From 2012.igem.org

Latest revision as of 03:52, 27 October 2012

The Problem

Viewed as a minor inconvenience in the developed world, diarrhea can be a death sentence. Diarrhea can be life threatening as it causes severe dehydration as a result of extensive fluid loss. An estimated 2.0 billion cases of diarrhea occur each year amongst children under five years of age. Of these cases, 1.5 million children die. The major pathogens that most frequently cause acute childhood diarrhea cases are bacterial pathogens such as E. coli, Shigella, Campylobacter and Salmonella. Currently, existing biosensors for water-borne pathogens are either costly, inaccessible to developing countries, difficult to use without training, and not very reliable. For example, immunoassays, which uses antibodies specific for certain antigens on pathogenic diarrhea, have a good turnaround time. However, not all antigens have available antibodies that can be used for detection, and those antibodies that are available can be very costly. As such, the ASU iGEM team plans to develop an inexpensive way for communities to test the purity of water sources. To this end, we aimed to identify the specific pathogens in the water source in efforts of reducing the incidence of childhood diarrhea and ultimately decreasing mortality rates.

Figure 1: Epidemiology map indicating disability-adjusted life year for diarrhea per 100,000 inhabitants (2004). Image source: Wikimedia Commons.

Quantitative considerations

What concentration of pathogens causes sickness?

A coliform count is defined as a test of water contamination in which the number of the colonies of coliform-bacteria Escherichia coli (E.coli) per 100 milliliter of water is present in a sample (The Law Dictionary). The result is expressed as “Coliform Microbial Density” and indicates the amount of fecal matter (The Law Dictionary). The Environmental Protection Agency has standards that define non-potable water as a sample that contains one bacterium per 100 mL.

What specific design approaches did we take to try to reduce false positives, while making the biosensor effective?

Our design uses split beta-galactosidase to create a blue signal in the presence of a specific pathogen. We incorporated a mutational version of split beta-galactosidase that reforms into a functional enzyme unit only when the halves are brought into close proximity. This mutational version of split beta-galactosidase effectively reduces false positive created by chance interactions in the wild-type split beta-galactosidase.

Why are we doing this?

What do we hope to accomplish/want to figure out?

For this project we are hoping to make our biosensor as user friendly and most cost effective product as possible. In the process in making this design we also wanted to address the results from using the biosensor. We wanted our project to give results in real time and be able to determine the phenotype of the potential pathogen.

Who are we doing this for? What do we care about?

This project is primarily based on its applications. We wanted to develop a low-cost biosensor that could be implemented in developing countries. This is because pathogenic bacteria causes diarrhea which is one of the world's leading global health issues. By designing our biosensor it would be a potential solution to this problem.

What is our ultimate goal?

Our ultimate goal for the project is to be able to see our biosensor used in developing nations. Ideally our biosensor would be able to be used as an early detection device in order to prevent future pathogenic outbreaks. This would be a solution to not only maintaining a healthy water supply but also to also improve the overall sanitation of developing nations.