LogInIt's just going to take you around 600 years.
There is one fundamental truth in the game industry: you can run Doom on anything if you have enough creativity and willpower. This has once again been proven by Lauren Ramlan, a PhD student in the Biological Engineering department, who managed to get it working on living cells. Well, kind of.
Ramlan was inspired by the E. coli digital display and decided to use it for this unusual task. For this, cells must be able to function as pixels:
"In a simple 1-bit black-and-white display, a pixel is either on or off, represented as 1 and 0 respectively. I propose a system whereby cells are contained within a 32x48 well plate connected to a display controller that processes the graphics output of Doom and translates the binary arrays into the addition or omission of a repressor controlling the fluorescence of the cells in that well. This simple system requires only a single genetic part: a repressor-operator pair controlling the expression of a fluorescent protein such as GFP."
Image credit: Lauren Ramlan, id Software
Ramlan took the first frames of Doom, compressed them into 32x48 grayscale arrays of average neighboring pixel values, and then "performed thresholding on the images to determine which pixels should be considered “on” and have AHL added, and which pixels are off." This thresholded binary array was then passed into the simulation code.
As it's a 1-bit display, don't expect to see much. However, this is not the main issue in this impressive experiment. Ramlan says that cells reach the peak display output in approximately 70 minutes and then return to their starting state in 8 hours and 20 minutes. Doom's framerate is capped at 35 frames per second, and the average playthrough time is about 5 hours, so it would take 599 years to run Doom on cells.
Of course, this result could be improved, and Ramlan suggests adding more experimentally derived values for the parameters, incorporating a quenching mechanism to speed up the rate of turning back off, and introducing a memory and prediction system.
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