Yaakov Benenson

Yaakov (Kobi) Benenson

We design molecular systems to process biological information in new ways. We are inspired by core biological mechanisms, such as genome replication, gene expression and signal transduction, which manipulate sequence and concentration information stored in molecules. We call our artificial systems “molecular automata”, after electro-mechanical automata (computers and robots), which process information encoded in electric pulses. Molecular automata are complex, artificially constructed networks of interacting biomolecules, immersed in a native biological environment. They gather information encoded in cellular molecular components through molecular sensors, subject it to programmable processing by a molecular computing module, and produce a molecular output which can directly affect the cell or be interpreted by an external observer. The potential applications of molecular automata range from complex real-time measurements in single cells, to disease diagnostics and treatment with single-cell resolution, to ‘reprogramming’ cells. We also hope, through this work, to uncover fundamental principles pertaining to information processing by living systems, in much the same way as the development of a steam engine led to the foundation of thermodynamics.

Our research encompasses all aspects of molecular automata:

  • Design and construction of real-time, precise and sensitive molecular sensors for a variety of endogenous signals, including RNA molecules, proteins and metabolites.
  • Construction of molecular computers, integrated with the sensors and able to process environmental information in algorithmic fashion.
  • Design of molecular “actuators”, to produce a desired change in the biological environment.
  • Exploring ways to ensure the robust operation of the automaton in a complex and changing biological environment. As a first step we will design feedback mechanisms to allow adaptation. In the long term we will also investigate possibilities to provide the automata with a rudimentary “learning” capability.

We employ two complementary strategies. First, we are exploring the limits of functional complexity in engineered molecular automata. This strategy, involving experimental tests performed in well controlled conditions, is designed to produce proofs-of-concept rather than real-life applications. Second, we develop methods to integrate molecular automata in real biological environments: in particular, the cytoplasm of mammalian cells, but also of bacteria. Successful integration with the cell may require sacrificing some of the complexity of the prototypes, to deal with the complexity of the biological environment and the multitude of unwanted, hard-to-predict interactions.

Benenson Lab

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