Supplement to: How Robust are Switches in Intracellular Signaling Cascades?
|
|
|
- Gwen Williamson
- 5 years ago
- Views:
Transcription
1 Supplement to: How Robust are Switches in Intracellular Signaling Cascades? Nils Blüthgen and Hanspeter Herzel Institute for Theoretical Biology, Invalidenstrasse 43, D Berlin, Germany (Dated: January 10, Journal of Theoretical Biology 225 (2003), ) Corrections There is a typo in the Methods section: Eqn. 3 should read k 1 = Vmax K m (1 + r) instead of k 1 = Vmax K m(1+r). Thanks to Keng-Hwee Chiam and Xinglai Ji who both pointed this out! I. MODELS FOR THE MAPK-CASCADE A. Parameters Concentrations taken from Bhalla & Iyengar (1999) (B/I), Huang & Ferrell (1996) (H/F). Total concentration [µm] (the corresponding parameter names are in brackets) protein B/I H/F Input-enzyme 0.1 (E 1,tot ) (PKC tot ) MAPKKK 0.2 (KKK tot ) (KKK tot ) MAPKK 0.18 (KK tot ) 1.2 (KK tot ) MAPK 0.36 (K tot ) 1.2 (K tot ) MAPKKK-Phosphatase (KKKS tot ) (PP2A tot ) MAPKK-Phosphatase (KKS tot ) (PP2A tot ) MAPK-Phosphatase (KS tot ) 0.12 (MKP1 tot ) V max -values taken from Bhalla & Iyengar (1999) (B/I), Huang & Ferrell (1996) (H/F).
2 V max [min 1 ] (the corresponding parameter names are in brackets) reaction B/I H/F MAPKKK MAPKKK* (V max,1 ) MAPKKK MAPKKK* (V max,2 ) MAPKKK* MAPKKK** (V max,3 ) MAPKKK* MAPKKK** (V max,4 ) MAPKK MAPKK* (V max,5 ) MAPKK* MAPKK** (V max,6 ) MAPKK MAPKK* (V max,7 ) MAPKK* MAPKK** (V max,8 ) MAPK MAPK* (V max,9 ) MAPK* MAPK** (V max,10 ) MAPK MAPK* (V max,11 ) MAPK* MAPK** (V max,12 )
3 k m values taken from Bhalla & Iyengar (1999) (B/I), Huang & Ferrell (1996) (H/F). k m [µm] (the corresponding parameter names are in brackets) reaction B/I H/F MAPKKK MAPKKK* (k m,1 ) MAPKKK MAPKKK* (k m,2 ) MAPKKK* MAPKKK** (k m,3 ) MAPKKK* MAPKKK** (k m,4 ) MAPKK MAPKK* (k m,5 ) MAPKK* MAPKK** (k m,6 ) MAPKK MAPKK* (k m,7 ) MAPKK* MAPKK** (k m,8 ) MAPK MAPK* (k m,9 ) MAPK* MAPK** (k m,10 ) MAPK MAPK* (k m,11 ) MAPK* MAPK** (k m,12 ) In the model according to (Bhalla & Iyengar, 1999), Raf is only active, if it binds to the complex of GTP and RAS, which assumed as a constant here. The concentration of GTP-RAS complex is set to GR tot = 0.2µM. The rate constants are k b = 24min 1 µm 1 for binding and k d = 0.5min 1 for dissotiation. In order to keep variable names short, MAPK is abbreviated as K. An S at the end indicates phosphotases, wherever it is neccessary, a underscore indicates a complex. Stars show the phosphorylation states. B. Model According to Huang and Ferrell The parameters k 1,n, k 1,n and k 2,n were choosen in order to fullfill the Michaelis-Menten constants (k m, n) and maximal velocities (V max,n ) values in Tables above. The ratio of k 1,n and k 2,n (r) was choosen as 4 as in Huang & Ferrell (1996). k 1,n = V max,n (1 + r) k m,n (1) k 1,n = V max,n r (2) k 2,n = V max,n. (3) d [KKK ] = k 2,1 [KKK E 1 ] + k 1,2 [KKK E 2 ] k 1,2 [KKK ] [E 2 ] (4) d [KKK E 1 ] = k 1,1 [KKK] [E 1 ] (k 1,1 + k 2,1 ) [KKK E 1 ] (5) d [KKK E 2 ] = k 1,2 [KKK ] [E 2 ] (k 1,2 + k 2,2 ) [KKK E 2 ] (6) [E 2 ] = [E 2,tot ] [KKK E 2 ] (7) [E 1 ] = [E 1,tot ] [KKK E 1 ] (8) [KKK] = [KKK tot ] [KKK ] [KKK E 1 ] [KKK E 2 ] (9) [KK KKK ] [KK KKK ] d [KK KKK ] = k 1,5 [KK] [KKK ] (k 1,5 + k 2,5 ) [KK KKK ] (10) d [KK ] = k 2,5 [KK KKK ] + k 2,8 [KK KKS] (11) 3
