Research Interests

My research can be grouped under two main areas: fundamental science and technological applications. Synthetic biology is at the intersect of classical genetic engineering approaches involved with current state of art technological developments where design, test and construction of biological matter, devices and systems are enabled through. Some of the subtopics I am working on can be seen below:

  • Synthetic genomes
  • Synthetic biology of Pseudomonas putida
  • Biosensors
  • Standardization in Synthetic Biology
  • Spatiotemporal protein distribution

Synthetic Genomes

Coming soon…

Synthetic Biology of Pseudomonas putida

Coming soon…

Biosensors

During my PhD thesis at De Lorenzo Lab, I designed and implemented artificial pathways and organizational circuits in the bacterial genome (specifically Pseudomonas putida) in order to provide desired functionalities, e.g. implementation of NOR logic gates. The emphasis of the work was formalizing and standardizing such design procedures through a close analogy with widely known digital circuit design paradigms borrowed from electrical and computer engineering.

The soil bacterium Pseudomonas putida is evolutionary endowed with unique characteristics towards environmental stresses (such as high resistance to solvents and the ability to execute harsh biochemical reactions). It thus offers a great chance to exploit its distinctive traits missing in other microbial cell platforms for many environmental and industrial applications. Since P. putida has become a model chassis in microbial biotechnology, it has been comprehensively investigated from different aspects. However, there is one field that has not been explored yet: utilizing P. putida cells as programmable robotic agents. 

In order to accomplish it, there is a need for domesticating P. putida in the direction of benefiting it as biosensor. Hence, there are things that are primarily important to actualize towards this purpose. One of the first actions to take is to develop quantification methods in P. putida, along with using computer power to enhance interface between experimental approach with in silico world. 

The standardization of biological matter has been a point of concern for a while in scientific community. This is important in order to succeed well-defined bacterial bugs and robust experiments. Towards this aim, I am exploring the possible circuits that will help to design bacterial genetic information in a fashion where we are capable of doing good estimations regarding expression of gene of interests, and this way to have reliable bacterial production lines.

This concept is borrowing a lot from electronic engineering, as the final aim is to create biological circuits with using logic gates. Approaching cells as electronic circuits gives us the opportunity to implement engineering discipline and apply digital to analog information flow inside the cell within the boundaries of cellular mechanisms. Instead of current flow, we have information flowing through RNA polymerases. In the future, this system may help us to build programmable robot bacteria, ro-bacs. With this, the system has the potential to make breakthroughs for applications in environmental and industrial implications, therapeutics, diagnostics, biosensors and several others.

Standardization in Synthetic Biology

Standards are important for everyday life. As well, they have the same impact in science. Most of the time we are using the standards without thinking, however what if we did not have the meter? The life has been more comfortable with the introduction of metric system by French scientists in the late 18th century. And from there on we have a standard unit for our length calculations.

With the same approach, I am trying to develop a system that will allow biological sciences, especially synthetic biology field, to have standards in gene expression. This may have an impact on the acceleration of having comparable results and in a standard way. 

Through this purpose, I am working on quantification of RNA Polymerase (RNAP). Following modifications I made on the RNAP, now it is ready for applying immunoprecipitation techniques, by which I can quantify the exact number of RNAP that is affiliated to a given DNA structure. In my case, I have a robust, orthogonal synthetic promoter that has unique characteristics of being a transcribable in an equally constitutive manner under different growth media. 

My aim is to characterize the polymerase per second (PoPS) that is affiliated to the promoter of interest, and then using this reference promoter to build a reference book under different physical conditions. This way, it may be possible to compare the gene expression level of an experimental setup with the reference promoter’s activity without needing any changes in the host organism, by simply growing the reference strain in the same conditions with the host strain, and report the unit of gene expression in a standard way. 

Spatial Distribution of Sigma Factors

Sigma factors are RNA-Polymerase (RNAP) binding transcription initiation factors in bacteria that discriminate between different types of promoters in order to switch between global gene expression paradigms. In order to understand fully how the σ-factors differentially regulate gene expression, we must first measure their spatial distribution. As a model system we study the sigma factors RpoS (σS) and RpoD (σ70) in E. coli. σ70 is the housekeeping sigma factor, recognizing promoters for those genes expressed in steady-state exponential growth, whereas σS is the sigma factor responsible for recognizing those genes expressed in response to stress, such as starvation in stationary phase. 

To measure the spatial distribution of these sigma factors, we have translationally fused the native chromosomal copies of σS and σ70 to fluorescent reporters. Our preliminary data indicate that the diffusion of sigma factors is highly dependent upon growth state, and differing conditions show distinct spatial distributions. To obtain a good understanding of the spatial distribution of the σ−factors in our model organism, we target a variety of growth conditions that will allow us to see the full scope of possible spatial distributions.