Rule-based Modeling William S. Hlavacek Theoretical Division Los - PowerPoint PPT Presentation

Rule-based Modeling William S. Hlavacek Theoretical Division Los Alamos National Laboratory Slide 1

Outline The motivation for rule-based modeling 1. Basic concepts of rule-based modeling 2. An example model specification 3. Methods for simulating a model 4. Suggested exercise 5.

The need for predictive models of signal-transduction systems  These systems mediate cellular information processing and regulate cellular phenotypes  They are complex  Molecular changes that affect cell signaling cause/sustain disease (e.g., cancer)  Numerous drugs that target signaling proteins are currently in clinical trials Spectacular successes (e.g., imatinib treatment of CML) • But results are disappointing for many patients •  Many clinical trials are underway to test combinations of drugs (clinicaltrials.gov) There are too many combinations to consider all possibilities in trials •

Value added by modeling We can use models to organize information about a system with  precision Introduces greater rigor and discipline • We can determine the logical consequences of a model specification  Design principles can be elucidated (key for synthetic biology) • Certification (essential for personalized medicine) •

A signaling protein is typically composed of multiple components (subunits, domains, and/or linear motifs) that mediate interactions with other proteins TCR/CD3 Lck Lck-SH2 (1bhh) CD3E: 184 PNPDYEPIRKGQRDLYSGL 202 PRS: PxxDY ITAM: YxxL/I(x 6-8 )YxxL/I Kesti T et al. (2007) J. Immunol. 179:878-85.

There are many protein interaction domains The Pawson Lab (http://pawsonlab.mshri.on.ca/)

Some domains are multivalent and mediate oligomerization via domain-domain interactions b b a FADD b c a b a c FADD b b c a b a b a c Fas Fas c a c a c c b b b a b a FADD FADD b c a c a b c c b a b a FADD FADD c a b b c a c b a b a b II Fas Fas c b b a c a c a b a c c Fas FADD I b b c a c a b b b a b a c III FADD FADD c II b a b a c a c a Fas c c I FADD c a b c a II c III b b b a c b a b a Fas Fas Fas I b c a c a c a b b a c c III c FADD b a b b c a FADD b a b a c FADD FADD b c a c a c a b a c c c FADD c a c b b b a b a A hexamer of death domains Fas Fas c a c a c c b b b a b a FADD FADD b c a c a b a c c FADD c a b Weber and Vincenz (2001) FEBS Lett. c b a FADD c a c C.-T. Tung (Los Alamos) There are many possible protein complexes!

Domain-motif interactions are often controlled by post -translational modifications There are many possible protein phosphoforms! Schulze WX et al. (2005) Mol. Syst. Biol.

