A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches Cantone, I., Marucci, L., Iorio, F., Ricci, M., Belcastro,

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Presentation transcript:

A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches Cantone, I., Marucci, L., Iorio, F., Ricci, M., Belcastro, V., Bansai, M., Santini, S., Bernardo, M., Bernardo, D., and Cosma, M. Cell (2009) 137: Presented by: Alfred Ramirez April 11, 2012

Problem: Determining Networks Elucidating and validating a gene regulatory network is hard and time consuming.

Solution: Need in vivo benchmarking tool Design a novel gene regulatory network Characterize the parameters of the network Model the network mathematically Test your reverse-engineering algorithm on the model

Design: A Novel Network Desired characteristics – Inducible – Orthogonal – Well characterized and nonessential parts

Design: A Novel Network Promoter/repressor/activator pairs – HO/swi5 AAA and HO/Ash1D – MET16/CBF1 – GAL1-10/GAL4 – Ash1D/swi5 AAA One protein-protein interaction – GAL4/GAL80 Chassis: YM4271 Yeast

Design: The IRMA Network

Model: A DE Approach Model network as a systems of differential equations (DE) Need to make assumptions!

Model: A DE Approach

Characterize: Two Types of Perturbations Want to characterize the dynamics of the system Two types of perturbations – Single perturbation and measure mRNA levels at different time points – Multiple perturbations and measure steady-state mRNA levels

Characterize: A Single Perturbation

Characterize: Multiple Perturbations

Test: Reverse Engineering From model parameters, test to see which algorithms can derive a network closest to the true network TSNI: Time Series Network Identification NIR: Network Inference by Regression

Test: TSNI

Test: NIR

Test: Simplified Network Most false interactions involve the protein regulators GAL4 and GAL80. Simplify the true network by eliminating the protein-protein interaction, leaving only transcriptional regulation

Test: TNSI

Test: NIR

Conclusions Developed and characterized a novel synthetic system Can be used to assess different models and network prediction algorithms

Significance Computational approaches help elucidate regulatory interactions in biological processes. Offers a tool to test and improve computational models and network algorithms

Concerns Mathematical model could use additional refining Made claims that didn’t necessarily emerge from the data.

Questions?