CHE.496/2009/Responses/a12

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CHE.496: Biological Systems Design Seminar

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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
    • The purpose of this article is to combine the fields of systems biology and synthetic biology, especially with respect to genome engineering that will result in exploration of genome-scale synthetic biology. In a way, this is being approached with the effort to make the minimal genome for an ideal chassis to be used in further synthetic biology research. Ideally, entire cellular systems can be reconstructed and fitted together in different ways – the minimal genome research has the approach of eliminating cellular components/systems to the point where the cell has enough systems to function but lacks the extraneous systems not required for life. Figure 1 in the article describes the relationships between systems biology and synthetic biology with respect to the spheres of different approaches one could take in both areas. The article makes extensive references to computational modeling of cellular systems – especially metabolic pathways for organisms. On its own, a good model can yield extremely good information about the system that is being studied. However, this is a tool that requires knowledge of the kinetics and fluxes in a metabolic system, in order to calculate the parameters used in the differential equations that defines the rate for each metabolite as it is created and consumed. This can be done via analysis of molecular components in the various cellular systems but is probably outside of the reach of an undergraduate research team to be done in 3 months.
  • Network Benchmarking: A Happy Marriage between Systems and Synthetic Biology
    • As before, the paper combines the fields of synthetic biology and systems biology to refer to the blending of the two branches of research and their effects on each other. Systems biology is generally involved in the understanding and critical analysis of biological networks from metabolic pathways, genomes, protein databases. This research is heavily steeped in computational modeling that is referred as ‘omics modeling. Synthetic biology is more along the lines of designing biological systems. As a result one could use systems biology to design rational biological systems and another could use the results of piecing different biological networks to elucidate function in each biological networks. Figure 1 in the paper outlines an approach taken by Cantone that takes a synthetic gene biological network and using it to build models and reverse engineer components of the network to benchmark interference between genes using differential equations, Bayesian networks (based on probabilities), and information theory. As the article mentions, there is potential for additional research in this area with bigger/more complex networks and the study of protein concentrations.
    • *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)
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