Undergrad Research Project - System Design and Automation for Uncovering Big Mechanisms with Application in Drug Discovery and Testing

Spring 2015

Isaac Manjarres
Natasa Miskov-Zivanov
Project description

Currently, computational software is relied upon heavily to observe the behavior of a biological system to predict the way it will react under certain conditions, or to simply learn more about its general behavior. However, the way that software applications carry out these processes is in a sequential manner, which does not mirror the true parallelism that biological networks abide by. Not only that, but as the networks become more complex, it becomes taxing on the system to perform many calculations, and as a result of this, performance is severely reduced. This need to develop a new type of methodology has brought about the idea of modelling biological networks with hardware components, as it is much more efficient in terms of performance, and it also allows for several processes to be performed in parallel, thus remedying the issues that software programs are facing. The objective of this project is to accurately model a biological network using hardware, such that it can exhibit the appropriate behavior under circumstances where certain stimuli or inhibitors are introduced into the system. The methods that will be used to perfect the hardware implementation of the network is to test it with a series of inputs to the system over a certain number of rounds, in order to randomize the order in which certain rules or behaviors are carried out through Boolean algebra. The testing will be done through the use of a random number generator (RNG) that will determine which rules will be executed, and in which order during each round. We anticipate that once the conditions for testing it have been perfected, the network will accurately model a biological networks that can be subject to random stimuli, as well as the introduction of inhibitors.

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