Simple computer models unravel genetic stress reactions in cellsSimple Computer Models Unravel Genetic Stress Reactions in Cells1360310400000Integrated biological and computational methods provide insight into why genes are activated.http://farm9.staticflickr.com/8094/8456884678_818373bcc6_o.jpgWhen complete, these barriers will be a portion of the NMSSUP upgrade.This molecular structure depicts a yeast transfer ribonucleic acid (tRNA), which carries a single amino acid to the ribosome during protein construction. A combined experimental and computational approach, to better understand signaling pathways that lead to genetic mutations, is at the core of this research. http://farm9.staticflickr.com/8094/8456884678_b5d4b125af_q.jpgSimple computer models unravel genetic stress reactions in cellsIntegrated biological and computational methods provide insight into why genes are activated.Experimental and computational scientists improve the illusive process of genetic prediction
with a simple model
Many questions arise when two identical twins raised in the same home—fed the same, nurtured the same way—follow disparate paths. How can the
identical offspring of a single egg turn out so differently?
Scientists ask the
same question when genetically identical cells in identical environments—monoclonal
cells produced by a single ancestor that replicated—exhibit wildly different
behaviors. Researchers say the changes may be due to random biochemical
fluctuations known as stochasticity, or “noise”—variability occurring over time
and influenced by environmental factors.
Cells are highly sensitive to this noise, and minor fluctuations can lead to major changes, such
as an Alzheimer’s gene turning on or off or a cancer cell not responding to
chemotheraphy. Are these random events due to chance or is there an
undiscovered cause?
The answer might be contained within
nearly undetectable processes—the noise—at the molecular level, so researchers
must peer into individual cells really, really closely without nudging and
accidentally altering these sensitive cells and their processes. This is
complicated in itself, there are so many cells—about 100 trillion in each human. All that can get
lost in the noise, and even supercomputers cannot hope to capture all aspects
of this incredible complexity.
But experimental and computational scientists developed
methods to sift through all the complexity and improve the illusive process of prediction
with a simple model.
Unraveling
genetic stress reactions
Brian Munsky, a scientist at Los Alamos National
Laboratory’s Center for Nonlinear Studies, is adept at untangling biology. Munsky
and colleagues report their combined experimental and modeling prediction
methods in the Feb. 1, 2013 edition of Science.
These methods integrate single-cell experiments and discrete stochastic
analysis to predict complex gene expression and signaling behaviors in Saccharomyces cerevisiae—or yeast, a
scientific-lab standard since yeast and human cells share many genes.
Scientists frequently test drugs or biological processes on yeast before
advancing to human trials.
Using a technique called
smFISH (single-molecule fluorescence in situ
hybridization), the team attached dozens of small fluorescently labeled probes
to each molecule of specific messenger ribonucleic acid (RNA), which causes
these molecules to light up under the microscope. Some cells turn on (lots of
lights) while others remain off. This data was incorporated into several
models. Munsky notes that it would be impossible to combine the infinite
possible reactions at different scales into a single computational model.
The combined, data-driven
approach that includes validation allows researchers to systematically
determine when models are too simple, too complex or just right—the
“Goldilocks” approach.
The approach developed in this study helps researchers understand cellular death at the single-molecule and
single-cell level—with wide applications
The research team identified predictive models of
transcription—the first step in gene expression—when the yeast cell is
responding to osmotic stress (salt), which greatly affects cell growth.
Understanding how yeast cope with osmotic stress is useful to understand how
human cells respond to medical treatments, but the principles are also applicable
to many other fields, including agriculture.
“Depending upon molecular fluctuations, a single gene in a
cell may switch randomly between active and inactive states, leading to vastly
different biological responses, even for genetically identical cells,” says
Munsky. “The fluctuations can be quantified at the single-molecule and
single-cell levels.”
Munsky was a joint first author on the Science paper with Vanderbilt University’s Gregor Neuert. The paper
was selected as an Editors’ Choice by the magazine.
“The approach we developed in this study is applicable far
beyond yeast,” Munsky adds. “Our experimental and computational analyses could
enable quantitative prediction for any gene, pathway or organism.”
