François Fages MPRI Bio-info 2005 Formal Biology of the Cell Locations, Transport and Signaling François Fages, Constraint Programming Group, INRIA Rocquencourt.

Slides:



Advertisements
Similar presentations
Cell Signaling A. Types of Cell Signaling
Advertisements

François Fages Les Houches, avril 2007 Formal Verification of Dynamical Models and Application to Cell Cycle Control François Fages, Sylvain Soliman Constraint.
François Fages MPRI Bio-info 2006 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraint Programming.
François Fages MPRI Bio-info 2007 Formal Biology of the Cell Protein structure prediction with constraint logic programming François Fages, Constraint.
François FagesLyon, Dec. 7th 2006 Biologie du système de signalisation cellulaire induit par la FSH ASC 2006, projet AgroBi INRIA Rocquencourt Thème “Systèmes.
François Fages MPRI Bio-info 2007 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraint Programming.
François Fages WCB Nantes 2006 On Using Temporal Logic with Constraints to express Biological Properties of Cell Processes François Fages, Constraint Programming.
Bioinformatics 3 V18 – Kinetic Motifs Mon, Jan 12, 2015.
François Fages MPRI Bio-info 2005 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraint Programming.
François Fages MPRI Bio-info 2007 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraint Programming.
François Fages MPRI Bio-info 2006 Formal Biology of the Cell Locations, Transport and Signaling François Fages, Constraint Programming Group, INRIA Rocquencourt.
François Fages MPRI Bio-info 2006 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraints Group, INRIA.
François Fages MPRI Bio-info 2007 Formal Biology of the Cell Inferring Reaction Rules from Temporal Properties François Fages, Constraint Programming Group,
Simulation of Prokaryotic Genetic Circuits Jonny Wells and Jimmy Bai.
François Fages MPRI Bio-info 2006 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraint Programming.
Modelling Cell Signalling and Pattern Formation Nick Monk Department of Computer Science Collaboration: Erik Plahte & Siren Veflingstad Agricultural University.
François Fages MPRI Bio-info 2005 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraint Programming.
Chapter 5 Ligand gated ion channels, intracellular receptors and phosphorylation cascades.
Endocrinology Introduction Lecture 3.
CELL CONNECTIONS & COMMUNICATION AP Biology Ch.6.7; Ch. 11.
Proteomics Proteomics is the study of protein structure and function. An organism’s proteome is its entire set of proteins. Proteomics is much more complicated.
Models and methods in systems biology Daniel Kluesing Algorithms in Biology Spring 2009.
Computer Information Processing Input Output Unorganized Data Organized Data Computing Computer Programming.
Digital Signal Processing with Biomolecular Reactions Hua Jiang, Aleksandra Kharam, Marc Riedel, and Keshab Parhi Electrical and Computer Engineering University.
Cell Biology Lecture 3. Function of Plasma Membrane Mechanical Support Cell Signaling Selective permeability Active transport Bulk Transport Metabolic.
4.A.3 Cell Specialization Interactions between external stimuli and regulated gene expression result in specialization of cells, tissues and organs.
Cell cycles and clocking mechanisms in systems biology ESE 680 – 003 : Systems Biology Spring 2007.
Boolean Here, we are focusing on the early steps of FSH-induced signalling: the FSH receptor transduction mechanisms. We have translated the model previously.
Introduction to Receptors Tim Bloom, Ph.D. Room 206 Maddox Hall
More regulating gene expression. Fig 16.1 Gene Expression is controlled at all of these steps: DNA packaging Transcription RNA processing and transport.
AP Biology Control of Eukaryotic Genes.
Presentation Schedule. Homework 8 Compare the tumor-immune model using Von Bertalanffy growth to the one presented in class using a qualitative analysis…
Metabolic pathway alteration, regulation and control (5) -- Simulation of metabolic network Xi Wang 02/07/2013 Spring 2013 BsysE 595 Biosystems Engineering.
Modeling and identification of biological networks Esa Pitkänen Seminar on Computational Systems Biology Department of Computer Science University.
Combined Experimental and Computational Modeling Studies at the Example of ErbB Family Birgit Schoeberl.
1. p53 Structure, Function and Therapeutic Applications Provider: Dr.Davood Nourabadi(PhD,medical physiology) mdphysiology.persianblog.ir.
Introduction to Chemical Kinetics and Computational Modeling Hana El-Samad Byers Hall (QB3), Rm 403D.
Modeling the Chemical Reactions Involved in Biological Digital Inverters Rick Corley Mentor: Geo Homsy.
G 1 and S Phases of the Cell Cycle SIGMA-ALDRICH.
Transport and Rate Phenomena in Biological Systems Redux.
Biology Chapter 8 Section 3. Key Ideas  How do cells use signal molecules?  How do cells receive signals?  How do cells respond to signaling?
François Fages MPRI Bio-info 2005 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraint Programming.
Chromatin Structure:  Tightly bound DNA less accessible for transcription  DNA methylation: methyl groups added to DNA; tightly packed;  transcription.
© 2011 Pearson Education, Inc. Lectures by Stephanie Scher Pandolfi BIOLOGICAL SCIENCE FOURTH EDITION SCOTT FREEMAN 17 Control of Gene Expression in Bacteria.
Please turn in the Unknown Solutions Lab Remember: We will vote on T-shirt designs on Monday.
François Fages MPRI Bio-info 2005 Formal Biology of the Cell Modeling, Computing and Reasoning with Constraints François Fages, Constraints Group, INRIA.
May 04, 2005Trento Seminar1 Process Algebras & Network Motifs section 3.
How is gene expression in eukaryotes accomplished ?
Cell Reproduction: Ch : Growth…What is it? Getting larger Making more Division/ mitosis (eukaryotes) Binary Fission (prokaryotes)-budding.
Date of download: 6/21/2016 Copyright © 2016 McGraw-Hill Education. All rights reserved. The hedgehog (Hh) signaling—a proliferative pathway especially.
BCB 570 Spring Signal Transduction Julie Dickerson Electrical and Computer Engineering.
Whole-cell models: combining genomics and dynamical modeling
Regulation of Gene Expression
The Plasma Membrane.
Cell Signaling (Lecture 1)
Regulation of Gene Expression
MicroRNAs: regulators of gene expression and cell differentiation
Relationship between Genotype and Phenotype
The Plasma Membrane.
Volume 96, Issue 5, Pages (March 2009)
Schedule-dependent interaction between anticancer treatments
EGF receptor signaling — a quantitative view
Volume 30, Issue 3, Pages (May 2008)
Cell Communication (Signaling) Part 3
Ingunn W. Jolma, Xiao Yu Ni, Ludger Rensing, Peter Ruoff 
Activated membrane signaling promotes survival in response to radiation. Activated membrane signaling promotes survival in response to radiation. Radiation.
Cell Communication (Signaling) Part 3
Relationship between Genotype and Phenotype
Regulation of p53 by MDM2. p53 and MDM2 form an autoregulatory feedback loop. p53 stimulates the expression of MDM2; MDM2, in turn, inhibits p53 activity.
PTEN and p53: Who will get the upper hand?
Presentation transcript:

François Fages MPRI Bio-info 2005 Formal Biology of the Cell Locations, Transport and Signaling François Fages, Constraint Programming Group, INRIA Rocquencourt

François Fages MPRI Bio-info 2005 Overview of the Lectures 1.Introduction. Formal molecules and reactions in BIOCHAM. 2.Formal biological properties in temporal logic. Symbolic model-checking. 3.Continuous dynamics. Kinetics models. 4.Computational models of the cell cycle control [L. Calzone]. 5.Mixed models of the cell cycle and the circadian cycle [L. Calzone]. 6.Machine learning reaction rules from temporal properties. 7.Learning kinetic parameter values. Constraint-based model checking. 8.Protein structure prediction with Constraint Logic Programming. 9.Locations, transport and signaling.

François Fages MPRI Bio-info 2005 Symbolic Locations in BIOCHAM Locations are symbolic notations used for representing mainly Cell compartments: nucleus, cytoplasm, membrane, … Tissues of cells: C1, C2, … Solution S == _ | O+S Object O == E | E::location Element E == name | E-E | E~{p1,…,pn} Declaring the set of possible locations for an element localize p53::[cytoplasm, nucleus]. defines all localized forms: p53, p53::cytoplasm, p53::nucleus

François Fages MPRI Bio-info 2005 Transport Rules A::L1 => A::L2 Cdk1~{p}-CycB::cytoplasm => Cdk1~{p}-CycB::nucleus. A~{p}::L1 => A::L2 Mdm-Mdm~{p}::cytoplasm => Mdm-Mdm::nucleus. localise Mdm-Mdm::[c,n]. localise Mdm-Mdm~{p}::c. volume_ratio (15,n),(1,c). meaning 15*Vn = 1*Vc (0.5*[Mdm-Mdm::n],15*[Mdm-Mdm~{p}::c]) for Mdm-Mdm::n Mdm-Mdm~{p}::c. shorthand for 15*Mdm-Mdm::n Mdm-Mdm~{p}::c.

François Fages MPRI Bio-info 2005 Volume Ratios for the Concentration Semantics A set of BIOCHAM reaction rules {e i for S i => S’ i | i=1,…,n} is interpreted in the concentration semantics by the system of ODEs: dx k /dt = Σ Xi=1 n r i (x k ) * e i − Σ Xj=1 n l j (x k ) * e j where r i (resp. l j ) is the stochiometric coefficient of x k in S’ i (resp. S i ) multiplied by the volume ratio of the location of x k.