4 +k 1,7 [KK KKK ] + k 1,6 [KK KKS] k 1,7 [KK ] [KKK ] k 1,6 [KK ] [KKS] d [KK KKK ] = k 1,7 [KK ] [KKK ] (k 2,7 + k 1,7 ) [KK KKK ] (12) d [KK ] = k 2,7 [KK KKK ] + k 1,8 [KK KKS] (13) k 1,8 [KK ] [KKS] d [KK KKS] = k 1,8 [KK ] [KKS] (k 2,8 + k 1,8 ) [KK KKS] (14) d [KK KKS] = k 1,6 [KK ] [KKS] (k 2,6 + k 1,6 ) [KK KKS] (15) [KKS] = [KKS tot ] [KK KKS] [KK KKS] (16) [KK] = [KK tot ] [KK ] [KK KKK ] (17) [KK KKK ] [KK ] [KK KKS] [KK KKS] [K KK ] [K KK ] d [K KK ] = k 1,9 [K] [KK ] (k 2,9 + k 1,9 ) [K KK ] (18) d [K ] = k 2,9 [K KK ] + V 10 [K KS] + k 1,11 [K KK ] (19) +k 1,10 [K KS] k 1,11 [K ] [KK ] k 1,10 [K ] [KS] d [K KK ] = k 1,11 [K ] [KK ] (k 2,11 + k 1,11 ) [K KK ] (20) d [K ] = k 2,11 [K KK ] + k 1,12 [K KS] k 1,12 [K ] [KS] (21) d [K KS] = k 1,12 [K ] [KS] (k 1,12 + k 2,12 ) [K KS] (22) d [K KS] = k 1,10 [K ] [KS] (k 1,10 + k 2,10 ) [K KS] (23) [KS] = [KS tot ] [K KS] [K KS] (24) [K] = [K tot ] [K ] [K ] [K KK ] (25) [K KK ] [K KS] [K KS] C. Model According to Bhalla and Iyengar The parameters k 1,n, k 1,n and k 2,n were calculated like in the Huang/Ferrell model. d [PKC KKK] d [KKK ] d [GR KKK ] = k 1,1 [KKK] [PKC] (k 1,1 + k 2,1 ) [PKC KKK] (26) = k 2,1 [PKC KKK] + k 2,4 [PP2A KKK ] (27) +k 1,2 [K KKK ] + k 1,3 [PP2A KKK ] (k 1,3 [PP2A] + k 1,2 [K ]) [KKK ] k b [GR] [KKK ] + k d [GR KKK ] = k b [GR] [KKK ] k d [GR KKK ] (28) k 1,5 [KK] [GR KKK ] k 1,6 [KK ] [GR KKK ] + (k 1,5 + k 2,5 ) [GR KKK KK] + (k 1,6 + k 2,6 ) [GR KKK KK ] 4
5 d [K KKK ] d [KKK ] d [PP2A KKK ] d [PP2A KKK ] d [KK] d [GR KKK KK] d [PP2A KK ] d [KK ] d [GR KKK KK ] d [PP2A KK ] d [KK K] d [K ] d [KK K ] d [K ] = k 1,2 [KKK ] [K ] (29) (k 1,2 + k 2,2 ) [K KKK ] = k 2,2 [K KKK ] + k 1,4 [PP2A KKK ] (30) k 1,4 [KKK ] [PP2A] = k 1,4 [PP2A] [KKK ] (31) (k 1,4 + k 2,4 ) [PP2A KKK ] = k 1,3 [PP2A] [KKK ] (32) (k 1,3 + k 2,3 ) [PP2A KKK ] = k 1,5 [GR KKK ] [KK] (33) +k 1,5 [GR KKK KK] + k 2,7 [PP2A KK ] = k 1,5 [GR KKK ] [KK] (34) (k 1,5 + k 2,5 ) [GR KKK KK] = k 1,7 [PP2A] [KK ] (k 1,7 + k 2,7 ) [PP2A KK ] (35) = k 2,5 [GR KKK KK] + k 2,8 [PP2A KK ] (36) +k 1,6 [GR KKK ] [KK ] + k 1,7 [PP2A KK ] (k 1,6 [GR KKK ] + k 1,7 [PP2A]) [KK ] = k 1,6 [KK ] [GR KKK ] (37) (k 1,6 + k 2,6 ) [GR KKK KK ] = k 1,8 [KK ] [PP2A] (k 1,8 + k 2,8 ) [PP2A KK ] (38) = k 1,9 [K] [KK ] (k 1,9 + k 2,9 ) [KK K] (39) = k 2,9 [KK K] + k 2,12 [MKP1 K ] (40) +k 1,11 [MKP1 K ] + k 1,10 [KK K ] (k 1,10 [KK ] + k 1,11 [MKP1]) [K ] = k 1,10 [K ] [KK ] (k 1,10 + k 2,10 ) [KK K ] (41) = k 2,10 [KK K ] + k 1,12 [MKP1 K ] (42) k 1,12 [MKP1] [K ] k 1,2 [K ] [KKK ] + (k 1,2 + k 2,2 ) [K KKK ] d [MKP1 K ] = k 1,12 [MKP1] [K ] (k 1,12 + k 2,12 ) [MKP1 K ] (43) d [MKP1 K ] = k 1,11 [MKP1] [K ] (k 1,11 + k 2,11 ) [MKP1 K ] (44) [KK ] = [KK tot ] ([KK ] + [GR KKK KK] (45) + [KK] + [GR KKK KK ] + [PP2A KK ] + [PP2A KK ] + [KK K] + [KK K ]) [PP2A] = [PP2A tot ] ([PP2A KK ] (46) + [PP2A KK ] + [PP2A K ] + [PP2A K ]) [GR] = [GR tot ] ([GR KKK KK] (47) 5
