380066 Engineering Solutions for Systems Biology: Microfluidics and Unbiased Statistical Tools to Uncover Genetic Relationships through Phenotyping
Systems biology focuses on studying living organisms as an ensemble of molecular, dynamic networks that are orchestrated to perform biological functions. The main questions are directed to understanding how all the players interact to accomplish certain goals within a cell or within an organism. This new approach to biology necessitates new experimental paradigms that enable perturbing biological networks and extracting information in a multidimensional scale. Just like systems biology aims at finding how biological ensembles work together (and what happens to them with disease), assays and experimental platforms should focus on extracting ensemble results rather than individual features. Experimental approaches need to be able to look deeper into the information provided by experiments and need to allow better control of the perturbations assayed.
Here I show how we have applied an integrated approach to perform biological studies enabled by microfluidics, automation, mathematical algorithms and machine learning with the nematode C. elegans (an excellent model for systems biology). I present how microfluidics can be coupled to unsupervised data extraction tools to obtain deep information and enable high-content studies of complex biological systems. Specifically, I discuss how integrated engineering platforms can be used to elucidate relevant questions in neuroscience, such as synaptic plasticity, learning and the effects of aging on neurological health. We have applied one such system to perform automated genetic screens and to identify differences in synaptic patterns, hidden to the naked eye. These new experimental platforms are promising in our challenging search for a systems-level understanding of biological processes.