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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Rapid Development of Cell State Identification Circuits with Poly-Transfection
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Multifunctionality and robustness trade-offs in model genetic circuits.

Olivier C Martin1, Andreas Wagner

  • 1Univ Paris-Sud, UMR8120, Laboratoire de Genetique Vegetale du Moulon, INRA, and CNRS, Gif-sur-Yvette, F-91190, France.

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|March 28, 2008
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Cellular networks with multiple functions do not necessarily face architectural constraints or robustness trade-offs. Evolution can favor robust multi-functional biological networks through diverse network structures.

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Area of Science:

  • Systems Biology
  • Evolutionary Biology
  • Genetics

Background:

  • Cellular systems, including genetic networks, often perform multiple functions.
  • Examples include Hox genes and Drosophila segmentation genes in developmental events.
  • The impact of multi-functionality on network architecture and robustness is poorly understood.

Purpose of the Study:

  • To investigate whether multi-functionality constrains biological network architecture.
  • To determine if multiple functions lead to trade-offs in robustness to mutations.
  • To explore the evolutionary implications for robust multi-functional networks.

Main Methods:

  • Development of a general mathematical model for transcriptional regulation networks.
  • Analysis of network architecture constraints imposed by requiring multiple functions.
  • Assessment of the correlation between robustness of different functions under mutation.

Main Results:

  • Requiring additional functions exponentially constrains permissible network architectures.
  • Robustness of one function is uncorrelated or weakly positively correlated with others.
  • Robustness trade-offs among functions generally do not arise in modeled systems.

Conclusions:

  • Multi-functionality does not inherently lead to severe architectural constraints or robustness trade-offs.
  • The existence of numerous alternative network structures facilitates the evolution of robust multi-functional systems.
  • Gradual Darwinian evolution can lead to high robustness across multiple functions if sufficient architectural diversity exists.