6 + [GR KKK KK ] + [GR KKK ]) [MKP1] = [MKP1 tot ] [MKP1 K ] [MKP1 K ] (48) [K] = [K tot ] [KK K] [KK K ] (49) [MKP1 K ] [MKP1 K ] [K ] [K ] [K KKK ] [PKC] = [PKC tot ] [PKC KKK] (50) [KKK] = [KKK tot ] ([PKC KKK] + [KKK ] (51) + [K KKK ] + [KKK ] + [PP2A KKK ] + [PP2A KKK ] + [GR KKK ] + [GR KKK KK ] + [GR KKK KK]) II. MODEL FOR THE GOLBETER-KOSHLAND SWITCH Goldbeter & Koshland (1981) A. Parameters Parameter value total substrate concentration (A tot ) 1µM total phosphatase concentration (E 2,tot ) 0.01µM V max of kinase (V max,1 ) 1min 1 V max of phosphatase (V max,2 ) 1min 1 k m of kinase (k m,1 ) 0.1µM k m of phosphatase (k m,2 ) 0.1µM B. Equations d [A E 1 ] = k 1,1 [A] [E 1 ] (k 1,1 + k 2,1 ) [A E 1 ] (52) d [A ] = k 2,1 [A E 1 ] k 1,2 [A ] [E 2 ] + k 1,2 [A E 2 ] (53) d [A E 2 ] = k 1,2 [A ] [E 2 ] (k 1,2 + k 2,2 ) [A E 2 ] (54) [A] = [A tot ] [A E 1 ] [A E 2 ] [A ] (55) [E 1 ] = [E 1,tot ] [A E 1 ] (56) [E 2 ] = [E 2,tot ] [A E 2 ] (57) Bhalla, U. & Iyengar, R. (1999) Emergent properties of networks of biological signaling pathways. Science 283, Goldbeter, A. & Koshland, D. (1981) An amplified sensitivity arising from covalent modification in biological systems. Proc. Natl. Acad. Sci. USA 78, Huang, C. & Ferrell, J. (1996) Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc. Natl. Acad. Sci. USA 93,
Systems Biology Across Scales: A Personal View XIV. Intra-cellular systems IV: Signal-transduction and networks. Sitabhra Sinha IMSc Chennai
Systems Biology Across Scales: A Personal View XIV. Intra-cellular systems IV: Signal-transduction and networks Sitabhra Sinha IMSc Chennai Intra-cellular biochemical networks Metabolic networks Nodes:
Activation of a receptor. Assembly of the complex
Activation of a receptor ligand inactive, monomeric active, dimeric When activated by growth factor binding, the growth factor receptor tyrosine kinase phosphorylates the neighboring receptor. Assembly
Problem Set # 3
20.320 Problem Set # 3 October 1 st, 2010 Due on October 8 th, 2010 at 11:59am. No extensions, no electronic submissions. General Instructions: 1. You are expected to state all your assumptions and provide
arxiv:physics/ v3 [physics.bio-ph] 16 Sep 2003
![arxiv:physics/ v3 [physics.bio-ph] 16 Sep 2003](/thumbs/92/110902603.jpg "arxiv:physics/ v3 [physics.bio-ph] 16 Sep 2003")
Accepted for publication in Biophysical Chemistry Special Issue for Walter Kauzmann arxiv:physics/0207049v3 [physics.bio-ph] 16 Sep 2003 Thermodynamic and Kinetic Analysis of Sensitivity Amplification
Study of Tricyclic Cascade Networks using Dynamic Optimization
Study of Tricyclic Cascade Networks using Dynamic Optimization Systems Biology interdisciplinary field that focuses on the systematic study of complex interactions in biological systems Signal transduction
Bistability from double phosphorylation in signal transduction
Bistability from double phosphorylation in signal transduction Kinetic and structural requirements Fernando Ortega 1,2,3, José L. Garcés 1,2, Francesc Mas 1,2, Boris N. Kholodenko 4 and Marta Cascante
Bioinformatics Modelling dynamic behaviour: Modularisation
Bioinformatics Modelling dynamic behaviour: David Gilbert Bioinformatics Research Centre www.brc.dcs.gla.ac.uk Department of Computing Science, University of Glasgow Lecture outline Modelling Enzymatic
PKA PKC. Reactions: Kf and Kb are in units of um and sec.
