Presented by Purdue University graduate students Kayal Madhivanan and Swetha Ramadesikan.

In order to develop novel and more efficient therapeutic approaches we need to understand disease mechanisms, which in turn requires the collection and analysis of biologically-relevant information. Unfortunately, the complex nature and high-information content of biological data, together with the lack of proper analysis methods make the information gathering and interpretation process very slow and imprecise. For example, following microscopy, image analysis is typically performed by visual examination of hundreds of cells or structures per experiment. This method, in addition to limiting the amount of samples that are to be analyzed, is not accurate and potentially poorly reproducible as it introduces human error and bias. Although some algorithms are currently available, they rely on subjective assumptions and are not suitable for widespread applications.

The objective of this student-initiated proposal is to develop tools to extract and analyze quantitative biological information related to the process of cell migration. Enhanced cell migration of cancer cells is the cause of metastasis (cancer-spreading) which currently accounts for 90% of cancer-related deaths. On the other hand, defective cell migration is observed in a developmental disease called Lowe Syndrome.