Logical modelling of cellular decision processes with GINsim C. - PowerPoint PPT Presentation

Logical modelling of cellular decision processes with GINsim C. Chaouiya, A. Naldi, L. Spinelli, P. Monteiro, D. Berenguier, L. Grieco, A. Mbodj,S. Collombet, A. Niarakis, L. Tichit, E. Remy & D. Thieffry JOBIM, Rennes, July 5h, 2012

Cell proliferation, differentiation or death... How are decisions taken?

Dynamical modelling Why ? • To gain rigorous, global, functional understanding of the (complex) underlying networks • To predict the behaviour of the system in novel situations • To design novel experiments How ? • Regulatory charts/maps/graphs (CellDesigner, Cytoscape) • Qualitative modelling: Boolean / multilevel discrete networks • Quantitative modelling: ODE, PDE, Stochastic equations

Boolean networks - Stuart Kauffman (1969) x t + 1 = B ( x t ) The Boolean vector x represents the state of the system Random connections, nodes with predefined degree Canalizing Boolean functions Focus on asymptotic behaviour Two types of attractors: stable states and (simple) cycles Deterministic behaviour (only one possible following state)

Kinetic logic - René Thomas (1973) X i ( image or logical function ) specifies whether gene i is currently transcribed X = B ( x ) x i ( logical variable ) denotes the presence (above a threshold of the functional product of gene i Asynchronous, potentially non deterministic behaviour! Gene i switched ON Gene i switched OFF 1 0 X i 1 x i 0 t Delay d OFF Delay d ON

Logical modelling of regulatory networks A  A graph describes the interactions [1] between genes or regulatory products [2] C  Discrete levels of expression associated to each regulatory component and interaction B Logical rules/parameters K A = 2 IFF (C=0) K B = 1 IFF (A=1) K C = 1 IFF (B=1) AND (C=0) K A = 0 otherwise K B = 0 otherwise K C = 0 otherwise C A B C=0 1 0 2 1 C C C 2 0 0 0 1 0 0 1 0 Decision trees

Logical modelling of regulatory networks A  A graph describes the interactions [1] between genes or regulatory products [2] C  Discrete levels of expression associated to each regulatory component and interaction B Logical rules/parameters K A = 2 IFF (C=0) K B = 1 IFF (A=1) K C = 1 IFF (B=1) AND (C=0) K A = 0 otherwise K B = 0 otherwise K C = 0 otherwise C A B 0 2 C=0 1 1 C 2 0 0 1 1 0 Decision diagrams

Logical state transition graphs A Regulatory graph + Logical rules => simulations / dynamical analysis [1] [2] C Asynchronous updating (R Thomas, L Glass) B ABC C ↓ C ↑ A ↑ State B ↓ transition B ↓ graph Stable state

Logical state transition graphs A [1] [2] Synchronous updating (S Kauffman) C B ABC + Logical rules A ↑ C ↑ Cycle State B ↓ C ↓ transition graph Cycle C ↓ A ↑ Stable state

GINsim ( G ene I nteraction N etworks sim ulation ) Aurélien NALDI Available at http://gin.univ-mrs.fr/GINsim Fabrice LOPEZ Duncan BERENGIER Claudine CHAOUIYA analysis toolbox core simulator State transition graph GINML parser user interface Regulatory graph graph simulation editor graph analysis Naldi et al (2009) BioSystems 97 : 134-9 Chaouiya et al (2012) Methods in Molecular Biology 804 : 463-79

Development of dynamical analysis tools  Decision diagrams • Identification of attractors [Naldi et al , 2007] • Analysis of regulatory circuits [Naldi et al , 2007] • Model reduction [Naldi et al , 2010] • Compression of state transition graphs [Berenguier et al , in prep]  Priority classes • Mixed a/synchronous simulations [Fauré et al , 2006]  Petri nets (standard or coloured)  Model checking • Verification of dynamical properties (temporal logic) [Sanchez et al , 2008]  Constraint programming • Model identification (regulatory interactions, thresholds, rules) [Corblin et al , 2001]

Coping with the exponential growth of logical state transition graphs  Model reduction  Attractor identification  Temporisation (e.g. priorities, delays, etc.)  Compaction of state transition graphs

Logical model of the Th network IL27_e IL21_e IL23_e TGFB_e APC IFNB_e IFNG_e IL6_e IL10_e IL12_e IL4_e IL15_e IL2_e proliferation CGC IL15RA IFNGR2 GP130 IL2RA IL12RB1 IL12RB2 IL4RA IL2RB IFNGR1 IL27RA IL6RA IL10RA IL10RB CD28 TCR IFNBR IFNGR IL27R TGFBR IL12R IL15R IL6R IL21R IL23R IL10R IL4R IL2R IFNBR => 1 IFF IFNR_e =1 NFAT STAT5 STAT4 STAT6 STAT1 STAT3 STAT1 => 1 IFF INFGR=1 OR IL7R=1 OR IFNB_e =1 IKB IL21 IL23 TGFB IL17 IL10 IL4 IL2 NFKB IFNG SMAD3 IRF1 RUNX3 TBET GATA3 RORGT FOXP3 13 input components, 52 internal components, 339 circuits => too large to perform simulations Naldi et al (2010) PLoS Comput Biol 6 : e1000912.

Reduced Th model Naldi et al (2011) 13 input components Theoretical Computer Science 412 : 2207-18 21 internal components

State transition graph (704 nodes) TH0 + (APC, IL4_e, IL6_e, IL12) ON Th1 Th2 2 reachable stable states Pathways? Crucial decisions?

Graph of strongly connected components Also 704 nodes!

Hierarchical state transition graph Tbet+ Gata3+ IFNg-, IL2-, IL10+, IL2-, IL4-, IL10+, IL21+, Tbet+ Gata3+ IL21+, IL23+, IL23+, STAT1+ STAT6+ Th2 Th1 2 reachable stable states Schemata On the fly STG compaction using 5 nodes! Tarjan algorithm + decision diagrams

Overview of the simulation results for ≠ micro-environments Absence of APC only stimulation Pro-Th1 Pro-Th2 IL2 & IFNg IL4 & IL6 or IL12 Pro-Treg Pro-Th17 IL2 & TGFb IL6 & TGFb or IL10 GATA3 Tbet Foxp3 ROR γ t Naldi et a l (2010) PLoS Comput Biol 6 : e1000912.

Main biological applications  Drosophila development • Segmentation genetic network [Sanchez et al , 2008] • Mesoderm specification (heart, muscle, with E Furlong) [Mbodj et al , in prep]  Heamatopoietic cell differentiation and activation • TCR signalling & T cell activation/differentiation (with V Soumelis) [Naldi et al , 2010] • Mast cell activation (with M Daëron & B Malissen) • Specification of haematopoietic lineages (with T Graf) [Collombet et al , in prep]  Cell proliferation (DIAMONDS, FP6) [Fauré et al , 2006 & 2009; Sahin et al , 2009]  Apoptosis (APO-SYS, FP7) [Calzone et al , 2010]  MAPK network [Grieco et al , in prep]