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.


 

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

 

Entire Genetic Sequence of Individual Human Sperm Determined

ScienceDaily (July 19, 2012) — The entire genomes of 91 human sperm from one man have been sequenced by Stanford University researchers. The results provide a fascinating glimpse into naturally occurring genetic variation in one individual, and are the first to report the whole-genome sequence of a human gamete — the only cells that become a child and through which parents pass on physical traits.


“This represents the culmination of nearly a decade of work in my lab,” said Stephen Quake, PhD, the Lee Otterson Professor in the School of Engineering and professor of bioengineering and of applied physics. “We now have devices that will allow us to routinely amplify and sequence to a high degree of accuracy the entire genomes of single cells, which has far-ranging implications for the study of cancer, infertility and many other disorders.”

Quake is the senior author of the research, published July 20 in Cell. Graduate student Jianbin Wang and former graduate student H. Christina Fan, PhD, now a senior scientist at ImmuMetrix, share first authorship of the paper.

Sequencing sperm cells is particularly interesting because of a natural process called recombination that ensures that a baby is a blend of DNA from all four of his or her grandparents. Until now, scientists had to rely on genetic studies of populations to estimate how frequently recombination had occurred in individual sperm and egg cells, and how much genetic mixing that entailed.

“Single-sperm sequencing will allow us to chart and understand how recombination differs between individuals at the finest scales. This is an important proof of principle that will allow us to study both fundamental dynamics of recombination in humans and whether it is involved in issues relating to male infertility,” said Gilean McVean, PhD, professor of statistical genetics at the Wellcome Trust Centre for Human Genetics. McVean was not involved in the research.

The Stanford study showed that the previous, population-based estimates were, for the most part, surprisingly accurate: on average, the sperm in the sample had each undergone about 23 recombinations, or mixing events. However, individual sperm varied greatly in the degree of genetic mixing and in the number and severity of spontaneously arising genetic mutations. Two sperm were missing entire chromosomes. The study has long-ranging implication for infertility doctors and researchers.

“For the first time, we were able to generate an individual recombination map and mutation rate for each of several sperm from one person,” said study co-author Barry Behr, PhD, HCLD, professor of obstetrics and gynecology and director of Stanford’s in vitro fertilization laboratory. “Now we can look at a particular individual, make some calls about what they would likely contribute genetically to an embryo and perhaps even diagnose or detect potential problems.”

Most cells in the human body have two copies of each of 23 chromosomes, and are known as “diploid” cells. Recombination occurs during a process called meiosis, which partitions a single copy of each chromosome into a sperm (in a man) or egg (in a woman) cell. When a sperm and an egg join, the resulting fertilized egg again has a full complement of DNA.

To ensure an orderly distribution during recombination, pairs of chromosomes are lined up in tight formation along the midsection of the cell. During this snug embrace, portions of matching chromosomes are sometimes randomly swapped. The process generates much more genetic variation in a potential offspring than would be possible if only intact chromosomes were segregated into the reproductive cells.

“The exact sites, frequency and degree of this genetic mixing process is unique for each sperm and egg cell,” said Quake, “and we’ve never before been able to see it with this level of detail. It’s very interesting that what happens in one person’s body mirrors the population average.”

Major problems with the recombination process can generate sperm missing portions or even whole chromosomes, making them incapable of or unlikely to fertilize an egg. But it can be difficult for fertility researchers to identify potential problems.

“Most of the techniques we currently use to assess sperm viability are fairly crude,” said Quake.

To conduct the research, Wang, Quake and Behr first isolated and sequenced nearly 100 sperm cells from the study subject, a 40-year-old man. The man has healthy offspring, and the semen sample appeared normal. His whole-genome sequence (obtained from diploid cells) has been previously sequenced to a high level of accuracy.

They then compared the sequence of the sperm with that of the study subject’s diploid genome. They could see, by comparing the sequences of the chromosomes in the diploid cells with those in the haploid sperm cells, where each recombination event took place. The researchers also identified 25 to 36 new single nucleotide mutations in each sperm cell that were not present in the subject’s diploid genome. Such random mutations are another way to generate genetic variation, but if they occur at particular points in the genome they can have deleterious effects.

It’s important to note that individual sperm cells are destroyed by the sequencing process, meaning that they couldn’t go on to be used for fertilization. However, the single-cell sequencing described in the paper could potentially be used to diagnose male reproductive disorders and help infertile couples assess their options. It could also be used to learn more about how male fertility and sperm quality change with increasing age.

“This could serve as a new kind of early detection system for men who may have reproductive problems,” said Behr, who also co-directs Stanford’s reproductive endocrinology and infertility program. “It’s also possible that we could one day use other, correlating features to harmlessly identify healthy sperm for use in IVF. In the end, the DNA is the raw material that ultimately defines a sperm’s potential. If we can learn more about this process, we can better understand human fertility.”

The research was supported by the National Institute of Health, the Chinese Scholarship Council and the Siebel Foundation.

 

Link:

http://med.stanford.edu/ism/2012/july/sperm.html

Journal Reference:

  1. Jianbin Wang, H. Christina Fan, Barry Behr, Stephen R. Quake. Genome-wide Single-Cell Analysis of Recombination Activity and De Novo Mutation Rates in Human Sperm. Cell, 20 July 2012 DOI: 10.1016/j.cell.2012.06.030

Citation:

Stanford University Medical Center (2012, July 19). Entire genetic sequence of individual human sperm determined. ScienceDaily. Retrieved July 21, 2012, from http://www.sciencedaily.com­