Notes on output: Molecules: Only non-zero intial concentrations are shown. All other values are generated in the course of the simulation. A value of 1 for Buffering indicates that the molecular concentration
No oscillations in the Michaelis-Menten approximation of the dual futile cycle under a sequential and distributive mechanism
No oscillations in the Michaelis-Menten approximation of the dual futile cycle under a sequential and distributive mechanism Luna Bozeman 1 and Adriana Morales 2 1 Department of Mathematical Sciences Clemson
Chapter 4 Signaling Cascades: Consequences of Varying Substrate and Phosphatase Levels
Chapter 4 Signaling Cascades: Consequences of Varying Substrate and Phosphatase Levels Elisenda Feliu, Michael Knudsen, and Carsten Wiuf Abstract We study signaling cascades with an arbitrary number of
Systems biology 9 Signal Transduction
Humbol- Sommersemester 2 Systems biology 9 Signal Transuction Ea Klipp Humbol- Institut für Biologie Theoretische Biophysi Moeling of Signal Transuction Humbol- Before: Metabolismus - Mass transfer Now:
COMPUTER SIMULATION OF DIFFERENTIAL KINETICS OF MAPK ACTIVATION UPON EGF RECEPTOR OVEREXPRESSION
COMPUTER SIMULATION OF DIFFERENTIAL KINETICS OF MAPK ACTIVATION UPON EGF RECEPTOR OVEREXPRESSION I. Aksan 1, M. Sen 2, M. K. Araz 3, and M. L. Kurnaz 3 1 School of Biological Sciences, University of Manchester,
1 Periodic stimulations of the Incoherent Feedforward Loop network
1 Periodic stimulations of the Incoherent Feedforward Loop network In this Additional file, we give more details about the mathematical analysis of the periodic activation of the IFFL network by a train
Simulation Methods for Optimal Experimental Design in Systems Biology
Simulation Methods for Optimal Experimental Design in Systems Biology D. Faller Freiburg Center for Data Analysis and Modeling Eckerstr. 1, 79104 Freiburg, Germany U. Klingmüller Max-Planck-Institute for
A mechanistic study of evolvability using the mitogen-activated protein kinase cascade
EVOLUTION & DEVELOPMENT 5:3, 81 94 (003) A mechanistic study of evolvability using the mitogen-activated protein kinase cascade H. F. Nijhout,* A. M. Berg, and W. T. Gibson Department of Biology, Duke
State Machine Modeling of MAPK Signaling Pathways
State Machine Modeling of MAPK Signaling Pathways Youcef Derbal Ryerson University yderbal@ryerson.ca Abstract Mitogen activated protein kinase (MAPK) signaling pathways are frequently deregulated in human
Overview of Kinetics
Overview of Kinetics [P] t = ν = k[s] Velocity of reaction Conc. of reactant(s) Rate of reaction M/sec Rate constant sec -1, M -1 sec -1 1 st order reaction-rate depends on concentration of one reactant
Modeling of signaling networks
Modeling of signaling networks Susana R. Neves and Ravi Iyengar* Summary Biochemical networks, including those containing signaling pathways, display a wide range of regulatory properties. These include
The Goldbeter-Koshland switch in the first-order region and its response to dynamic disorder
The Goldbeter-Koshland switch in the first-order region and its response to dynamic disorder Jianhua Xing1, 2, 4, Jing Chen3 1. Chemistry, Materials, and Life Sciences Directorate, University of California
Quasi Steady-State Approximations in Intracellular Signal Transduction a Word of Caution
Quasi Steady-State Approximations in Intracellular Signal Transduction a Word of Caution Morten Gram Pedersen, Alberto M. Bersani, and Enrico Bersani. 1. Department of Mathematics, Technical University
Operating Regimes of Signaling Cycles: Statics, Dynamics, and Noise Filtering
Operating Regimes of Signaling Cycles: Statics, Dynamics, and Noise Filtering Carlos Gomez-Uribe, 1,2 George C. Verghese, 2 and Leonid A. Mirny 1 1 Harvard-MIT Division of Health Sciences and Technology,
56:198:582 Biological Networks Lecture 11
56:198:582 Biological Networks Lecture 11 Network Motifs in Signal Transduction Networks Signal transduction networks Signal transduction networks are composed of interactions between signaling proteins.
Chem Lecture 10 Signal Transduction
Chem 452 - Lecture 10 Signal Transduction 111202 Here we look at the movement of a signal from the outside of a cell to its inside, where it elicits changes within the cell. These changes are usually mediated
14 COMPUTATIONAL CELL BIOLOGY AND COMPLEXITY
14 COMPUTATIONAL CELL BIOLOGY AND COMPLEXITY Charles Coward, MD * Banu Onaral, PhD * Chapter Contents 14.1 Computational Biology 14.1.1 Computational Modeling of Cellular Processes 14.1.2 Modeling Control
BE 150 Problem Set #2 Issued: 16 Jan 2013 Due: 23 Jan 2013
M. Elowitz and R. M. Murray Winter 2013 CALIFORNIA INSTITUTE OF TECHNOLOGY Biology and Biological Engineering (BBE) BE 150 Problem Set #2 Issued: 16 Jan 2013 Due: 23 Jan 2013 1. (Shaping pulses; based
Enzyme Reactions. Lecture 13: Kinetics II Michaelis-Menten Kinetics. Margaret A. Daugherty Fall v = k 1 [A] E + S ES ES* EP E + P
![Enzyme Reactions. Lecture 13: Kinetics II Michaelis-Menten Kinetics. Margaret A. Daugherty Fall v = k 1 [A] E + S ES ES* EP E + P](/thumbs/76/74341799.jpg "Enzyme Reactions. Lecture 13: Kinetics II Michaelis-Menten Kinetics. Margaret A. Daugherty Fall v = k 1 [A] E + S ES ES* EP E + P")
Lecture 13: Kinetics II Michaelis-Menten Kinetics Margaret A. Daugherty Fall 2003 Enzyme Reactions E + S ES ES* EP E + P E = enzyme ES = enzyme-substrate complex ES* = enzyme/transition state complex EP
