Megan McClean
Megan McClean
Assistant Professor
Room 3156 Engineering Centers Building, 1550 Engineering Driv
(608) 890-0416
The McClean Lab research focuses on experimentally and theoretically understanding signal processing in biological networks so that these networks can be controlled and engineered for applications in biotechnology and biomedicine. We are interesting in understanding the following questions about biological signal processing: Signaling Specificity: How do signaling pathways that share multiple components or regulatory interactions respond specifically to their input without crosstalk? Under what conditions is crosstalk desirable? Determination of in vivo kinetics: How fast does a signal propagate through a signaling pathway? How does this determine the bandwidth, or information carrying capacity, of the pathway? Transcription factor regulation: How are transcription factors regulated to produce appropriate transcriptional outputs? How can the dynamics of transcription factor activity be used to differentially regulate transcriptional targets? What is the transfer function between transcription factor concentration and transcriptional output? Controllability: How can we control biological networks for applications in biomedicine and bioengineering? Thinking of a biological network as an electrical circuit is a useful analogy. Electrical circuits take input voltages and convert them to appropriate output voltages so that a desired outcome is achieved. In much the same way, biological networks take particular input stimuli and convert them into appropriate biological outputs. Engineers uncover the inner workings of electrical circuits by measuring the transfer function between input voltage and output voltage. In much the same way, I use microfluidic technology to generate dynamic inputs to interrogate biological networks and measure their output. A major focus of my research moving forward is to develop novel microfluidic and optogenetic tools for generating more sophisticated and dynamic perturbations. By combining these perturbative experimental approaches with pathway models, we can uncover the underlying connections and kinetics in biological signaling and regulatory networks. Furthermore, we can control and engineer these networks for desirable applications.
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