Using engineering techniques to study biological systems: systems biology software is taking advantage of the modeling, simulation, and analysis techniques created for engineering and mathematical systems.
The application of engineering techniques to biological systems appears to be a big jump--until we consider how these systems are described. Biological systems are often described as a series of chemical reactions or by a set of ordinary differential equations. Differential equations are often the core of engineering systems, as well. Because of this underlying similarity, many traditional engineering techniques are helping systems biologists gain further insight into biological systems.
While the techniques used to build engineering systems, such as modeling, simulation, and analysis, can be applied in systems biology, few research groups have used them successfully. This is partly because researchers lack the necessary tools and because most biological systems are much more complex than even the most sophisticated man-made systems. As a result, it has taken decades for biologists to gain enough insight into system components and interactions to be able to model them.
While computational methods have yet to be universally adopted, some common engineering techniques are becoming widely used in systems biology--notably, parameter estimation, simulation, and sensitivity analysis.
Engineers use parameter estimation to calibrate the response of a model to the observed outputs of a physical system. Instead of using educated guesses to adjust model parameters and initial conditions, they automatically compute these values using data gathered through experiments.
Simulation allows engineers to observe a system in action, change its inputs, parameters, and components, and analyze the results computationally. Most engineering simulations are deterministic: motors, circuits, and control systems must all provide the same outputs for a given set of inputs for each simulation run. Biological simulations, on the other hand, must incorporate the innate randomness of nature.
Sensitivity analysis enables engineers to determine which components of the model have the greatest effect on its output. In systems biology, sensitivity analysis provides a computational mechanism to determine which parameters are most important under a specific set of conditions. In a model with 200 species and 100 different parameters, being able to determine which species and parameters most effect the desired output can eliminate fruitless avenues of research and enable scientists to focus wet-bench experiments on the most promising candidates.
Obstacles to adoption
Systems biology research requires contributions from a diverse group of researchers. Modelers understand the computational approach and the mathematics behind it, while the scientists know the underlying biology. The two groups frequently use a different vocabulary and work with different concepts and tools. Many software tools cater to either modelers or scientists, creating a roadblock on systems biology projects that require collaboration.
It was in response to these obstacles that The MathWorks, Natick, Mass., developed SimBiology. SimBiology provides a number of capabilities needed by biologists, such as stochastic solvers, sensitivity analysis, and conservation of laws, including mass and energy. To meet the needs of both modelers and scientists, there are three different ways to interact with SimBiology: via a graphical interface, a tabular interface, or the command line. The graphical interface enables scientists to construct molecular pathways by dragging and dropping species, reactions, blocks, and lines. The tabular interface lets them type in reactants, products, parameters, and rules. All functionality is also accessible from the command line, enabling programmers to write scripts and code. These three approaches to using the tool enable scientists and modelers to collaborate and complete their entire workflow in the same software environment. Biologists can build a model by graphically defining reactions. SimBiology converts the defined reactions into a mathematical model that the modeler can refine, analyze, and simulate. In the same way, modelers can create a complex, mathematically intensive model and then use a graphical representation of the model to communicate their work to biologists.
Find the full length version of this article at www.rdmag.com
The MathWorks, Natick, Mass., www.mathworks.com
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|Publication:||R & D|
|Date:||Jun 1, 2007|
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