Michaelis-Menten Kinetics. Lecture 13: Kinetics II. Enzyme Reactions. Margaret A. Daugherty. Fall Substrates bind to the enzyme s active site
Lecture 13: Kinetics II Michaelis-Menten Kinetics Margaret A. Daugherty Fall 2003 Enzyme Reactions E + S ES ES* EP E + P E = enzyme ES = enzyme-substrate complex ES* = enzyme/transition state complex EP
Proceedings of the FOSBE 2007 Stuttgart, Germany, September 9-12, 2007
Proceedings of the FOSBE 2007 Stuttgart, Germany, September 9-12, 2007 A FEEDBACK APPROACH TO BIFURCATION ANALYSIS IN BIOCHEMICAL NETWORKS WITH MANY PARAMETERS Steffen Waldherr 1 and Frank Allgöwer Institute
Hybrid simulations of stochastic reaction-diffusion processes for modeling intracellular signaling pathways
Hybrid simulations of stochastic reaction-diffusion processes for modeling intracellular signaling pathways K.-H. Chiam,* Chee Meng Tan, Vipul Bhargava, and Gunaretnam Rajagopal Bioinformatics Institute,
The EGF Signaling Pathway! Introduction! Introduction! Chem Lecture 10 Signal Transduction & Sensory Systems Part 3. EGF promotes cell growth
Chem 452 - Lecture 10 Signal Transduction & Sensory Systems Part 3 Question of the Day: Who is the son of Sevenless? Introduction! Signal transduction involves the changing of a cell s metabolism or gene
Mechanism underlying the bio-effects of an electromagnetic field based on the Huang-Ferrell model
Mechanism underlying the bio-effects of an electromagnetic field based on the Huang-Ferrell model D.Y. Geng, G. Hu, L. Wang, N. Jia and F.X. Wang Province-Ministry Joint Key Laboratory of Electromagnetic
Supporting Information. Methods. Equations for four regimes
Supporting Information A Methods All analytical expressions were obtained starting from quation 3, the tqssa approximation of the cycle, the derivation of which is discussed in Appendix C. The full mass
Regulation and signaling. Overview. Control of gene expression. Cells need to regulate the amounts of different proteins they express, depending on
Regulation and signaling Overview Cells need to regulate the amounts of different proteins they express, depending on cell development (skin vs liver cell) cell stage environmental conditions (food, temperature,
Analyzing Pathways using SAT-based Approaches
Analyzing Pathways using SAT-based Approaches Ashish Tiwari, Carolyn Talcott, Merrill Knapp, Patrick Lincoln, and Keith Laderoute SRI International, Menlo Park, CA 94025 Abstract. A network of reactions
Richik N. Ghosh, Linnette Grove, and Oleg Lapets ASSAY and Drug Development Technologies 2004, 2:
1 3/1/2005 A Quantitative Cell-Based High-Content Screening Assay for the Epidermal Growth Factor Receptor-Specific Activation of Mitogen-Activated Protein Kinase Richik N. Ghosh, Linnette Grove, and Oleg
Network Dynamics and Cell Physiology
Network Dynamics and Cell Physiology John J. Tyson Department of Biological Sciences & Virginia Bioinformatics Institute Outline 1. The cell is an information processing system. 2. Information is processed
Systems Biology: From the Cell to the Brain
Current Trends in Science (Ed. N Mukunda), Bangalore: Indian Academy of Sciences (2009), pp 199-205 Systems Biology: From the Cell to the Brain SITABHRA SINHA*, T JESAN and NIVEDITA CHATTERJEE + * The
Using Probabilistic Model Checking in Systems Biology
Using Probabilistic Model Checking in Systems Biology Marta Kwiatkowska, Gethin Norman and David Parker Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford, OX1 3QD ABSTRACT Probabilistic
A Structured Approach Part IV. Model Checking in Systems and Synthetic Biology
A Structured Approach Part IV Model Checking in Systems and Synthetic Biology Robin Donaldson Bioinformatics Research Centre University of Glasgow, Glasgow, UK Model Checking - ISMB08 1 Outline Introduction
ENZYME KINETICS. Medical Biochemistry, Lecture 24
ENZYME KINETICS Medical Biochemistry, Lecture 24 Lecture 24, Outline Michaelis-Menten kinetics Interpretations and uses of the Michaelis- Menten equation Enzyme inhibitors: types and kinetics Enzyme Kinetics
EMPLOYING A MECHANISTIC MODEL FOR THE MAPK PATHWAY TO EXAMINE THE IMPACT OF CELLULAR ALL OR NONE BEHAVIOR ON OVERALL TISSUE RESPONSE
Dose-Response: An International Journal Volume 8 Issue 3 Article 8 9-2010 EMPLOYING A MECHANISTIC MODEL FOR THE MAPK PATHWAY TO EXAMINE THE IMPACT OF CELLULAR ALL OR NONE BEHAVIOR ON OVERALL TISSUE RESPONSE
Biomolecular Feedback Systems
Biomolecular Feedback Systems Domitilla Del Vecchio MIT Richard M. Murray Caltech Classroom Copy v.6c, July, 22 c California Institute of Technology All rights reserved. This manuscript is for review purposes
PROBABILISTIC SENSITIVITY ANALYSIS OF BIOCHEMICAL REACTION SYSTEMS. Hongxuan Zhang
PROBABILISTIC SENSITIVITY ANALYSIS OF BIOCHEMICAL REACTION SYSTEMS by Hongxuan Zhang A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor
Progress in modeling biological development as it bears on SBML
Progress in modeling biological development as it bears on SBML Eric Mjolsness UC Irvine representing the Computable Plant project www.computableplant.org Three relevant examples Meristem maintenance by
Signal Transduction Phosphorylation Protein kinases. Misfolding diseases. Protein Engineering Lysozyme variants
Signal Transduction Phosphorylation Protein kinases Misfolding diseases Protein Engineering Lysozyme variants Cells and Signals Regulation The cell must be able to respond to stimuli Cellular activities
