1.
Simulating Systems Genetics data (An update on SysGenSIM without technical details: those will be provided by Andrea next week) Alberto de la Fuente

2. Systems Genetics data http://www.bioinformatica.crs4.org
A segregating or genetically randomized population is genotyped for many DNA variants, and profiled for phenotypes of interest (e.g. disease phenotypes), gene expression, and potentially other omics variables (protein expression, metabolomics, DNA methylation, etc.

3.
SysGenSIM: Overview

4. SysGenSIM: Dynamical model
SysGenSIM: Dynamical model

5.
SysGenSIM: Why?
Many Systems genetics data can be expected in the near future
Next generation sequencing for genotying and gene- expression
Only genotype is limited in explaining diseases (Schadt, E.E. (2009) Molecular networks as sensors and drivers of common human diseases. Nature 461, )
Many algorithms have been (and even more will be) proposed for Systems Genetics data: need for unbiased evaluation
DREAM project has not considered SysGen data (yet)
Many Gene Network simulators, but none allow to produce SysGen data

6.
SysGenSIM: Funding
NIH, Application ID: 1R01HG Proposal Title: Highly multivariate quantitative trait loci mapping in systems genetics (Amount: 20% of Alberto’s salary)
NIH, Application ID: 1R01GM Proposal Title: SysGenSIM: A Systems Genetics Simulator for Evaluation of Analysis Methods and Sample Size Determination (Amount: 50% of Alberto’s salary)

7. DREAM 5 Systems Genetics challenges
DREAM 5 Systems Genetics challenges
Part A: Soybean challenges
~300 RILs under real and mock infections (Brett Tyler and colleagues at VBI, Virginia Tech and Ohio State University)
~1000 genetic markers
~40,000 gene-expression levels
3 continuous phenotypes (related to the severity of infection)
Part B: In-silico-network challenges
Based on data generated with SysGenSIM

8. DREAM 5 Systems Genetics challenges
DREAM 5 Systems Genetics challenges
Part A: Soybean challenges
Predictive modeling

9. DREAM 5 Systems Genetics challenges
DREAM 5 Systems Genetics challenges
Part B: In-silico-network challenges
Goal
Reverse-engineering Gene Networks from ‘in-silico data’
Data
Three networks of 1000 genes with ‘modular scale-free topology’ were generated, and a dynamical model was defined according to the network structures.
For each of the three networks, a population of RILs was generated with population sizes: n = 100 for network 1 (sub challenge B1), 300 for network 2 (sub challenge B2) and 1000 for network 3 (sub challenge B3).
Steady state gene-expression levels for all RILs were calculated after adjusting the Z parameters according to the corresponding genotype vector.

10.
To be continued Next week by Andrea Pinna with all the interesting technical details of SysGenSIM