David Goldfarb is a serial entrepreneur, consultant, and software engineer with a Bachelor's degree from MIT.
He's invested the past quarter-century working on the software technologies that are making this generation so exciting. From internet and window systems in the early eighties through community applications today, he has had the pleasure of riding nearly every wave of the emerging tech economy.
He specializes in converting raw ideas into prototypes and products, helping his clients and startups to navigate the "0 to 60" stages of development and product release, with hands-on technical management in the areas of software architecture, social communities, embedded language design, mobile apps, prototyping, and AI.
Along the way, David thoroughly neglected biology. What little he knew came from a junior high school class, years ago. This year, preparing for the biotech century we are entering, he has focused on rectifying this error by studying biotech and genomics. He wants to use his CS background and NKS tools to identify patterns in genomic evolution and hopes to prove that enthusiasm can compensate for technical naivety.
DNA as a Computer
DNA in each living cell guides the creation of protein. These proteins control all elements of the cell's life and reproduction. DNA is not a simple template; it is more like a parallel computer program, with subroutines turned on or off dynamically.
One mechanism that modulates this process is chemical "markings" on the DNA structure, sometimes called the histone code.
Protein molecules can read and write segments of this code. A recent paper shows that these actions are Turing-equivalent. These proteins can solve any computable problem. Also, the raw computational power of each cell is comparable to that of a small computer.
In this project, I simulate these proteins. I use the simulation to solve a well-known computer science problem called Hamiltonian Path. In addition, I use a learning algorithm to show that simple evolutionary mechanisms lead to dramatic increases in the cell's problem-solving efficiency.
This demonstration will discuss the structure of this biological computer and will graphically show emergent behavior that appears as the cell evolves.