518 protein kinases (~2% of human genes) FGFR2 FGFR3 TrkC EphB2 TrkB FGFR1 FGFR4 TrkA FLT1/VEGFR1 EphB1 EphA5 KDR/VEGFR2 The Human Kinome MuSK ROR2 Fms/CSFR ROR1 EphB3 Ret Kit EphA3 DDR2 Mer DDR1 Tyro3/ EphA4 Axl FLT4 Sky PDGFR � EphA6 IGF1R IRR FLT3 PDGFR � InsR HER2/ErbB2 Yes EphB4 Met EGFR Ron Src MLK3 Ros ALK EphA7 MLK1 Tie2 Lyn LTK Tie1 HCK Fyn RYK HER4 EphA8 MLK4 CCK4/PTK7 MLK2 Lck Fgr Tnk1 Tyk2 Ack TKL Jak1 TK HER3 Jak2 EphA2 Jak3 BLK DLK Syk Zap70/SRK ANKRD3 SgK288 EphA1 LZK PYK2/FAK2 FAK Lmr1 Lmr2 ALK4 ITK C-Raf/Raf1 TGF � R1 FRK ZAK BRaf EphB6 TEC KSR KSR2 Srm RIPK2 TXK Brk Lmr3 ALK7 BTK IRAK3 IRAK1 ARaf LIMK1 BMPR1B EphA10 Etk/BMX LIMK2 TESK1 BMPR1A ILK TAK1 CTK RIPK3 TSK2 HH498 ALK1 CSK ALK2 Abl2/Arg IRAK2 ActR2 Fes STE Abl ActR2B Fer RIPK1 Jak3~b TGF � R2 LRRK2 MEKK2/MAP3K2 Jak2~b LRRK1 MISR2 MEKK3/MAP3K3 BMPR2 ASK/MAP3K5 Tyk2~b SuRTK106 IRAK4 MAP3K8 ANP � /NPR1 Jak1~b MAP3K7 ANP � /NPR2 KHS1 M S T 1 MOS KHS2 M S T 2 H P K 1 HSER SgK496 WNK1 G C K MEKK6/MAP3K6 WNK3 YSK1 DYRK2 MST4 PBK MAP3K4 DYRK3 STLK3 MINK/ZC3 GUCY2D WNK2 MST3 DYRK4 NRBP1 NRBP2 DYRK1A GUCY2F MEKK1/MAP3K1 OSR1 HGK/ZC1 TNIK/ZC2 DYRK1B WNK4 N R K / Z C 4 MLKL STRAD/STLK5 MYO3A PERK/PEK SgK307 S T L K 6 SLK M Y O 3 B PKR LOK SgK424 HIPK1 HIPK3 GCN2 TAO1 There are TAO2 SCYL3 SCYL1 Tpl2/COT HIPK2 TAO3 SCYL2 NIK PAK1 PRP4 PAK3 CLK4 HIPK4 HRI PAK4 CLK1 CLIK1 PAK2 IRE1 PAK5/PAK7 CLK2 CLIK1L MAP2K5 IRE2 TBCK PAK6 CLK3 MAP2K7 MEK1/MAP2K1 RNAseL GCN2~b MEK2/MAP2K2 CMGC TTK SgK071 MSSK1 KIS SRPK2 MYT1 SEK1/MAP2K4 MKK3/MKK6 phosphatases too! SRPK1 CK2 � 1 Wee1 SgK196 CK1 � MAK CK2 � 2 CDC7 Wee1B TTBK1 ICK PRPK CK1 � GSK3 � TTBK2 GSK3 � MOK CK1 � 1 CDKL3 CK1 � 2 CDKL2 PINK1 SgK493 VRK3 CK1 � 2 SgK269 SgK396 CDKL1 CDKL5 ERK7 SgK223 CK1 � 1 CDKL4 ERK4 Slob ERK3 SgK110 PIK3R4 CK1 � 3 NLK SgK069 Bub1 CK1 ERK5 SBK BubR1 IKK � E RK 1/p44MAPK Haspin VRK1 IKK � CDK7 VRK2 IKK � E RK 2/p42MAPK PLK4 PITSLRE TBK1/NAK p38 � MPSK1 JNK1 p38 � JNK2 TLK2 JNK3 CDK10 GAK PLK3 AAK1 TLK1 p38 � CDK8 CDK11 CAMKK1 ULK3 PLK1 PLK2 p38 � CDK4 CCRK BIKE CAMKK2 BARK1/GRK2 CDK6 BARK2/GRK3 ULK1 RHOK/GRK1 GRK5 Fused CHED PFTAIRE2 SgK494 ULK2 ULK4 GRK7 GRK4 CDK9 GRK6 PFTAIRE1 Nek6 RSKL1 PCTAIRE2 Nek7 Nek10 SgK495 RSKL2 PASK PDK1 RSK1/p90RSK Nek8 MSK1 CRK7 PCTAIRE1 PCTAIRE3 CDK5 Nek9 LKB1 MSK2 RSK2 RSK4 RSK3 Nek2 Chk1 p70S6K Akt2/PKB � AurA/Aur2 p70S6K � Akt1/PKB � cdc2/CDK1 Nek11 Akt3/PKB � CDK3 SGK1 CDK2 Nek4 AurB/Aur1 SGK3 AurC/Aur3 SGK2 Trb3 Pim1 Pim2 PKG2 PKN1/PRK1 LATS1 Nek3 PKG1 PKN2/PRK2 Pim3 LATS2 Trb2 Trio PKN3 NDR1 PKC � Nek5 Trad PRKY Atypical Protein Kinases Trb1 NDR2 PKC � Obscn~b PRKX YANK1 PKC � Nek1 SPEG~b MAST3 MASTL PKC � Obscn STK33 ADCK1 YANK2 PKC � PKC � YANK3 ADCK5 SPEG PKA � PKC � ADCK3 PKA � ABC1 MAST2 TTN PKA � ADCK 4 smMLCK ADCK2 TSSK4 ROCK1 PKC � HUNK ROCK2 PKC � ChaK1 Chk2/Rad53 skMLCK SSTK SNRK MAST4 MAST1 DMPK ChaK2 DRAK2 NIM1 CRIK Alpha AlphaK3 TSSK3 DRAK1 PKD2/PKC µ AGC TSSK1 EEF2K TSSK2 CASK MAPKAPK2 DMPK2 AlphaK 2 SgK085 DCAMKL3 AlphaK1 caMLCK PKD1 DAPK2 MAPKAPK5 DCAMKL1 DAPK3 MELK PKD3 / PKC � DAPK1 DCAMKL2 MRCK � Brd2 MRCK � VACAMKL Brd 3 MNK1 PhK � 1 Brd MNK2 Brd4 PhK � 2 PSKH1 BrdT CaMK II � MAPKAPK3 PSKH2 CaMK II � BRSK2 CaMK II � PDHK2 AMPK � 2 BRSK1 CaMK II � PDHK3 RSK4~b PDHK AMPK � 1 SNARK PDHK1 CAMK RSK1~b CaMK IV ARK5 PDHK4 MSK1~b MSK2~b BCKDK QSK RSK3~b RSK2~b ATM CaMK I � ATR PIKK mTOR/FRAP SIK QIK DNAPK CaMK I � SMG1 TRRAP MARK4 CaMK I � RIOK3 CaMK I � RIO MARK3 RIOK1 MARK1 RIOK2 MARK2 TIF � TIF1 TIF1 � Manning G et al. (2002) TIF1 � Science 298:1912-34.

