Backgorund & Motivation

The main intent of biological research is to further a deeper understanding of living systems. After we learned about genetics and biological macromolecules, the next step is to develop a method useful to control all these elements and to combine them in order to create new artificial behaviours. This is the precise objective of synthetic biology: the development of reliable, stable and robust genetic circuits to engineer microorganisms to carry out a function in an established way or to obtain systems that can process useful information.

Relying on mathematical in silico modeling is one of the main priorities if a predictable proceeding is to be obtained. Since we are dealing with the behavior of a complex system, it is fundamental to exploit the potentiality of modeling by describing the behavior of simple modules first and then undertaking the prediction of more complex circuits, in a bottom-up fashion.

Computational sciences allow to disentangle complex cellular networks behavior, especially in a contest of intricate cell-cell interactions. In electronic engineering, circuits consist of several layered semiconductors while in biology homeostasis is obtained through regulatory networks consisting in interactions taking place between macromolecules linking signals from environment and cells. In this way logic gates have been designed, realized and tested in biological systems. One of the simplest device that has been implemented is an AND gate that can associate two input signals and control various behaviors (Anderson JC et al. 2007). Also other types of devices have been created, like OR, NOT and NOR gates. In addition to these simple logic gates, multiple layered gates have been produced (Tamsir A et al. 2010).

The real problem in synthetic biology is the capability to control the resulting state of the system. Control systems elements like oscillators and toggle switches have also been implemented. Oscillators or Synchronized clocks in particular, are of great relevance for the coordination of rhythmic processes among individual elements in a larger system (Danino T et al. 2010). A toggle switch system has been constructed as a bistable gene system which can switch between its two states trough a chemical induction; this circuit has been designed on the predictions of a mathematical model (Atkinson MR et al. 2003).

Other interesting elements to develop and implement in biological systems are those found in Control theory. This branch of engineering deals with the study of dynamical systems; for example, in a closed-loop control system, a sensor monitors the output of the system and feeds the data to an element called a “controller” which properly manipulates the error signal to preserve the desired output of the system. Negative feedback occurs when the output acts to oppose changes to the input of the system, with the result that the changes are attenuated and limited. If the overall feedback of the system is negative (in terms of its transfer function), then the system will tend to be stable, reaching a desired set-point in the value of the controlled variable.

Schematic description of a closed loop circuit

Obtaining a negative feedback means processing information about the system; quorum sensing is a well known mechanism that can be exploited to send and receive signals by means of so called autoinducer (AI) molecules. In nature it is used by prokaryota for synchronizing the activities of large groups of cells. This molecular machinery allows bacteria to inspect the environment for other bacteria and to alter the behavior of the entire population mainly in response to changes in cell density. Cells are able to react to a minimal threshold concentration of the inducer molecules and alter their gene expression in response. Using this signal-response system, bacteria can synchronize particular actions on a population-wide range and thus exhibit behavior peculiar of multi-cellular organisms (Waters C.M. et al. 2005).

One of the best studied quorum sensing systems is the control of luminescence in Vibrio fischeri (Schaefer AL et al. 1996). The two genes involved are luxI and luxR. The proteins, LuxI and LuxR, control expression of the luciferase operon (luxICDABE) required for light production.

In particular the first gene directs the synthesis of N-acylhomoserine lactone (HSL) which is the key molecule of the system and diffuses in and out of the cell membrane and increases in concentration with increasing cell density; the second codes for a protein with two functional domains, a cytoplasmic autoinducer receptor and a DNA-binding transcriptional activator (Engebrecht J et al. 1983).

Simplified view of Vibrio fischeri quorum sensing system

In nature some of these quorum sensing systems are used to trigger pathogenicity of some bacteria strains, such as Pseudomonas putida; the target organisms sometimes develop a defense system, based on the degradation of AI molecules (Molina L et al. 2003). Thus some bacteria and plants are able to produce lactonolysing enzymes, such as AiiA (from Bacillus subtilis sp240B1), which can carry out the degradation of HSL. This lytic enzyme hydrolyzes the AHL (a class of compounds which HSL is part of) of the ubiquitous plant pathogen bacterium Erwinia carotovora, preventing it from infecting plants (Dong Y et al. 1983). Microbial cell to cell communication is widespread in the microscopic world and understanding this process is of great relevance to all of microbiology in particular for industrial and clinical applications (Bassler BL et al. 1983).

The relevance of these systems plays a role in the development of devices to control interbacterial signaling mechanisms (Fuqua C et al. 1983). Considering the significance of control devices in this discipline we decided to experience a proof of concept of the reach of building complex genetic circuits with a behavior, predictable on the basis of the data that we already have on the single BioBrick parts. To do so we plan to realize a circuit in E. coli which implements a closed loop control function exploiting quorum sensing mechanism, with a model-based approach.


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