Network Inference, With an Application to Yeast Systems Biology Center for Genomic Sciences Cuernavaca, Mexico September 25, 2006 Reinhard Laubenbacher Virginia Bioinformatics Institute And Department of Mathematics Virginia Tech http://polymath.vbi.vt.edu
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Network Inference, With an Application to Yeast Systems Biology Center for Genomic Sciences Cuernavaca, Mexico September 25, 2006 Reinhard Laubenbacher.
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Network Inference, With an Application to Yeast Systems Biology
Center for Genomic SciencesCuernavaca, MexicoSeptember 25, 2006
Reinhard Laubenbacher
Virginia Bioinformatics Institute
And
Department of Mathematics
Virginia Tech
http://polymath.vbi.vt.edu
Contributors and Collaborators
Applied Discrete Mathematics Group
(http://polymath.vbi.vt.edu)
• Miguel Colòn-Velez
• Elena Dimitrova (now at Clemson U)
• Luis Garcia (now at Texas A&M)
• Abdul Jarrah
• John McGee (now at Radford U)
• Brandy Stigler (now at MBI)
• Paola Vera-Licona
Collaborators• Diogo Camacho (VBI)• Ana Martins (VBI)• Pedro Mendes (VBI)• Wei Shah (VBI)• Vladimir Shulaev (VBI)• Michael Stillman (Cornell)• Bernd Sturmfels (UC
Berkeley)
Funding: NIH, NSF, Commonwealth of VA
“All processes in organisms,
from the interaction of molecules to the complex
functions of the brain and other whole organs,
strictly obey […] physical laws.
“Where organisms differ from inanimate matter is
in the organization of their systems and especially
in the possession of coded information.”
E. Mayr, 1988
Genome
Molecularnetworks
Organism
Environment
Increasingcomplexity
A multiscale system
Discrete models
“[The] transcriptional control of a gene can be described by a discrete-valued function of several discrete-valued variables.”
“A regulatory network, consisting of many interacting genes and transcription factors, can be described as a collection of interrelated discrete functionsand depicted by a wiring diagram similar to the diagram of a digital logic circuit.”
Karp, 2002
Model Types
Ideker, Lauffenburger, Trends in Biotech 21, 2003
Biochemical Networks
Brazhnik, P., de la Fuente, A. and Mendes, P. Trends in Biotechnology 20, 2002
Gene space
Protein space
Metabolic space
M etabo lite 1 M etabo lite 2
P ro te in 1
P ro te in 2
P ro te in 3
P ro te in 4 C om p lex 3 :4
G ene 1
G ene 2
G ene 3
G ene 4
• Oxidative Stress is a general term used to describe the steady state level of oxidative damage in a cell, tissue, or organ, caused by the species with high oxidative potential.
Introduction to oxidative stress and CHP
+ X + oxidized X
Cumene hydroperoxide (CHP)
Cumyl alcohol (COH)
C CH3CH3
O
O
H
C CH3CH3
O
H
• Cumene hydroperoxide (CHP) is an organic peroxide, thus has high oxidative potential. CHP is very reactive and can easily oxidize molecules such as lipids, proteins and DNA.
• Oxidation by CHP
Courtesy of Wei Sha
Glutathione-glutaredoxin antioxidant defense system
Phase space: There are 4 components and 4 fixed point(s) Components Size Cycle Length 1 2200 1 2 890 1 3 10 1 4 25 1
TOTAL: 3125 = 55 nodes
Printing fixed point(s)... [ 0 1 2 1 0 ] lies in a component of size 25. [ 2 2 4 2 3 ] lies in a component of size 10. [ 4 4 2 2 3 ] lies in a component of size 890. [ 4 4 2 4 2 ] lies in a component of size 2200.
Summary
• To use “omics” data set to their full potential network inference methods are useful.
• Cellular processes are dynamical systems, so we need methods for the inference of dynamical systems models.