I am a Biology student working in the Carnevale lab at Temple University while continuing my role as an informatics scientist at a local analytical chemistry startup as both are critical to my goals to advance end-to-end protein analysis and design workflows through advanced computational methods.
Through my current industry research position, collaborations on omics workflows for mass spectrometry ion mobility data analysis have illustrated the complexities of drawing biological conclusions from dense, multidimensional data. Often, information can be lost while focusing on only portions of the raw data, limiting information available for discovery and often increasing analysis time to prohibitive lengths. Additionally, attempting to analyze novel data in existing tools requires intense review to ensure proper interpretation of results and retention of all valuable information. As instrumentation and analysis techniques advance rapidly, the need significantly increases for methods to provide rapid, accurate analysis and prevent missed or improper biological conclusions which may impact patient health or cause intense financial burden in drug development pipelines.
One such generalizable method involved parsing through feature-rich data to extract information and convert to a dimensionally reduced format, thus increasing the resources available for target identification and quantification. My work downstream on an ion mobility data pipeline enabled thorough analysis in tools specialized to handle nuances of their respective analytes such as lipids and peptides and focused on relating results back to the original data, yielding benefits from all components of the separation techniques rather than focusing on just one as was limited by prior workflows. Another such method for increasing quality of biological conclusions from complex data involved algorithms and tools specific to handling the full dimensionality with the context of the analyte of interest built into the very beginning of the pipeline. Enabling rapid and accurate analysis of rich biological data generates opportunity for novel drug target analysis and, depending on the analyte, can cross over into other important areas such as toxin analysis in exposomics.
I am currently working on a protein design project in the Carnevale lab where designs for a specific, novel target are generated based on their desired shape and scaffold. Evaluation of sequences in the target space to determine fitness for the intended biotechnological purposes is then followed by another scaffold and design evaluation. This involves a series of cutting-edge modeling software which presents similar challenges to the multidimensional work referenced where understanding and optimization of workflow components are critical to produce an accurate conclusion. Additionally, I would like to develop new tools and pipelines to merge some of these steps and strengthen the confidence in these conclusions, including opportunities for analytical testing. Work on this the pipeline and its tools will lead to viable therapeutic biomolecules to answer some of the most challenging current medical questions.