The Effect of Site-to-Site Variability in Ultrasensitive Dose Responses
Noname manuscript No. (will be inserted by the editor) The Effect of Site-to-Site Variability in Ultrasensitive Dose Responses German A. Enciso Shane Ryerson Received: date / Accepted: date Abstract In
Lecture 19 (10/30/17) Enzyme Regulation
Reading: Ch5; 164, 166-169 Problems: none Remember Today at 6:30 in PHO-206 is the first MB lecture & quiz NEXT Reading: Ch5; 158-169, 162-166, 169-174 Lecture 19 (10/30/17) Problems: Ch5 (text); 3,7,8,10
Bahnson Biochemistry Cume, April 8, 2006 The Structural Biology of Signal Transduction
Name page 1 of 6 Bahnson Biochemistry Cume, April 8, 2006 The Structural Biology of Signal Transduction Part I. The ion Ca 2+ can function as a 2 nd messenger. Pick a specific signal transduction pathway
Biomolecular Feedback Systems
Biomolecular Feedback Systems Domitilla Del Vecchio MIT Richard M. Murray Caltech Version.b, September 4, 24 c 24 by Princeton University Press All rights reserved. This is the electronic edition of Biomolecular
CHAPTER 15 Metabolism: Basic Concepts and Design
CHAPTER 15 Metabolism: Basic Concepts and Design Chapter 15 An overview of Metabolism Metabolism is the sum of cellular reactions - Metabolism the entire network of chemical reactions carried out by living
Lecture 13: Data Analysis for the V versus [S] Experiment and Interpretation of the Michaelis-Menten Parameters
![Lecture 13: Data Analysis for the V versus [S] Experiment and Interpretation of the Michaelis-Menten Parameters](/thumbs/89/100978514.jpg "Lecture 13: Data Analysis for the V versus [S] Experiment and Interpretation of the Michaelis-Menten Parameters")
Biological Chemistry Laboratory Biology 3515/Chemistry 3515 Spring 2018 Lecture 13: Data Analysis for the V versus [S] Experiment and Interpretation of the Michaelis-Menten Parameters 20 February 2018
Basic Synthetic Biology circuits
Basic Synthetic Biology circuits Note: these practices were obtained from the Computer Modelling Practicals lecture by Vincent Rouilly and Geoff Baldwin at Imperial College s course of Introduction to
Compact Modeling of Allosteric Multisite Proteins: Application to a Cell Size Checkpoint
Compact Modeling of Allosteric Multisite Proteins: Application to a Cell Size Checkpoint Germán Enciso 1 *, Douglas R. Kellogg 2, Arturo Vargas 3 1 Department of Mathematics, Department of Developmental
Modeling Cellular Networks
06_4774 12/7/06 3:02 PM Page 151 CHAPTER 6 Modeling Cellular Networks Tae Jun Lee, Dennis Tu, Chee Meng Tan, and Lingchong You 6.1 Introduction Systems-level understanding of cellular dynamics is important
Prediction of Enzyme Kinetic Parameters Based on Statistical Learning
80 Genome Informatics 17(1): 80 87 (2006) Prediction of Enzyme Kinetic Parameters Based on Statistical Learning Simon Borger Wolfram Liebermeister Edda Klipp borger@molgen.mpg.de lieberme@molgen.mpg.de
Bioinformatics 3. V18 Kinetic Motifs. Fri, Jan 8, 2016
Bioinformatics 3 V18 Kinetic Motifs Fri, Jan 8, 2016 Modelling of Signalling Pathways Curr. Op. Cell Biol. 15 (2003) 221 1) How do the magnitudes of signal output and signal duration depend on the kinetic
Bioinformatics 3! V20 Kinetic Motifs" Mon, Jan 13, 2014"
Bioinformatics 3! V20 Kinetic Motifs" Mon, Jan 13, 2014" Modelling of Signalling Pathways" Curr. Op. Cell Biol. 15 (2003) 221" 1) How do the magnitudes of signal output and signal duration depend on the
Modeling Biological Systems in Stochastic Concurrent Constraint Programming
Modeling Biological Systems in Stochastic Concurrent Constraint Programming Luca Bortolussi Alberto Policriti Abstract We present an application of stochastic Concurrent Constraint Programming (sccp) for
Positive Feedback Promotes Oscillations in Negative Feedback Loops
Positive Feedback Promotes Oscillations in Negative Feedback Loops Bharath Ananthasubramaniam* Hanspeter Herzel Institute for Theoretical Biology Charité and Humboldt-Universität zu Berlin Berlin Germany
Stochastic modeling of intracellular signaling and regulation
Stochastic modeling of intracellular signaling and regulation Cover: Intracellular signaling can be regarded as messages in molecular bottles. Many different types of those molecular bottles are floating
Computational Biology 1
Computational Biology 1 Protein Function & nzyme inetics Guna Rajagopal, Bioinformatics Institute, guna@bii.a-star.edu.sg References : Molecular Biology of the Cell, 4 th d. Alberts et. al. Pg. 129 190
Macromolecular Crowding
Macromolecular Crowding Keng-Hwee Chiam Mathematical and Theoretical Biology Group Goodsell (1994) Macromolecular Crowding, Oct. 15, 2003 p.1/33 Outline What: introduction, definition Why: implications
Reasoning about the ERK signal transduction pathway using BioSigNet-RR
Reasoning about the ERK signal transduction pathway using BioSigNet-RR Carron Shankland a, Nam Tran b, Chitta Baral b, and Walter Kolch c a Department of Computing Science and Mathematics, University of
Problem Set 2. 1 Competitive and uncompetitive inhibition (12 points) Systems Biology (7.32/7.81J/8.591J)
Problem Set 2 1 Competitive and uncompetitive inhibition (12 points) a. Reversible enzyme inhibitors can bind enzymes reversibly, and slowing down or halting enzymatic reactions. If an inhibitor occupies
Time allowed: 2 hours Answer ALL questions in Section A, ALL PARTS of the question in Section B and ONE question from Section C.
