Stanford creates biological transistors, the final step towards computers inside living cells

Πηγή: ExtremeTech
By Sebastian Anthony
March 29 2013

Bioengineers at Stanford University have created the first biological transistor made from genetic materials: DNA and RNA. Dubbed the “transcriptor,” this biological transistor is the final component required to build biological computers that operate inside living cells. We are now tantalizingly close to biological computers that can detect changes in a cell’s environment, store a record of that change in memory made of DNA, and then trigger some kind of response — say, commanding a cell to stop producing insulin, or to self-destruct if cancer is detected.

Stanford’s transcriptor is essentially the biological analog of the digital transistor. Where transistors control the flow of electricity, transcriptors control the flow of RNA polymerase as it travels along a strand of DNA. The transcriptors do this by using special combinations of enzymes (integrases) that control the RNA’s movement along the strand of DNA. “The choice of enzymes is important,” says Jerome Bonnet, who worked on the project. “We have been careful to select enzymes that function in bacteria, fungi, plants and animals, so that bio-computers can be engineered within a variety of organisms.”

Like a transistor, which enables a small current to turn on a larger one, one of the key functions of transcriptors is signal amplification. A tiny change in the enzyme’s activity (the transcriptor’s gate) can cause a very large change in the two connected genes (the channel). By combining multiple transcriptors, the Stanford researchers have created a full suite of Boolean Integrase Logic (BIL) gates — the biological equivalent of AND, NAND, OR, XOR, NOR, and XNOR logic gates. With these BIL gates (pun possibly intended), a biological computer could perform almost computation inside a living cell.

You need more than just BIL gates to make a computer, though. You also need somewhere to store data (memory, RAM), and some way to connect all of the transcriptors and memory together (a bus). Fortunately, as we’ve covered a few times before, numerous research groups have successfully stored data in DNA — and Stanford has already developed an ingenious method of using the M13 virus to transmit strands of DNA between cells. (See:Harvard cracks DNA storage, crams 700 terabytes of data into a single gram.) In short, all of the building blocks of a biological computer are now in place.

This isn’t to say that highly functional biological computers will arrive in short order, but we should certainly begin to see simple biological sensors that measure and record changes in a cell’s environment. Stanford has contributed the BIL gate design to the public domain, which should allow other research institutes, such as Harvard’s Wyss Institute, to also begin work on the first biological computer. 

Moving forward, though, the potential for real biological computers is immense. We are essentially talking about fully-functional computers that can sense their surroundings, and then manipulate their host cells into doing just about anything. Biological computers might be used as an early-warning system for disease, or simply as a diagnostic tool (has the patient consumed excess amounts of sugar, even after the doctor told them not to?) Biological computers could tell their host cells to stop producing insulin, to pump out more adrenaline, to reproduce some healthy cells to combat disease, or to stop reproducing if cancer is detected. Biological computers will probably obviate the use of many pharmaceutical drugs.

Stanford University researchers create first virtual organism

Πηγή: TGDaily
By Trent Nouveau
July 21 2012

A team of Stanford University researchers has managed to create the world's first complete computer model of an organism.



The team - led by Prof. Markus Covert - leveraged data from more than 900 scientific papers to account for every molecular interaction that takes place in the life cycle of Mycoplasma genitalium, the world's smallest free-living bacterium.

Not only does the model allow researchers to address questions that aren't practical to examine otherwise, it represents a major stepping-stone toward the use of computer-aided design in bioengineering and medicine.

As Covert notes, recent developments in the field of biology has been marked by the rise of high-throughput studies producing staggering troves of cellular information. As such, a lack of experimental data is no longer the primary limiting factor for researchers. Instead, it's how to make sense of what they already know.

However, most biological experiments, still adopt a reductionist approach to this vast array of data - knocking out a single gene and seeing what happens.

"[Yet], many of the issues we're interested in aren't single-gene problems," Covert explained. "They're the complex result of hundreds or thousands of genes interacting."

According to Stanford bioengineering graduate student and co-first author Jayodita Sanghvi, this situation has resulted in a yawning gap between information and understanding that can only be addressed by "bringing all of that data into one place and seeing how it fits together." 

Indeed, integrative computational models clarify data sets whose sheer size would otherwise place them outside human ken.

"You don't really understand how something works until you can reproduce it yourself," said Sanghvi.

Mycoplasma genitalium is a humble parasitic bacterium known mainly for arriving uninvited in human urogenital and respiratory tracts. But the pathogen also has the distinction of containing the smallest genome of any free-living organism – only 525 genes, as opposed to the 4,288 of E. coli, a more traditional laboratory bacterium.

Despite the difficulty of working with this sexually transmitted parasite, the minimalism of its genome has made it the focus of several recent bioengineering efforts. Notably, these include the J. Craig Venter Institute's 2008 synthesis of the first artificial chromosome.

"The goal hasn't only been to understand M. genitalium better," said co-first author and Stanford biophysics graduate student Jonathan Karr. "It's to understand biology generally."

Even at this small scale, the quantity of data that the Stanford researchers incorporated into the virtual cell's code was enormous - with the final model making use of more than 1,900 experimentally determined parameters.

To integrate these disparate data points into a unified machine, the researchers modeled individual biological processes as 28 separate "modules," each governed by its own algorithm. These modules then communicated to each other after every time step, making for a unified whole that closely matched M. genitalium's real-world behavior.



As noted above, the purely computational cell opens up procedures that would be difficult to perform in an actual organism, as well as opportunities to reexamine experimental data. A number of these approaches have already been demonstrated, including detailed investigations of DNA-binding protein dynamics and the identification of new gene functions.

The researchers had noticed, for example, that the length of individual stages in the cell cycle varied from cell to cell, while the length of the overall cycle was much more consistent. Consulting the model, the researchers hypothesized that the overall cell cycle's lack of variation was the result of a built-in negative feedback mechanism.

Cells that took longer to begin DNA replication had time to amass a large pool of free nucleotides. The actual replication step, which uses these nucleotides to form new DNA strands, then passed relatively quickly. Cells that went through the initial step quicker, on the other hand, lacked a nucleotide surplus. Replication ended up slowing to the rate of nucleotide production.

While these types of conclusions remain hypotheses until they're confirmed by real-world experiments, they do promise to accelerate the process of scientific inquiry.

"If you use a model to guide your experiments, you're going to discover things faster. We've shown that time and time again," Covert added.