Yaakov Benenson, Professor of Synthetic Biology at D-BSSE, has spent a large part of his career developing biological computers that operate in living cells. His long-term goal is to construct biocomputers that detect molecules carrying important information about cell wellbeing and process this information to direct appropriate therapeutic response if the cell is found to be abnormal. Now, together with MIT professor Ron Weiss and a team of scientists including postdoctoral scholars Zhen Xie and Liliana Wroblewska, and a doctoral student Laura Prochazka, they made a major step towards reaching this goal. In a study that has just been published in "Science", they describe a multi-gene synthetic “circuit” whose task is to distinguish between cancer and healthy cells and subsequently target cancer cells for destruction. This circuit works by sampling and integrating five intracellular cancer-specific molecular factors and their concentration. The circuit makes a positive identification only when all factors are present in the cell, resulting in a highly-precise process. Researchers hope that it can serve a basis for very specific anti-cancer treatments.
The scientists tested the gene network in cultured human cells: cervical cancer cells, called HeLa cells, and normal cells. When the genetic biocomputer was introduced into the different cell types, only HeLa cells, but not the healthy ones, were destroyed.

Extensive groundwork was required to achieve this result. Benenson and his team had to first find out which combinations of molecules are unique to HeLa cells. They looked among the molecules that belong to the class of compounds known as microRNA (miRNA). The researchers had identified one miRNA combination, or profile, that was typical of a HeLa cell.

This was a challenging task. In the body there are about 250 different healthy cell types. In addition, there are numerous variants of cancer cells, of which hundreds can be grown in the laboratory.  The diversity of miRNA is ever greater: between 500 to 1000 different species have been described in human cells. "Each cell type, healthy or diseased, has different miRNA molecules switched on or off," says Benenson.
Creating a miRNA “profile” is not unlike finding a set of symptoms to reliably diagnose a disease: "One symptom alone, such as fever, can never characterize a disease. The more information is available to a doctor, the more reliable becomes his diagnosis”, explains the professor, who came to ETH from Harvard University a year and a half ago. The researchers have therefore sought after several factors that reliably distinguish HeLa cancer cells from all other healthy cells. It turned out that a combination of only five specific miRNAs, some present at high levels and some present al very low levels, is enough to identify a HeLa cell among all healthy cells.
"The miRNA factors are subjected to Boolean calculations. They are combined using logic operations such as AND and NOT, and the network only generates the required outcome, namely cell death, when the entire calculation with all the factors results in a logical TRUE value”, says Benenson.

The researchers were able to demonstrate that the network works very reliably in living cells. It correctly combines all the intracellular factors using a prescribed molecular “program” and gives the right diagnosis. This, according to Benenson, represents a significant achievement in the field.
In a next step, he wants to test this cellular computation in an appropriate animal model, with the aim to build diagnostic and therapeutic tools in the future. This may sound like science fiction, but Benenson believes that this is feasible. However, difficult problems , for example the delivery of foreign genes into a cell efficiently and safely, still remain to be solved. In particular this approach requires temporary rather than permanent introduction of foreign DNA into the cells, but the currently available methods, both viral and chemical, are not fully developed and need to be improved.
"We are still very far from a fully functional treatment method for humans. This work, however, is an important first step that demonstrates feasibility of such a selective diagnostic method at a single cell level,” said Benenson.
This story is reprinted from material from ETH Zurich, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier.