Lin, Xiaofei (Fay)
Fay is a graduate student in the Biochemistry, Molecular and Structural Biology Program. She received a B.A. degree in Biology from New York University, and is now working towards her Ph.D. in the laboratory of Dr. Alexander Hoffmann. She entered the CMB Training Program in 2018.
Mentor: Dr. Alexander Hoffmann
The signal-responsive transcription factor, nuclear factor kappa B (NFkB), responds to diverse external stimuli and initiates the appropriate gene expression for physiological functions such as lymphoid tissue development, immune, inflammatory, and environmental stress responses, and neuronal signaling. Impairment of NFkB regulation causes human pathologies such as chronic inflammatory diseases and cancers. As a result, the biomedical field has substantially invested in the development of inhibitors of the NFkB system. However, agents that directly inhibit NFkB often result in debilitating side effects that cause them to be clinically unusable. Understanding how NFkB distinguishes each pathogenic threat, injury, or inflammatory signal to produce the appropriate gene expression program is necessary for the development of pharmacological targeting techniques that minimize detrimental side effects.
The Dynamical Code hypothesis suggests that information about an external stimulus is encoded in the temporal profile of NFkB activity. For example, whereas NFkB activity is oscillatory in response to the proinflammatory cytokine tumor necrosis factor (TNF), its activity is steady when responding to a component of the outer membrane of gram-negative bacteria, lipopolysaccharide (LPS). Quantitatively understanding the stimulus-specific dynamical control of the NFkB network is critical for fine grained therapeutic targeting. In addition, signaling networks need to transmit information from external stimuli in the presence of high cell-to-cell variability. Understanding how cells undergo the appropriate stimulus response in lieu of this variability is unknown.
My research aims to use mathematical modeling in the context of high cell-to-cell variability to elucidate the network behavior of the NFkB system for applications in therapeutics. Specific aims of my project include identifying key stimulus-specific NFkB dynamics, pinpointing crucial sources of molecular noise that diminish NFkB information encoding, and understanding the mechanisms for decoding NFkB dynamics into stimulus-specific gene expression.
