New Model Predicts Molecular Response of Living Cells
Scientists
at the Institute for Systems Biology in Seattle, Wash., in
collaboration with researchers from New York University, have developed
a model that rapidly characterizes and accurately predicts the
molecular-level, mechanistic response of a free-living cell to genetic
and environmental changes. The paper describing the model was published
Dec. 27 in the online edition of the journal Cell.
The Environmental and Gene Regulatory Influence (EGRIN) model
provides information that helps researchers understand how complex
biological systems work. Scientists hope that knowledge will open the
door to more complex genetic engineering that produces fewer unintended
consequences.
"Unraveling complex biological networks is why I came to ISB," said
Nitin Baliga, Ph.D, an associate professor at the institute. "The
systems approach to biology, of which the founders of ISB were early
champions, has proven to be a spectacular success in achieving a
molecular level understanding of complex biology, which is necessary if
we are to engineer cells back to health or reengineer organisms to
improve bioenergy production or bioremediation, for example," Baliga
said.
The EGRIN model linked biological processes with previously unknown
molecular relationships and accurately predicted both new regulation of
known biological processes and the transcriptional responses of more
than 1,900 genes to completely novel genetic and environmental
experiments.
Baliga and colleagues used Halobacterium salinarum NRC-1, a member
of the Archaea family of organisms, because it has been the subject of
relatively little scientific study. Archael organisms have evolved to
thrive in harsh environments that would be lethal to most other
organisms. As a result, their unique biology could provide new
solutions to challenges in environmental contamination, energy
production and healthcare.
Working with an organism about which relatively little is known
allowed the Baliga lab to demonstrate the value of taking a systems
approach, which can lead to the rapid discovery of structure and
function in unstudied biological networks.
"The ability to gather this level of information regarding a poorly
characterized organism from a single study is significant and
unprecedented," Baliga said. "In addition, the nature of the EGRIN
model is such that it's applicable to many complex biological networks."
The process of discovery involved perturbing cells (for example,
altering, individually and in combination, 10 environmental factors and
32 genes), characterizing growth and/or survival phenotype,
quantitatively measuring steady state and dynamic changes in mRNA,
assimilating the changes into a network model able to repeat the
observations and experimentally validating hypotheses formulated
through the model. More than 230 out of 413 microarray experiments used
were collected and/or conducted specifically for this study. In
addition, researchers used data from genome-wide binding location
analysis for eight transcription factors, mass spectrometry-based
proteomic analysis, protein structure predictions, computational
analysis of genome structure and protein evolution as well as data from
public sources.
The vast array of approaches to data gathering and validation
required a systems biology approach, in which scientists of varied
disciplines (for example, biochemistry, physics, mathematics,
computation, statistics, genetics and more) collaborate and contribute
their skill sets to the achievement of a single scientific objective.
The researchers' next steps involve applying the EGRIN model to more
complicated organisms and/or networks, and actually reengineering
organisms based on knowledge obtained through the EGRIN model.
"It will take a lot more effort before the EGRIN model can be
applied in a practical fashion," Baliga said. "At this point we've
basically proven that we can develop a comprehensive understanding of
how complex biological systems work, which has been an open question to
this point."