François Fages MPRI Bio-info 2005 Example: DNA Repair Control by p53/mdm2 Vogelstein et al. 2000

François Fages MPRI Bio-info 2005 Observed p53/mdm2 Oscillations after Irradiation Damped oscillations after strong irradiation Delay and no oscillations after weak irradiation Lev Bar-Or et al. (2000)

François Fages MPRI Bio-info 2005 Single Cell Behaviors « Analogic » « Digital » From Lahav et al. (2004) Geva-Zatorsky et al. (2006)

François Fages MPRI Bio-info 2005 Effect of Ionizing Radiation (IR) on DNA Irradiation: 0.2*[IR] for IR => _. DNA damage: 0.18*[IR] for _ =[IR]=> damaged_dna. DNA repair: 0.017*([p53]+[p53-u]+[p53-u-u]) *[damaged_dna]/(1+[damaged_dna]) for damaged_dna => dna.

François Fages MPRI Bio-info 2005 Interaction and Influence Schemas Ciliberto et al Kaufman et al. 2006

François Fages MPRI Bio-info 2005 Synthesis and Degradation of p53 (0.055, *[p53]) for _ p53. P53 degradation is accelerated by Mdm2::n through ubiquitination 8.8 *[p53]*[Mdm-Mdm::n] for p53 =[Mdm-Mdm::n]=> p53-u. 2.5*[p53-u] for p53-u => p *[p53-u] for p53-u => _. 8.8*[p53-u]*[Mdm-Mdm::n] for p53-u =[Mdm-Mdm::n]=> p53-u-u. 2.5*[p53-u-u] for p53-u-u => p53-u *[p53-u-u] for p53-u-u => _.

François Fages MPRI Bio-info 2005 Synthesis and Degradation of Mdm2 in the Cytoplasm P53 promotes the transcription of Mdm /(1.2^3/(([p53]+[p53-u]+[p53-u-u])^3)) _ =[p53]=> Mdm-Mdm::c. 0.05*[Mdm-Mdm::c]/(0.01+[p53]+[p53-u]+[p53-u-u]) for Mdm-Mdm::c => Mdm-Mdm~{p}::c. 6*[Mdm-Mdm~{p}::c] for Mdm-Mdm~{p}::c => Mdm-Mdm::c. 0.01*[Mdm-Mdm~{p}::c] for Mdm-Mdm~{p}::c => _. 0.01*[Mdm-Mdm::c] for Mdm-Mdm::c => _.

François Fages MPRI Bio-info 2005 Transport and Degradation of mdm2 in the Nucleus (14*[Mdm-Mdm~{p}::c], 0.5*[Mdm-Mdm::n]) for Mdm-Mdm~{p}::c Mdm-Mdm::n. 0.01*[Mdm-Mdm::n] for Mdm-Mdm::n => _. DNA damage accelerates the degradation of Mdm2::n by auto-ubiquitination (ATM and ATR kinases) 0.01*[damaged_dna]*[Mdm-Mdm::n]/(0.2+[damaged_dna]) for Mdm-Mdm::n =[damaged_dna]=> _.

François Fages MPRI Bio-info 2005 Simulation of Irradiation and DNA Repair p53/mdm2 model of Ciliberto et al. 2005

François Fages MPRI Bio-info 2005 Cell Differentiation by Delta-Notch Signaling Xenopus embryonic skin [Ghosh, Tomlin 2001]

François Fages MPRI Bio-info 2005 Delta-Notch Lateral Signaling Delta and Notch proteins are transmembrane proteins Delta acts as a ligand and Notch as a receptor Notch production is triggered by high Delta levels in neigboring cells Delta production is triggered by low Notch concentration in the same cell Notch and Delta are degraded. At the steady state, a cell has either the Delta phenotype or the Notch

François Fages MPRI Bio-info 2005 Four Possible States Delta expressed and Notch inhibited Vd=0.2 Vn=0.5 D>Vd N<Vn Delta and Notch expressed D>Vd N>Vn Delta inhibited and Notch expressed D Vn Delta and Notch inhibited D<Vd N<Vn

François Fages MPRI Bio-info 2005 Delta-Notch on a Loop of 20 Cells localise D::[c1,c2,c3,c4,…,c20]. localise N::[c1,c2,c3,c4,…,c20]. Delta production and degradation for all cells if [N::c1]>0.5 then (-[D::c1]) else (1-[D::c1]) for_ => D::c1. Notch production and degradation for a one neighbor cell if [D::c2]<0.2 then (-[N::c1]) else (1-[N::c1]) for _ => N::c1. Notch production and degradation for a two neighbors cell if [D::c1]+[D::c3]<0.2 then (-[N::c2]) else (1-[N::c2]) for _ => N::c2.

François Fages MPRI Bio-info 2005 Delta-Notch on a Square Grid of 36 Cells Delta production and degradation for all cells if [N::c1]>0.5 then (-[D::c1]) else (1-[D::c1]) for_ => D::c1. Notch production and degradation for a four neighbors cell if [D::c21]+[D::c23]+[D::c12]+[D::c32]<0.2 then (-[N::c22]) else (1-[N::c22]) for _ => N::c22.

François Fages MPRI Bio-info 2005 Life = Auto-activation + Degradation