Russian Scientists Refine Repressilator Model to Engineer Therapeutic Cells
Researchers enhanced an artificial gene circuit with a probabilistic algorithm that captures random molecular events, improving the accuracy of predictions for synthetic biology applications.
Scientists at Perm National Research Polytechnic University have upgraded an artificial gene circuit known as the repressilator. With improved accuracy in predicting gene behavior, the model could help engineers design therapeutic cells that release insulin or painkillers inside the body on a set schedule. State news agency TASS reported the development, citing the university’s press service.
The first repressilator was created 25 years ago. It is widely used in synthetic biology, a field in which researchers build biological systems with predefined properties. The model consists of a closed loop of three genes whose combination does not occur naturally. It allows scientists to predict how new genetic systems will behave before physically constructing them. The updated version delivers more precise forecasts because the team incorporated additional factors that influence the repressilator’s function, including the time required for protein production and the number of molecules present in a cell.
According to the university, the researchers have developed a model that reliably describes processes occurring in real cells. That could make it easier to design robust genetic programs. The advance opens new possibilities for medicine, including implantable therapeutic cells. For example, cells could be engineered to produce insulin and release it into the bloodstream in short pulses when needed, rather than continuously. Other cells could rhythmically deliver pain-relief compounds directly to a target organ.
Randomness Is Not a Bug
The team augmented the repressilator with an algorithm that accounts for probabilistic molecular-level events. In living cells, molecules are created and degraded unpredictably. The probabilistic model incorporates these fluctuations, making simulations more closely aligned with real cellular dynamics.
The findings suggest genetic engineers may need to rethink how they design synthetic constructs. Previously, they sought to eliminate cellular “noise,” treating random events as interference. The new research indicates that such randomness is a necessary component of gene function, helping genes synchronize more quickly and operate in sequence, an important feature when developing therapeutic components.
Earlier, we reported that researchers at Sechenov University’s Institute of Regenerative Medicine and telecom company VimpelCom developed an AI-based system to track dystrophic changes in kidney tissue.
