First Complete Computer Model Of An Organism Created


There has been a significant breakthrough in computational biology, a complete computer model of an organism has been created for the first time.

A research team led by Markus Covert, an assistant professor of bioengineering, “used 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.”

“By encompassing the entirety of an organism in silico, the paper fulfills a longstanding goal for the field. Not only does the model allow researchers to address questions that aren’t practical to examine otherwise, it represents a stepping-stone toward the use of computer-aided design in bioengineering and medicine.”

“This achievement demonstrates a transforming approach to answering questions about fundamental biological processes,” said James M. Anderson, director of the National Institutes of Health Division of Program Coordination, Planning and Strategic Initiatives. “Comprehensive computer models of entire cells have the potential to advance our understanding of cellular function and, ultimately, to inform new approaches for the diagnosis and treatment of disease.”

Over the past two decades Biology has been characterized by the rise of ‘high-throughput’ studies that produce enormous amounts of cellular information. The primary limiting factor for researchers now isn’t a lack of experimental data, it’s how to make use and sense of what they already know.

Most biological experiments that are done still take a reductionist approach to this vast array of data though: “knocking out a single gene and seeing what happens.”

“Many of the issues we’re interested in aren’t single-gene problems,” said Covert. “They’re the complex result of hundreds or thousands of genes interacting.”

This has resulted in a massive gap between information and understanding, that will only be bridged by “bringing all of that data into one place and seeing how it fits together,” according to Stanford bioengineering graduate student and co-first author Jayodita Sanghvi.

‘Integrative computational models’ can clarify data sets whose enormous size would otherwise place them outside human understanding.

“You don’t really understand how something works until you can reproduce it yourself,” Sanghvi said.

20120721-205128.jpg “Mycoplasma genitalium is a humble parasitic bacterium known mainly for showing up 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. The final model made 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.”

“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.”

“In the paper, the model is used to demonstrate a number of these approaches, including detailed investigations of DNA-binding protein dynamics and the identification of new gene functions.”

“The program also allowed the researchers to address aspects of cell behavior that emerge from vast numbers of interacting factors.”

“The researchers had noticed, for instance, 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, had no nucleotide surplus. Replication ended up slowing to the rate of nucleotide production.”

Until they are confirmed by real-world experiments, these kinds of findings remain hypotheses, but they help to accelerate 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,” said Covert.

“CAD — computer-aided design — has revolutionized fields from aeronautics to civil engineering by drastically reducing the trial-and-error involved in design. But our incomplete understanding of even the simplest biological systems has meant that CAD hasn’t yet found a place in bioengineering.”

“Computational models like that of M. genitalium could bring rational design to biology — allowing not only for computer-guided experimental regimes, but also for the wholesale creation of new microorganisms.”

“Once similar models have been devised for more experimentally tractable organisms, Karr envisions bacteria or yeast specifically designed to mass-produce pharmaceuticals.
Bio-CAD could also lead to enticing medical advances — especially in the field of personalized medicine. But these applications are a long way off, the researchers said.”

“This is potentially the new Human Genome Project,” Karr said. “It’s going to take a really large community effort to get close to a human model.”

Source: Stanford University and Wikipedia

Image Credits: Illustration by Erik Jacobsen / Covert Lab; Mycoplasma via Wikimedia Commons

7 thoughts on “First Complete Computer Model Of An Organism Created”

  1. Assuming it was ill, would M.Genitalium really believe that it was going to get better?

    The placebo effect is perhaps our greatest medical mystery, we are nowhere near understanding how it works, let alone fitting it into a computer model.

    Great job to the guys involved in the project, however, like the atom, it’s a work in progress.

  2. This is bollocks. There are zillions of unknown parameters. Go ahead – call the “simulate” function and lets see the thing live….

  3. So what happens if we can model a human being and the simulation perceives itself to be alive, wouldn’t that present a whole host of ethical problems?

    1. Humans will never be able to model consciousness with a computer, because consciousness is in the realm of spirit. It is the spirit which gives inanimate objects the ability to move. Not the other way around.

      We cannot perceive God with our eyes. Yet it is God which allows our eyes to see.

      1. The evidence the consciousness is spirit is as strong as the evidence of life being spirit before biochemistry and as strong as the evidence that fire was spirit before chemistry. Which is to say there is no evidence.

        God of the gaps is the god used to “explain” whatever is not yet understood. But it explains nothing, it just makes people who are uncomfortable with the current lack explanation feel better. And fortunately, it has never held back scientific curiosity and inquiry, except in the individuals who fall for it, unless backed up by religious-political power.

        We don’t understand consciousness now, but there are very good reasons to believe we will in the next few decades. The brain is very complicated, but the rate at which we are learning how it works is incredible. The profound insights into the world that are happening at a greater rate demonstrate that discovery is accelerating not hitting a “spirit” wall.

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