To decide between irreversible cell fates such as growth, differentiation or death, each cell processes information about its environment through a network of molecular circuits referred to as ‘signaling pathways’. The output of these signaling pathways often converge on gene transcription, encoding proteins that alter the cell’s biochemical state. Our research combines principles of systems, synthetic and computational biology to understand how information flows through these signaling pathways. By observing input-output relationships in the same cell using microfluidics, live-cell dynamics and single-molecule microscopy, we aim to decode the signaling ‘language’ and develop mathematical models of information flow with single-cell resolution. Our ultimate goal is to understand how population-level responses emerge from single-cell heterogeneity and to rationally manipulate cell fate decisions in disease.

A quantitative single-cell perspective of signaling:

While cells of a clonal population ostensibly express the same components of a signaling network, subtle differences between cells in the abundance or activity of signaling molecules can lead otherwise identical cells to distinct fates. Using real-time microscopy technologies, and genetically modified cells that express fluorescent biosensors, we track the activity of different signaling pathway components over time and associate these with downstream responses in the same cell. Properties of single cells are quantified with computer vision and bioimage informatics tools, and used to parameterize rule-based models of biochemical networks that govern single cell responses. These quantitative relationships that link ‘input’ and ‘output’ in the same cell often reveal non-intuitive mechanisms of signal transduction that regulate cell fate decisions in response to stimuli or drugs.



Transcriptional diversity through competition on promoters:

When bound to promoters or enhancers, DNA-binding proteins can either activate or repress transcription of nearby genes. During inflammation or in response to a drug, activator and repressor proteins can compete to occupy the same DNA region and their interplay determines whether the associated gene is transcribed. While competition for DNA promoters is generally thought to limit transcription, our work with NF-kB family transcription factors has led to the tantalizing suggestion that competition can diversify transcriptional responses to cues in the cellular milieu. Depending on the relative abundance of activator and repressor proteins in a cell, and their respective affinity for a promoter DNA sequence, distinct classes of transcription emerge – in the same cell, 100’s of NF-kB regulated genes can be transcribed uniquely even though their transcription is regulated by the same proteins. One of our goals is to understand how the expression of system-specific competitors ‘fine-tune’ transcription, and how variability in the abundance of activator and repressor proteins between single cells affect their response. By integrating experimental and computational techniques, we aim to quantify and model competition-dependent transcription with single-cell resolution.