The role of the cellular economics in the expression of exogenous genes: Towards modularity in synthetic circuit design.

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One of the goals of synthetic biology is the design and construction of biological systems capable of performing logic computations, that is, to integrate environmental signals to produce desired outputs. This is a very important step towards further developments of the field, such as the construction of synthetic tissues and organs. To achieve that goal, it is required that engineering principles could be applied into biological systems. This proposal focuses in the principle of modularity as a rule of design in biology. Modularity will allow fast and reliable building of complex synthetic genetic circuits. Large circuits could be easily implemented as the sum of well-known modules that keep the same properties that they exhibit when they are isolated, as it happens in electronics. That is a big challenge for synthetic biologists as the modules are rarely independent from each other. This is mainly because all of them compete for the same pool of cellular resources for the expression of their constituent genes.As a consequence in the limitations of the availability of resources, the cell can be envisioned as a molecular economical market where many genes compete simultaneously for the same transcriptional and translational machineries. These machineries catalyse, respectively, the synthesis of the RNA and the synthesis of the proteins that ultimately perform functions in the cell. The resources available for gene expression are not infinite and, therefore, depending on how the cellular resources are invested, some genes will benefit from larger fractions of the pool while some others will only have access to very small amounts. This asymmetric distribution or resources is consequently reflected in the different relative abundances of the proteins encoded by genes being expressed at the same time. This uneven distribution affects particularly the performance of artificial genetic circuits and, ultimately, compromise the notion of modularity.In a comparison with computer science, the synthetic circuits would be the software of the system - a set of scripts designed to perform a function -, whereas the cell machinery would be the hardware needed to accomplish the instructions coded in the exogenous DNA. In this project we aim to identify modifications of the hardware increasing the processing capabilities of the cell so that more complex programs can be run using to that end the model system Escherichia coli. We will analyse in a test circuit the effect of modifications in genes involved in the synthesis and function of the RNA polymerase and ribosomes. We will use for this task a circuit that I developed and applied previously to determine the extent of competition in gene expression using fluorescent reporters. Once the relevant genes for resource allocation have been identified, we will develop strains optimized for circuit implementation. These newly evolved strains will be used to implement complex computations based on genetic circuits encoding transcriptional cascades, oscillators and multi-layered logic gates. To understand the behaviour of these complex systems we will generate mathematical models describing each of the circuits operating in conditions where the cell resources are limited. The models will be used to make predictions about circuit performance, and these predictions will be validated experimentally.With the results of these investigations we aim to bypass the problem of poor circuit performance due to sharing of resources in the cell, a phenomenon that substantially limits the complexity of the circuits that can be built. In addition, we will test the limits of modular design in bacteria. Achieving modular construction of genetic networks will have a great impact in the way that we can design complex processes for making decisions in living organisms. It will help to transform synthetic biology into a real engineering discipline, paving the path for multiple biotechnological applications.