UNIVERSITY OF EAST ANGLIA School of Biological Sciences Main Series UG Examination 2015-2016 FUNDAMENTALS OF CELL BIOLOGY AND BIOCHEMISTRY BIO-4004B Time allowed: 2 hours Answer ALL questions in Section
Steady State and (Bi-) Stability Evaluation of Simple Protease Signalling Networks
Steady State and (Bi-) Stability Evaluation of Simple Protease Signalling Networks Thomas Eißing a,, Steffen Waldherr a, Frank Allgöwer a, Peter Scheurich c, and Eric Bullinger a,b a Institute for Systems
COURSE NUMBER: EH 590R SECTION: 1 SEMESTER: Fall COURSE TITLE: Computational Systems Biology: Modeling Biological Responses
DEPARTMENT: Environmental Health COURSE NUMBER: EH 590R SECTION: 1 SEMESTER: Fall 2017 CREDIT HOURS: 2 COURSE TITLE: Computational Systems Biology: Modeling Biological Responses COURSE LOCATION: TBD PREREQUISITE:
Dynamic Stability of Signal Transduction Networks Depending on Downstream and Upstream Specificity of Protein Kinases
Dynamic Stability of Signal Transduction Networks Depending on Downstream and Upstream Specificity of Protein Kinases Bernd Binder and Reinhart Heinrich Theoretical Biophysics, Institute of Biology, Math.-Nat.
Deterministic modelling and stochastic simulation of biochemical pathways using MATLAB
Deterministic modelling and stochastic simulation of biochemical pathways using MATLAB M. Ullah, H. Schmidt, K.-H. Cho and O. Wolkenhauer Abstract: The analysis of complex biochemical networks is conducted
Rapid and sustained nuclear cytoplasmic ERK oscillations induced by epidermal growth factor
Molecular Systems Biology 5; Article number 332; doi:1.138/msb.29.9 Citation: Molecular Systems Biology 5:332 & 29 EMBO and Macmillan Publishers Limited All rights reserved 1744-4292/9 www.molecularsystemsbiology.com
Cell, Volume 153. Supplemental Information. The Ribosome as an Optimal Decoder: A Lesson in Molecular Recognition. Yonatan Savir and Tsvi Tlusty
ell, Volume 153 Supplemental Information The Ribosome as an Optimal Decoder: A Lesson in Molecular Recognition Yonatan Savir and Tsvi Tlusty 1 Extended Experimental Procedures Michaelis-Menten Kinetics
Learning Outcomes. k 1
Module 1DHS - Data Handling Skills Unit: Applied Maths Lecturer: Dr. Simon Hubbard (H13), Email: Simon.Hubbard@umist.ac.uk Title: Equilibria & Michaelis-Menten This lecture and problem class will introduce
Bioinformatics Modelling dynamic behaviour (Systems biology)
Bioinformatics Modelling dynamic behaviour (Systems biology) Xu Gu Bioinformatics Research Centre www.brc.dcs.gla.ac.uk Department of Computing Science, University of Glasgow Lecture outline Biochemical
Unit 3. Enzymes. Catalysis and enzyme kinetics.
Unit 3 Enzymes. Catalysis and enzyme kinetics. OUTLINE 3.1. Characteristics of biological catalysts. Coenzymes, cofactors, vitamins Enzyme nomenclature and classification 3.2. Enzyme catalysis. Transition
From Petri Nets to Differential Equations An Integrative Approach for Biochemical Network Analysis
From Petri Nets to Differential Equations An Integrative Approach for Biochemical Network Analysis David Gilbert drg@brc.dcs.gla.ac.uk Bioinformatics Research Centre, University of Glasgow and Monika Heiner
Biochemistry 462a - Enzyme Kinetics Reading - Chapter 8 Practice problems - Chapter 8: (not yet assigned); Enzymes extra problems
Biochemistry 462a - Enzyme Kinetics Reading - Chapter 8 Practice problems - Chapter 8: (not yet assigned); Enzymes extra problems Introduction Enzymes are Biological Catalysis A catalyst is a substance
Signal Transduction. Dr. Chaidir, Apt
Signal Transduction Dr. Chaidir, Apt Background Complex unicellular organisms existed on Earth for approximately 2.5 billion years before the first multicellular organisms appeared.this long period for
Simulation and Optimization Tools to Study Design Principles of Biological Networks. Bambang Senoaji Adiwijaya
Simulation and Optimization Tools to Study Design Principles of Biological Networks by Bambang Senoaji Adiwijaya B.S., University of Wisconsin, Madison, WI (1995) M.S., Carnegie Mellon University, Pittsburgh,
After lectures by. disappearance of reactants or appearance of. measure a reaction rate we monitor the. Reaction Rates (reaction velocities): To
Revised 3/21/2017 After lectures by Dr. Loren Williams (GeorgiaTech) Protein Folding: 1 st order reaction DNA annealing: 2 nd order reaction Reaction Rates (reaction velocities): To measure a reaction
Alliance for Cellular Signaling
Alliance for Cellular Signaling 40 laboratories investigating basic questions in cell signaling How complex is signal processing in cells? What is the structure and dynamics of the network? Can functional
Chemical kinetics and catalysis
Chemical kinetics and catalysis Outline Classification of chemical reactions Definition of chemical kinetics Rate of chemical reaction The law of chemical raction rate Collision theory of reactions, transition
A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker s yeast
