Given the career outcomes in multiple fields of biotechnology, a need has emerged to broadly train the predoctoral students in rigorous experimental design and transparency to enhance reproducibility for diverse careers in the biomedical research workforce. To this end we have developed the following training modules for advancing rigor and reproducibility in research for students at Rensselaer.
1. Chemical Resource Authentication
This module will provide the best practices used in the characterization of small molecules and protein therapeutics such as antibodies. It will include quantitative analysis of mixtures and minor components commonly used to assess composition of growth media and cell culture reagents, purity of starting materials and end products in drug manufacturing, real-time reaction monitoring, and decomposition in stability studies. Methods used in structure validation and proof of composition of small molecules will also be presented.
2. Animal Modeling
This module will cover murine study design including strain identification, food and micobiome analysis, transgenic validation and relevant biological variables (age, sex, race, weight etc.).
3. Cell Line Authentication
This module will cover cell line authentication by short tandem repeat (STR) profiling, as well as karyotyping and testing for contaminating microorganisms such as mycoplasma. In addition, students will learn about molecular characterization and functional validation of stem cell lines, including self-renewal and differentiation potential.
4. Experimental Design and Enterprise Development
This module will introduce various models, tools and contemporary practices for conducting and establishing a successful biomedical research project in academic and industrial environments. This unit will emphasize identifying an unmet need for biomedical research and innovation, development and evaluation of key value propositions, rigorous experimental design for hypothesis and non-hypothesis laboratory research, considerations of biological diversity and variables, sampling and statistical analyses.