Mapping Uncertainty

Can gene regulatory networks help scientists predict cell behavior and improve therapeutics for cancer and other diseases?

By Bernard Cook III

It’s one of the big mysteries of biology. Why do genetically identical cells often differ in the way they move, which proteins they produce and how they respond to their surroundings? Casim Sarkar, a professor in Biomedical Engineering and a member of the BioTechnology Institute, is working to solve this enigma by studying gene regulatory networks — the interaction between genes and the proteins they produce.  

Sarkar likens these gene regulatory networks to computer code that governs which genes are turned on, what proteins they produce and in what quantity. As it turns out, a cell’s behavior (for example, how it moves) and the decisions it makes (whether to move) are largely determined by gene regulatory networks and how they interact with the cell’s environment. 

To begin unraveling the link between gene regulatory networks and cell behavior, Sarkar’s lab borrowed the concept of the “epigenetic landscape” from developmental biologists. Initially, the epigenetic landscape was developed as a visual metaphor to demonstrate how a stem cell (a cell that has not yet developed a specific function) becomes one of many cells with a defined function, like a neuron or a muscle cell. According to Sarkar, “The landscape looks like a ski slope where you can take different paths all starting from the same point.” 

Like a ski slope, the epigenetic landscape has defined features like height, steepness and surrounding hills and valleys, which collectively inform what a cell — in this case, the skier — will do next. Picture placing a marble on a Pringle: in some directions, it may move downward and in others it can only go up. Let go, and the marble will most likely move downward. Like this, a cell on or near a crest is likely to move downhill and perform actions that are more likely, while a cell in a valley is met with resistance and likely won’t change its behavior at all (unless, like a skier, it has accumulated sufficient prior momentum to move upwards).

Instead of simply using the landscape to conceptualize cell behavior, Sarkar’s lab is developing a computer-based epigenetic landscape to predict what a cell in a given state might do next — and the likelihood of that outcome. This approach is particularly useful because it allows him to incorporate elements like cell-to-cell variability and DNA modifications, two features of gene regulatory networks that may push two otherwise identical cells to do different things in the same circumstances. In his model, these elements play a role in determining the shape of the landscape, which allows Sarkar and his team to make predictions about cellular behavior by accounting for these confounding elements. Importantly, this approach also allows him to pinpoint facets of the gene regulatory network that drive two identical cells to behave differently. 

Understanding why identical cells take different paths may help improve therapies for cancer and other conditions. In cancer, for example, tumors consist primarily of cells that proliferate rapidly. But some cancers also contain dormant cells that evade standard chemotherapy and often cause relapse. Sarkar’s approach could identify aspects of the gene regulatory network that push some cells to choose dormancy, which in turn, may help researchers keep these cells dormant or identify an intervention that awakens dormant cells and renders them vulnerable to standard chemotherapy. 

Sarkar’s long-term goal is to make concrete predictions about what a cell might do in specific environmental conditions as a result of the underlying gene regulatory network. Solving this mystery could help engineers working with stem cells create tissues with the desired structure and function and improve therapeutic strategies to combat antibiotic resistance.