Feeds:
Posts
Comments

Posts Tagged ‘Gene regulatory network’


Genomic Model of Organogenesis: Computer Modeling of the Gene Regulatory Networks

Curator: Larry H Bernstein, MD, FACP

 

 

Caltech biologists created the first predictive computational model of gene networks that control the development of sea-urchin embryos. This model outlines the paths cells take in forming different body parts—muscles, bones, heart. In the process the organ development follows a genetic blueprint, which consists of complex webs of interacting genes called gene regulatory networks.

This model, the scientists say, does a remarkably good job of calculating what these networks do to control the fates of different cells in the early stages of sea-urchin development—confirming that the interactions among a few dozen genes suffice to tell an embryo how to start the development of different body parts in their respective spatial locations. The model is also a powerful tool for understanding gene regulatory networks in a way not previously possible, allowing scientists to better study the genetic bases of both development and evolution.

“We have never had the opportunity to explore the significance of these networks before,” says Eric Davidson, the Norman Chandler Professor of Cell Biology at Caltech. “The results are amazing to us.”

The researchers described their computer model in a paper in the Proceedings of the National Academy of Sciences that appeared as an advance online publication on August 27.

The model encompasses the gene regulatory network that controls the first 30 hours of the development of endomesoderm cells, which eventually form the embryo’s gut, skeleton, muscles, and immune system. This network—so far the most extensively analyzed developmental gene regulatory network of any animal organism—consists of about 50 regulatory genes that turn one another on and off.

To create the model, the researchers distilled everything they knew about the network into a series of logical statements that a computer could understand. “We translated all of our biological knowledge into very simple Boolean statements,” explains Isabelle Peter, a senior research fellow and the first author of the paper. In other words, the researchers represented the network as a series of if-then statements that determine whether certain genes in different cells are on or off (i.e., if gene A is on, then genes B and C will turn off).

By computing the results of each sequence hour by hour, the model determines when and where in the embryo each gene is on and off. Comparing the computed results with experiments, the researchers found that the model reproduced the data almost exactly. “It works surprisingly well,” Peter says.

Some details about the network may still be uncovered, the researchers say, but the fact that the model mirrors a real embryo so well shows that biologists have indeed identified almost all of the genes that are necessary to control these particular developmental processes. The model is accurate enough that the researchers can tweak specific parts—for example, suppress a particular gene—and get computed results that match those of previous experiments.

Allowing biologists to do these kinds of virtual experiments is precisely how computer models can be powerful tools, Peter says. Gene regulatory networks are so complex that it is almost impossible for a person to fully understand the role of each gene without the help of a computational model, which can reveal how the networks function in unprecedented detail.

Studying gene regulatory networks with models may also offer new insights into the evolutionary origins of species. By comparing the gene regulatory networks of different species, biologists can probe how they branched off from common ancestors at the genetic level.

So far, the researchers have only modeled one gene regulatory network, but their goal is to model the networks responsible for every part of a sea-urchin embryo, to build a model that covers not just the first 30 hours of a sea urchin’s life but its entire embryonic development. Now that this modeling approach has been proven effective, Davidson says, creating a complete model is just a matter of time, effort, and resources. 

The title of the PNAS paper is “Predictive computation of genomic logic processing functions in embryonic development.”

http://www.pnas.org/content/109/41/16434.abstract

In addition to Peter and Davidson, the other author on the PNAS paper is Emmanuel Faure, a former Caltech postdoctoral scholar who is now at the École Polytechnique in France. This work was supported by the National Institute of Child Health and Human Development and the National Institute of General Medical Sciences.

A small part of the network is shown here. Image: Caltech/Davidson Lab
After a decade detailing how these gene networks control development in sea-urchin embryos, they   constructed a computational model of sea-urchin embryonic development.
VIEW VIDEO Courtesy of genenetwork

Introduction to Gene Network

GeneNetwork is a group of linked data sets and tools used to study complex networks of genes, molecules, and higher order gene function and phenotypes. GeneNetwork combines more than 25 years of legacy data generated by hundreds of scientists together with sequence data (SNPs) and massive transcriptome data sets (expression genetic or eQTL data sets). The quantitative trait locus (QTL) mapping module that is built into GN is optimized for fast on-line analysis of traits that are controlled by combinations of gene variants and environmental factors.

