Francesca Tomasi received her B.A. from the University of Chicago and is now a microbiologist.
A recent challenge in biology has been the depiction of cellular functions as algorithms, discrete sets of rules that reach a desired outcome. The idea comes from the notion that cells are intricate networks of feedback and signaling systems that work together to regulate physiological processes. These systems act on Boolean principles: conditions necessary to activate or inactivate different pathways are either “true” or “false.” Basically, cells are computers. The overarching goal of these computers is homeostasis, a stable balance of physiological processes that maintain a healthy organism.
A classic example is the lac operon, a series of genes that regulate the metabolism of lactose (a sugar) in E. coli. A skeleton of the algorithm for activating the lac operon would look like this:
IF glucose AND lactose are present in the environment,
THEN activate genes for glucose metabolism
ELSE IF only lactose is present in the environment,
THEN activate the lac operon to metabolize lactose
Glucose is the preferred sugar (carbon source) for most bacteria; when surrounded by different carbon sources, E. coli will always go through all the glucose it can get before switching on genes to digest other carbon sources like lactose. Energetically-speaking, it would be a waste to produce lactose-digesting enzymes if there is no lactose present and if there is a more preferable source of energy available (glucose). Thus, the lac operon works as a two-part control mechanism to ensure it is only working when it needs to: in the absence of lactose, a lac “repressor” halts the production of enzymes encoded by the lac operon. In the presence of glucose, another protein required for the production of these enzymes also remains inactive. When lactose is present AND no glucose is present, these enzymes and proteins are activated and the bacterial cell starts to catabolize lactose. How would you write the above algorithm more specifically so that a computer could carry it out?
Being able to consolidate a cell’s entire physiology into a series of on and off switches would be a holy grail in computational biology: it would mean a full understanding of how a cell works in its environment.
What about exploiting the “cells are computers” concept for genetic engineering? After all, harnessing a cell’s computational operations to alter a cell’s response in specific environment has many biomedical and technical implications. For instance, designing bacteria that can release anti-tumor drugs when exposed to a tumor could be used as a targeted cancer treatment. Synthetic genetic circuits could be interwoven with natural ones to achieve such goals.
Researchers from MIT, Boston University, and the National Institute of Standards and Technology have taken the computer analogy a step further to achieve just that. They recently designed a program called Cello (http://www.cellocad.org/) using an existing language called Verilog that does something similar for electronic circuits. Cello enables users to describe the function of a desired “genetic circuit” – for example, detect and respond to certain environmental conditions. The program then translates the circuit into a DNA sequence by organizing “logic gates” into networks. Synthesize the proposed sequence, clone the DNA onto a bacterial plasmid, stick the plasmid into cells, and theoretically you should eventually have E. coli colonies expressing your desired circuit. In order to test Cello, the researchers built 60 circuits. Of these, 45 worked. 92% of the 412 programmed outputs from these circuits functioned as predicted. Right now, Cello can be used to build circuits capable of detecting up to 3 inputs and responding differently according to different combinations and levels of inputs.
Cells are computers. DNA encodes these computers’ circuitries. Circuits process sensory information and use it to control biological functions. Now, researchers have made it possible for you to use your own digital computer to edit the organic one of a microbe.