Systems Biology

Cells have internal circuits that control their behavior. These circuits can be studied and redesigned in a manner analogous to electrical circuits. However, since the signals are chemical they are inevitably stochastic, which causes differences of gne expression –and therefore phenotype - even within genetically identical populations. In our group, we study the origin and consequences of this variability and, in particular, how to combat microbial strategies bases on this heterogeneity and how to design robust genetic circuits. Additionally, we are working on some practical applications of the ability to design artificial genetic circuits, on the fitness value and energy cost of each bit of information a cell obtains about the environment, and on the experimental confirmation, in bacteria and humans, of theoretical results on the conditions for evolutionary stability of altruism.

Easurements of gene expression with direct electrical output using Shewanella oneidensis MR-1

Shewanella oneidensis MR-1 is a bacterium with a broad respiratory capacity that allows it to reduce various electron acceptors, from oxygen to metal oxides and electrodes. This has diverse technological applications such as Microbial Fuel Cells, where electricity is generated using bacteria as a catalyst. In this project, we wish to take advantage of the electrical current generation capacity of Shewanella to create a genetic expression measurement system with electrical output that does not need fluorescence and gives fast and simple readings. For that purpose, we will modify Shewanella oneidensis MR-1 in a way that it regulates the amount of electrons given to an electrode (electric current) depending on the intensity with which the gene of interest is expressed.

Juliana Sandoval

Measurement of size and gene expression of evolved E. coli cells along the growth curve using MACS

MACS (Microfluidics-assisted cell screening) is a recently developed microfluidic platform that allows to take mesurements of some properties of E. coli cells. In this work we want to measure some properties of E. coli cells along the growth curve using MACS. These cells have been evolved with minimal medium with different concentrations of carbon and nitrogen sources and samples have been taken at different stages of the evolution process. I will measure the expression levels and the sizes of all these samples with MACS and compare the distributions of the different variables both between different stages of the evolution process, and between different growth conditions.

Study of the persistence generation mechanisms in bacteria

Persistence is a transient phenotype in which results in tolerance to multiple bactericide substances, separately from the usual resistance obtained through mutations. Though entrance and exit from this state is stochastic, the rates are dependent on environmental conditions, in particular stress. In a study by Balaban and coworkers in 2004, two types of persistent cells were identified, Type I and Type II. So far antibiotics have been used to isolate persistent cells, but the use of antibiotics does not differentiate between the two types of persistent cells and a stress response is induced so that the proportion of persistent cells is changes. In this project, we worked on the design of a protocol that allows for the isolation and differentiation of the types of persistent cells, without inducing persistence through stress. We then used this protocol to perform transcriptomic studies on persistors, and isolated the mutation responsible for a high type II persistence mutant (DS1).

Silvia Cañas

Stochastic modeling of genetic expression of growing cells

Genetic expression is a naturally noisy process. The study of the origin of these variations is fundamental for the understanding of genetic circuits. Cell size, as well as the speed at which a cell grows, are important factors in this noise. In this project, we are interested in including the size and growth rate so that a correlation between these factors and noise can be found. We will use Monte Carlo simulations using the Gillespie algorithm to obtain experimental parameters for confirmation of these effects.

David Camilo Durán Chaparro

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