Modeling Heterogeneity in Apoptotic Decision-Making

Apoptosis, a form of programmed cell death, is a fundamental process that allows the body to remove damaged or unnecessary cells in a controlled manner. A key step in this process is mitochondrial outer membrane permeabilization (MOMP), which acts as a point of no return: once triggered, the cell is committed to die. This decision is tightly regulated by the Bcl-2 protein family, which includes pro- and anti-apoptotic factors such as Mcl-1 - and is influenced by the cell’s internal state, including its position in the cell cycle.

Understanding how cells make this life-or-death decision is crucial, particularly in diseases like cancer, where apoptosis is often dysregulated. Cells within the same population can respond very differently to the same stimulus, and this variability is thought to arise from differences in protein levels and cellular states. Uncovering the origins and consequences of this heterogeneity is essential for improving therapeutic strategies that aim to selectively induce cell death in diseased cells.

Within the Bcl-2 protein family, the anti-apoptotic protein Mcl-1 plays a particularly dynamic role. Its expression levels and intracellular distribution are tightly regulated and change throughout the cell cycle. This cell cycle-dependent regulation makes Mcl-1 a key contributor to cell-to-cell variability in apoptotic sensitivity, linking progression through the cell cycle to differences in how cells respond to death signals.

In this project, we combine experimental data with mathematical modeling to investigate how variability in protein expression shapes apoptotic responses. A particular focus lies on solving inverse problems: using observed cellular outcomes to infer the underlying distributions of key regulatory proteins prior to stimulation. By integrating single-cell measurements with mechanistic models of apoptosis and cell cycle progression, the project aims to quantitatively link molecular variability to functional outcomes. Ultimately, this approach seeks to provide a deeper, predictive understanding of how cells regulate apoptosis, and how heterogeneity between cells - arising from multiple layers of variability - shapes these decisions.