Cooperators Can Coexist With Cheaters, as Long as There Is Room to Grow

Feb. 1, 2013 — Microbes exhibit bewildering diversity even in relatively tight living quarters. But when a population is a mix of cooperators, microbes that share resources, and cheaters, those that selfishly take yet give nothing back, the natural outcome is perpetual war. A new model by a team of researchers from Princeton University in New Jersey and Ben-Gurion University in Israel reveals that even with never-ending battles, the exploiter and the exploited can survive, but only if they have room to expand and grow.

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The researchers present their findings at the 57th Annual Meeting of the Biophysical Society (BPS), held Feb. 2-6, 2013, in Philadelphia, Pa.

“In a fixed population, cells that share can’t live together with cells that only take,” said David Bruce Borenstein, a researcher at Princeton. “But if the population repeatedly expands and contracts then such ‘cooperators’ and ‘cheaters’ can coexist.”

Our world and our bodies play host to a vast array of microbes. On our teeth alone, there are approximately a thousand different kinds of bacteria, all living in very close quarters. This is amazing, the researchers observe, because many of those species share resources with nearby neighbors, who might not be so cooperative or even related [1].

At the scale of cells, individuals cooperate mainly by exporting resources into the environment and letting them float away. “This is a deceptively complex process in which cells interact at long ranges, but compete only with nearby individuals,” explained Borenstein. “Our models predict that, even when this exploitation prevents any possibility of peaceful coexistence, the exploiter and the exploited can survive across generations in what is basically a perpetual war.” The researchers speculate that similar competition might occur between cancer cells and normal tissue.

Borenstein and his colleagues made their conclusions based on a computer model that considered two types of cells, cooperators and cheaters, and laid them out on a grid. Cooperators were given the ability, not uncommon in nature, to make a resource that speeds up growth in both kinds of cells. Producing this resource slowed down the growth of cooperators, because they have to divert some energy to resource production. This resource then spread out from the cooperator by diffusion, so that the cells closest to a producer have the greatest resource access. The model revealed that the producers tended to cluster, meaning that being a producer gave you greater access to resources. It also meant that even though cheaters are avoiding the cost of production, they pay for it with reduced resource access.

Within these basic constraints it was found that when the two populations must compete directly for survival, no coexistence is possible. “One type always wins out,” observed Borenstein. However, when the two populations can grow into empty space, the researchers found a strange and paradoxical interaction: cheaters may be outcompeting cooperators locally, even as cooperators grow better overall. These complex interactions may play an important role in the maintenance of diverse microbial communities, like those seen in the mouth.

“To our astonishment, we found that while cheaters can exploit cooperators, cooperators can isolate cheaters, just from cooperation and growth,” concludes Borenstein. “As a result, the community can persist in a sort of perpetual race from which a winner need not emerge.”

[1] J. M. ten Cate. “Biofilms, a new approach to the microbiology of dental plaque.” Odontolgy 2006(94):1-9.

 

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Story Source:

The above story is reprinted from materials provided byBiophysical Society, via Newswise.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

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Biophysical Society (2013, February 1). Cooperators can coexist with cheaters, as long as there is room to grow. ScienceDaily. Retrieved February 3, 2013, from http://www.sciencedaily.com/releases/2013/02/130201095947.htm

Note: If no author is given, the source is cited instead.

 

Researchers Produce First Complete Computer Model of an Organism

ScienceDaily (July 21, 2012) — In a breakthrough effort for computational biology, the world’s first complete computer model of an organism has been completed, Stanford researchers reported last week in the journal Cell.

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A team led by Markus Covert, 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.”

The research was partially funded by an NIH Director’s Pioneer Award from the National Institutes of Health Common Fund.

From information to understanding

Biology over the past two decades has been marked by the rise of high-throughput studies producing enormous troves of cellular information. 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.

Most biological experiments, however, still take a reductionist approach to this vast array of data: 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 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,” according to Stanford bioengineering graduate student and co-first author Jayodita Sanghvi.

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,” Sanghvi said.

Small is beautiful

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.

Probing the silicon cell

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.

These kinds of findings remain hypotheses until they’re confirmed by real-world experiments, but they 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,” said Covert.

Bio-CAD

Much of the model’s future promise lies in more applied fields.

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.”

 

Link:

http://news.stanford.edu/pr/2012/pr-computer-model-organism-071812.html

Journal Reference:

1. Jonathan R. Karr, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, Jared M. Jacobs, Benjamin Bolival, Nacyra Assad-Garcia, John I. Glass, Markus W. Covert. A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell, 2012; 150 (2): 389 DOI: 10.1016/j.cell.2012.05.044

Citation:

Stanford University (2012, July 21). Researchers produce first complete computer model of an organism. ScienceDaily. Retrieved July 22, 2012, from http://www.sciencedaily.com­ /releases/2012/07/120721091451.htm