CHE.496/2009/Responses/a12

Systems biology and synthetic biology

• Discussion leader: Brandon

Thaddeus Webb's Response

Systems biology as a foundation for genome-scale synthetic biology

• Offers ideas about how synthetic and systems biology will come together.
• The goal of reconstruction would be the composition of a mathematical model describing all of the parts of a biological system accurately.
• Currently use stoichiometric matrix for reconstruction.
• Will benefit from high input data experiments
• Will need to define parameters to turn reconstructions into models
• Software and computing will drive synthetic biology.
• Until models are extremely accurate systems will require the use of directed evolution to be tuned.
• Tying optimization to growth is an effective way to direct evolution.

Network Benchmarking: A Happy Marriage between Systems and Synthetic Biology

• Outlines the mutualism between synthetic and systems biology.
• Systems biology modeling will enable synthetic design.
• A five gene system was created
  • Had well defined parts
  • robust against cellular inputs
  • interesting abilities
  • easy external control
• Designers used this network to estimate parameters.

Thaddeus Webb 23:05, 1 April 2009 (EDT)

Patrick Gildea's Response

• Systems biology as a foundation for genome-scale synthetic biology
• Network Benchmarking: A Happy Marriage between Systems and Synthetic Biology
  • *Patrick Gildea 18:32, 2 April 2009 (EDT):

Rohini's Response

Systems Biology Article

• Systems Biotechnology- systems level analysis of metabolic, gene regulatory and signaling networks
• Goal for the field of Synthetic Biology- characterize and accurately model cellular systems
• Reconstruction of a cellular system- collecting and putting molecular components together in a mathematical consistent manner ( “in silico” ) (Matrix representation is a powerful tool)
• High throughput biology- efficiently identify cellular constituents by GPR associations
• Problem with in silico models- determining parameters, inaccuracies in design algorithms
• Cytoscape- used for static data analysis
• How to accomplish parameter tuning?

-Use directed evolution to identify the appropriate selection pressure -Couple the synthetic design to growth to force parameter tuning and ensure evolutionary stability

• OptStrain strategy:

1) Collect reactions from database 2) Calculate maximum theoretical yield 3) Identify a pathway that maximizes the yield and minimizes the number of non-native functionalities 4) Optimize the metabolic pathway

• Goal of Systems Biology- integrate quantitative data to make informative cell-type measurements (ex. protein quantification

Network Benchmarking Article=

• Synthetic Biology- design/construction of artificial biological networks to understand how natural systems function

(ex. Build a synthetic oscillatory gene network to help understand how circadian rhythm is created)

• Problems with the field of Synthetic Biology- lack of golden standards and extreme complexity dealing with biological systems
• Research that combines synthetic biology and systems biology- constructing a synthetic gene regulatory network in yeast and utilizing it for several reverse engineering and modeling approaches. ( project- 5 gene network with well characterized transcription factors. It contained various interactions, i.e., positive and negative feedback loops and transcriptional cascading. The network was controlled by galactose and therefore could be inactivated or activated by its presence. The researchers performed a number of perturbation experiments. For the mathematical modeling aspect of their project, they used ordinary differential equations. They were able to access the strengths and weaknesses of different network inference algorithms by comparing the over expression of the gene with varying the concentration of glucose and galactose.

Rohini Manaktala 18:42, 2 April 2009 (EDT)