Read the Science article: Systematic Identification of Signal-Activated Stochastic Gene Regulation/The combined, data-driven approach that includes validation allows researchers to systematically determine when models are too simple, too complex or just right—the “Goldilocks” approach. http://farm9.staticflickr.com/8518/8455818905_9fbbb41933_o.jpghttp://farm9.staticflickr.com/8518/8455818905_bec911e40f_q.jpgBrian Munsky, PhD – Photo Credit: Brian MunskyYesGet Expertise/newsroom/_assets/thumbnails/brian-munsky-thumbnail.jpgsite://Green/newsroom/_assets/thumbnails/brian-munsky-thumbnail.jpgGreenbrian-munsky-thumbnail.jpgBrian MunskyDistinguished Postdoctoral FellowBrian Munsky, PhDInformation Services, Advanced Measurement Science(505) [email protected] Computer Models Unravel Genetic Stress Reactions in CellsIntegrated biological and computational methods provide insight into why genes are activated.February 8, 2013This molecular structure depicts a yeast transfer ribonucleic acid (tRNA), which carries a single amino acid to the ribosome during protein construction. A combined experimental and computational approach, to better understand signaling pathways that lead to genetic mutations, is at the core of this research. Get Expertise Distinguished Postdoctoral FellowBrian Munsky, PhDInformation Services, Advanced Measurement Science(505) 665-6691
Email
The combined, data-driven approach that includes validation allows researchers to systematically determine when models are too simple, too complex or just right—the “Goldilocks” approach. Experimental and computational scientists improve the illusive process of genetic prediction
with a simple model
Many questions arise when two identical twins raised in the same home—fed the same, nurtured the same way—follow disparate paths. How can the
identical offspring of a single egg turn out so differently?
Scientists ask the
same question when genetically identical cells in identical environments—monoclonal
cells produced by a single ancestor that replicated—exhibit wildly different
behaviors. Researchers say the changes may be due to random biochemical
fluctuations known as stochasticity, or “noise”—variability occurring over time
and influenced by environmental factors.
Cells are highly sensitive to this noise, and minor fluctuations can lead to major changes, such
as an Alzheimer’s gene turning on or off or a cancer cell not responding to
chemotheraphy. Are these random events due to chance or is there an
undiscovered cause?
The answer might be contained within
nearly undetectable processes—the noise—at the molecular level, so researchers
must peer into individual cells really, really closely without nudging and
accidentally altering these sensitive cells and their processes. This is
complicated in itself, there are so many cells—about 100 trillion in each human. All that can get
lost in the noise, and even supercomputers cannot hope to capture all aspects
of this incredible complexity.
But experimental and computational scientists developed
methods to sift through all the complexity and improve the illusive process of prediction
with a simple model.
Unraveling
genetic stress reactions
Brian Munsky, a scientist at Los Alamos National
Laboratory’s Center for Nonlinear Studies, is adept at untangling biology. Munsky
and colleagues report their combined experimental and modeling prediction
methods in the Feb. 1, 2013 edition of Science.
These methods integrate single-cell experiments and discrete stochastic
analysis to predict complex gene expression and signaling behaviors in Saccharomyces cerevisiae—or yeast, a
scientific-lab standard since yeast and human cells share many genes.
Scientists frequently test drugs or biological processes on yeast before
advancing to human trials.
Using a technique called
smFISH (single-molecule fluorescence in situ
hybridization), the team attached dozens of small fluorescently labeled probes
to each molecule of specific messenger ribonucleic acid (RNA), which causes
these molecules to light up under the microscope. Some cells turn on (lots of
lights) while others remain off. This data was incorporated into several
models. Munsky notes that it would be impossible to combine the infinite
possible reactions at different scales into a single computational model.
The combined, data-driven
approach that includes validation allows researchers to systematically
determine when models are too simple, too complex or just right—the
“Goldilocks” approach.
The approach developed in this study helps researchers understand cellular death at the single-molecule and
single-cell level—with wide applications
The research team identified predictive models of
transcription—the first step in gene expression—when the yeast cell is
responding to osmotic stress (salt), which greatly affects cell growth.
Understanding how yeast cope with osmotic stress is useful to understand how
human cells respond to medical treatments, but the principles are also applicable
to many other fields, including agriculture.
“Depending upon molecular fluctuations, a single gene in a
cell may switch randomly between active and inactive states, leading to vastly
different biological responses, even for genetically identical cells,” says
Munsky. “The fluctuations can be quantified at the single-molecule and
single-cell levels.”
Munsky was a joint first author on the Science paper with Vanderbilt University’s Gregor Neuert. The paper
was selected as an Editors’ Choice by the magazine.
“The approach we developed in this study is applicable far
beyond yeast,” Munsky adds. “Our experimental and computational analyses could
enable quantitative prediction for any gene, pathway or organism.”
Read the Science article: Systematic Identification of Signal-Activated Stochastic Gene RegulationRELATED IMAGES