A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker s yeast Jörg Schaber 1, Bente Kofahl 2, Axel Kowald 1 and Edda Klipp 1 1 Max Planck Institute
Network Dynamics and Cell Physiology. John J. Tyson Department of Biological Sciences & Virginia Bioinformatics Institute
Network Dynamics and Cell Physiology John J. Tyson Department of Biological Sciences & Virginia Bioinformatics Institute Signal Transduction Network Hanahan & Weinberg (2000) Gene Expression Signal-Response
Biochemistry. Lecture 8
Biochemistry Lecture 8 Why Enzymes? igher reaction rates Greater reaction specificity Milder reaction conditions Capacity for regulation C - - C N 2 - C N 2 - C - C Chorismate mutase - C - C - C Metabolites
Modeling Yeast Cell Polarization Induced by Pheromone Gradients
Journal of Statistical Physics ( C 27 ) DOI: 1.17/s1955-7-9285-1 Modeling Yeast Cell Polarization Induced by Pheromone Gradients Tau-Mu Yi, 1 Shanqin Chen, 2 Ching-Shan Chou 2 and Qing Nie 2 Received March
LIMIT CYCLE OSCILLATORS
MCB 137 EXCITABLE & OSCILLATORY SYSTEMS WINTER 2008 LIMIT CYCLE OSCILLATORS The Fitzhugh-Nagumo Equations The best example of an excitable phenomenon is the firing of a nerve: according to the Hodgkin
Enabling Technologies from the Biology Perspective
Enabling Technologies from the Biology Perspective H. Steven Wiley January 22nd, 2002 What is a Systems Approach in the Context of Biological Organisms? Looking at cells as integrated systems and not as
SECOND PUBLIC EXAMINATION. Honour School of Physics Part C: 4 Year Course. Honour School of Physics and Philosophy Part C C7: BIOLOGICAL PHYSICS
2757 SECOND PUBLIC EXAMINATION Honour School of Physics Part C: 4 Year Course Honour School of Physics and Philosophy Part C C7: BIOLOGICAL PHYSICS TRINITY TERM 2011 Monday, 27 June, 9.30 am 12.30 pm Answer
Glasgow eprints Service
Kolch, W. and Calder, M. and Gilbert, d. (2005) When kinases meet mathematics: the systems biology of MAPK signalling. FEBS Letters 579(8):pp. 1891-1895. http://eprints.gla.ac.uk/2871/ Glasgow eprints
Ch 4: Cellular Metabolism, Part 1
Developed by John Gallagher, MS, DVM Ch 4: Cellular Metabolism, Part 1 Energy as it relates to Biology Energy for synthesis and movement Energy transformation Enzymes and how they speed reactions Metabolism
Chemistry 112 Chemical Kinetics. Kinetics of Simple Enzymatic Reactions: The Case of Competitive Inhibition
Chemistry Chemical Kinetics Kinetics of Simple Enzymatic Reactions: The Case of Competitive Inhibition Introduction: In the following, we will develop the equations describing the kinetics of a single
The MEK/ERK cascade: From signaling specificity to diverse functions
Biochimica et Biophysica Acta 1773 (2007) 1213 1226 www.elsevier.com/locate/bbamcr Review The MEK/ERK cascade: From signaling specificity to diverse functions Yoav D. Shaul, Rony Seger Department of Biological
Advances in high-throughput genomics and proteomics analysis
Untangling the wires: A strategy to trace functional interactions in signaling and gene networks Boris N. Kholodenko*, Anatoly Kiyatkin*, Frank J. Bruggeman, Eduardo Sontag, Hans V. Westerhoff, and Jan
Introduction. Dagmar Iber Jörg Stelling. CSB Deterministic, SS 2015, 1.
Introduction Dagmar Iber Jörg Stelling joerg.stelling@bsse.ethz.ch CSB Deterministic, SS 2015, 1 Origins of Systems Biology On this assumption of the passage of blood, made as a basis for argument, and
Computing Algebraic Functions with Biochemical Reaction Networks
Computing Algebraic Functions with Biochemical Reaction Networks H. J. Buisman** Technische Universiteit Eindhoven H. M. M. ten Eikelder** Technische Universiteit Eindhoven P. A. J. Hilbers** Technische
Pathway Logic: Helping Biologists Understand and Organize Pathway Information
Pathway Logic: Helping Biologists Understand and Organize Pathway Information M. Knapp, L. Briesemeister, S. Eker, P. Lincoln, I. Mason, A. Poggio, C. Talcott, and K. Laderoute Find out more about Pathway
cascade - mapk rule-based)
cacade - mapk rule-baed) Thurday, 5 November 2009 !tline - tart with a baic motive of reverible covalent modification - obtain a rule-baed verion of the Huang-Ferell model of a protein activation cacade
A compartmental model of the camp/pka/mapk pathway in Bio-PEPA
A compartmental model of the camp/pka/mapk pathway in Bio-PEPA Federica Ciocchetta The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Trento, Italy ciocchetta@cosbi.eu
Semiotic modelling of biological processes: Semiotic processes in the immune system João Queiroz a,b,c & Charbel El-Hani b,c,d
Semiotic modelling of biological processes: Semiotic processes in the immune system João Queiroz a,b,c & Charbel El-Hani b,c,d a. Institute of Arts and Design, Federal University of Juiz de Fora, Brazil.
Robustification as a Tool in Modeling Biochemical Reaction Networks
Proceedings of the 9th International Symposium on Dynamics and Control of Process Systems (DYCOPS 010), Leuven, Belgium, July 5-7, 010 Mayuresh Kothare, Moses Tade, Alain Vande Wouwer, Ilse Smets (Eds.)