Historic Highlights of Organ Development

Otto Warburg. Improved manometric techniques of Van Slyke and Haldane in 1920’s and used tissue slices of 100-150 layers of cells, allowing measurement of energy reactions using oxygen without damaging cells.  He demonstrated the rate of oxygen utilization and the respiration of sea urchin egg can increase up to sixfold after fertilization.

Otto Warburg. Hans Krebs.  Clarendon Press. 1981.
Thomas Hunt Morgan.  Explored the mechanism of heredity in accounting for the transmission of variations from 1910 -1928, and claimed that while Mendelian theory could predict breeding results, it could not describe the true processes of heredity.
N William Ingalls (1918)
Carnegie Institution No. 23 – Contributions to Embryology
The conditions found here in the cloacal membrane are such as would be expected from the gradual and not entirely regular transformation of the streak into the membrane. All that is required is an arrest of mesoderm formation and the subsequent separation of the upper and middle germ-layers. The entoderm below is a perfectly distinct layer the cells of which have nuclei larger and paler than those of the other layers.
The embryo which we have just described represents an extremely interesting and instructive stage in the ontogenesis of man. In it are found as many important features of early development as could well be expected in one and the same specimen.  Any discussion of the findings in this embryo naturally revolves around the question of gastrulation and the formation of the germ-layers. One should not conclude too much from a single stage, either as to antecedent or later conditions; but every stage must be in harmony with those which precede or follow.
 Hans Spemann (1869 – 1941). Awarded a Nobel Prize in Physiology or Medicine in 1935 for his discovery of the effect now known as embryonic induction. Spemann found that one half of two blastomeres could form a whole embryo, but observed that the plane of division was crucial. This gave support to the concept of a morphogenetic field, a concept of which Spemann learned from Paul Alfred Weiss.  He and colleagues described an area in the embryo, the portions of which, upon transplantation into a second embryo, organized or “induced” secondary embryonic primordia regardless of location.
NOBEL PRIZE FOR GENETICS OF DEVELOPMENT  By Sean Henahan, Access Excellence
Three biologists have been awarded the 1995 Nobel Prize in Medicine for their pioneering work on the genetic control of embryonic development. The researchers work with the Drosophila melanogaster fruit fly provided key information on factors influencing human embryology and birth defects. The recipients of this year’s prize are Drs. Edward Lewis, of the California Institute of Technology; Christiane Nuesslein-Volhard, of Germany’s Max-Planck Institute; and Eric Wieschaus, at Princeton. Each of the three were involved in the early research to find the genes controlling development.
The genes were arranged in the same order on the chromosomes as the body segments they controlled. The first genes in a complex of developmental genes controlled the head region, genes in the middle controlled abdominal segments while the last genes controlled the posterior (“tail”) region.  The fertilized egg is spherical. It divides rapidly to form 2, 4 , 8 cells and so on. Up until the 16-cell stage the early embryo is symmetrical and all cells are equal. Beyond this point, cells begin to specialize and the embryo becomes asymmetrical. Within a week it becomes clear what will form the head and tail regions and what will become the ventral and dorsal sides of the embryo. Somewhat later in development the body of the embryo forms segments and the position of the vertebral column is fixed.
The results of Nuesslein-Volhard and Wieschaus, first published in the English scientific journal Nature during the fall of 1980, established that genes controlling development could be systematically identified. The number of genes involved was limited and they could be classified into specific functional groups.
In 1978 Lewis summarized his results in a review article and formulated theories about how homeotic genes interact, how the gene order corresponded to the segment order along the body axis, and how the individual genes were expressed. This induced other scientists to examine families of analogous genes in higher organisms. In mammalians, the gene clusters first found in Drosophila have been duplicated into four complexes known as the HOX genes. Human genes in these complexes are sufficiently similar to their Drosophila analogues they can restore some of the normal functions of mutant Drosophila genes.
The individual genes within the four HOX gene families in vertebrates occur in the same order as they do in Drosophila, and they exert their influence along the body axis in agreement with the colinearity principle first discovered by Lewis in Drosophila. It is likely that mutations in such important genes are responsible for some of the early, spontaneous abortions that occur in man, and for some of the about 40% of the congenital malformations that develop due to unknown reasons.
Lewis, E.B. (1978) A Gene Complex Controlling Segmentation in Drosophila. Nature 276, 565-570 Nuesslein-Volhard, C., Wieschaus, E. (1980). Mutations Affecting Segment Number and Polarity in Drosophila. Nature 287, 795-801
 Sir John B Gurdon and Shinya Yamanaka. 2012 Nobel Prize for Physiology and Medicine.
the specialisation of cells is reversible,and mature, specialized cells can be reprogrammed.

