in the biochemical sciences. Reproducibility is central to scientific credibility. Meta-research has repeatedly shown that accurate reporting and sound peer-review do not by themselves guarantee the reproducibility of scientific results. One of the leading causes of poor reproducibility is limited research efforts in quantitative biology and chemometrics. In my laboratory, we are developing new ways to assess the reproducibility of quantitative findings in the biochemical sciences.

As a team scientist, Dr. Schnell’s research interest is to investigate complex biomedical systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole. His collaborators are primarily basic scientists who focus on the identification of molecular, biochemical or developmental mechanisms associated with diseases. To this end, Dr. Schnell’s expertise plays a central role in the identification of these mechanisms. Using mathematical and computational models, Dr. Schnell can formulate several hypothetical model mechanisms in parallel, which are compared with independent experimental data used to construct the models. The resulting comparisons are then independent between models, and any models that satisfy statistical measures of similarity will be used to make predictions, which will be tested experimentally by his collaborators. The model validated by the experiments will be considered the mechanism capable of explaining the behavior of the systems.