Signaling proteins typically contain multiple phosphorylation sites (S/T/Y) > 50% are phosphorylated at 2 or more sites Phospho.ELM database v. 3.0 (http://phospho.elm.eu.org)

There are many different kinds of post-translational modifications of proteins Walsh CT et al. (2005) Angew. Chem. Int. Ed. Engl. 44:7342-72.

Priming – cooperative phosphorylation of neighboring kinase substrates is common Coba MP et al. (2009) Sci. Signal.

Distinct time courses of phosphorylation for different amino acid residues within the same protein Schulze WX et al. (2005) Mol. Syst. Biol. Olsen JV et al. (2006) Cell 127:635-48.

Combinatorial complexity – a serious problem for the conventional modeling approach Epidermal growth factor receptor (EGFR) 9 sites => 2 9 =512 phosphorylation states Each site has ≥ 1 binding partner => more than 3 9 =19,683 total states EGFR must form dimers to become active => more than 1.9x10 8 states

The textbook approach

Network (model) size tends to grow nonlinearly (exponentially) with the number of molecular interactions in a system when molecules are structured There are only three interactions. We can use a “rule” to model each of these interactions. Science’s STKE re6 (2006)

If you can write the model by hand, it may look like a mechanistic model, but it’s probably just a complicated fitting function A reaction scheme incorporated in many published models of EGFR signaling

Rule-based modeling solves the problem of combinatorial complexity Inside a Chemical Plant  Large numbers of molecules… • …of a few types • Conventional modeling works fine (a good idea since 1865) • Inside a Cell  Possibly small numbers of molecules… • …of many possible types • Rule-based modeling is designed to deal with this situation (new) •  ZAP-70

Outline The motivation for rule-based modeling 1. Basic concepts of rule-based modeling 2. An example model specification 3. Methods for simulating a model 4. Suggested exercise 5.

Rule-based modeling: basic concepts Graphs represent molecules, their component parts, and “internal states” Molecules, components, and states can be directly linked to annotation in databases Graph-rewriting rules represent molecular interactions A rule specifies the addition or removal of an edge to represent binding or unbinding, or the change of an internal state to represent, for example, post -translational modification of a protein at a particular site TCR(Y111~p)+ZAP70(SH2)<->TCR(Y111~p!1).ZAP70(SH2!1)