Work by the Stanford Group on Gene Networks Computational Model

Protein-folding Simulation: Stanford’s Framework for Testing and Predicting Evolutionary Outcomes in Living Organisms – Work by Marcus Feldman

Other Notable and Related Research.
LASAGNA-Search: An integrated web tool for transcription factor binding site search and visualization.  C Lee and Chun-Hsi Huang.
Length-Aware Site Alignment Guided by Nucleotide Association (LASAGNA)
1. unaligned variable-length TF binding sites
2. used for high throughput techniques, such as ChIP-seq
3. a collection of 1726 models
4. automatic promoter sequence retrieval
5. visualization
Biotechniques Rapid Dispatches   http://dx.doi.org/10.2144/000113999

Gene Splicing by Overlap Extension: Tailor-Made Genes Using PCR

RM Horton, Z Cai, SN Ho, LR Pease.  Biotechniques Nov 1990; 8(5):528-535.
Gene splicing by Overlap Extension or “geneSOEing” is a PCR-based recombining DNA sequences without reliance on restriction sites and of directly generated DNA fragments in vitro.  Method relies on modifying the sequences incorporated into the 5′-ends of the primers. Strands from two different fragments can hybridize together forming and overlap.

Democritixing Flow Cytometry.

Reported by M O’Neill. Geneg News Mar 15, 2013; 33(6): p12
Due to advances on many fronts
1. microfluidics
2. software
Flow cytometry is being simplified so that it can be used by a broader range of scientist and clinicians.
Eight Hox genes of D. melanogaster (fruitfly).

Eight Hox genes of D. melanogaster (fruitfly). (Photo credit: Wikipedia)

Read Full Post »


 

Reported by: Dr. Venkat S. Karra, Ph.D.

Biologists create first predictive computational model of gene networks

Biologists at the California Institute of Technology (Caltech) have spent the last decade or so detailing how these gene networks control development in sea-urchin embryos. Now, for the first time, they have built a computational model of one of these networks.

This model, the scientists say, does a remarkably good job of calculating what these networks do to control the fates of different cells in the early stages of sea-urchin development—confirming that the interactions among a few dozen genes suffice to tell an embryo how to start the development of different body parts in their respective spatial locations. The model is also a powerful tool for understanding gene regulatory networks in a way not previously possible, allowing scientists to better study the genetic bases of both development and evolution.

The researchers described their computer model in a paper in the Proceedings of the National Academy of Sciences.

The model encompasses the gene regulatory network that controls the first 30 hours of the development of endomesoderm cells, which eventually form the embryo’s gut, skeleton, muscles, and immune system. This network—so far the most extensively analyzed developmental gene regulatory network of any animal organism—consists of about 50 regulatory genes that turn one another on and off.

To create the model, the researchers distilled everything they knew about the network into a series of logical statements that a computer could understand.

By computing the results of each sequence hour by hour, the model determines when and where in the embryo each gene is on and off. Comparing the computed results with experiments, the researchers found that the model reproduced the data almost exactly. “It works surprisingly well,” the researchers say.

Read more at:

rdmag

California Institute of Technology

 

 

 

 

Read Full Post »