FYTN05/TEK267 Chemical Forces and Self Assembly

Size: px
Start display at page:

Download "FYTN05/TEK267 Chemical Forces and Self Assembly"

Transcription

1 FYTN05/TEK267 Chemical Forces and Self Assembly FYTN05/TEK267 Chemical Forces and Self Assembly 1

2 Michaelis-Menten (10.3,10.4) M-M formalism can be used in many contexts, e.g. gene regulation, protein - protein interaction etc. In many cases reactions do not occur spontaneously The cells produce enzymes which, act as catalysts for reactions During reactions the enzymes do not get used up S + E k 1 P + E This is unrealistic because of obvious limitations e.g. volume of the cell FYTN05/TEK267 Chemical Forces and Self Assembly 2

3 Michaelis-Menten (10.3,10.4) We consider M-M rule for describing a simple enzymatic reaction We employ transition state theory (see notes) S + E k 1 k 2 SE k 3 P + E FYTN05/TEK267 Chemical Forces and Self Assembly 3

4 Michaelis-Menten (10.3,10.4) We assume SE to be in qwasi-equilibrium We assume a fixed amount of enzyme C Etotal = C E + C SE The goal is to describe P production by C S S + E k 1 k 2 SE k 3 P + E dc S dc E dc SE dc P = k 1 C S C E + k 2 C SE = k 1 C S C E + (k 2 + k 3 )C SE = k 1 C S C E (k 2 + k 3 )C SE = k 3 C SE FYTN05/TEK267 Chemical Forces and Self Assembly 4

5 Michaelis-Menten S + E k 1 k 2 SE k 3 P + E dc SE = k 1 C S C E (k 2 + k 3 )C SE = 0 (1) C Etotal = C E + C SE (2) from(1), (2) = C SE = k 1 C S C Etotal k 2 + k 3 + k 1 C S (3) dc P = k 3 C SE (4) from(3), (4) = dc P from(5), (6) dc P = k 3C S C Etotal k 2 +k 3 k 1 + C S (5) V max = k 3 C Etotal, K = k 2 + k 3 k 1 (6) = V maxc S K + C S (7) (8) FYTN05/TEK267 Chemical Forces and Self Assembly 5

6 Michaelis-Menten (10.3,10.4) d[p] = V max[r] K + [R] FYTN05/TEK267 Chemical Forces and Self Assembly 6

7 Michaelis-Menten - Example Gene Regulation TF + DNA DNA b X + DNA b Probability of TF bound: P b = Probability of TF unbound: P u = TF K + TF K K + TF Activator If transcription takes place when TF is bound d[x ] = V P b = V [TF ] K + [TF ] Repressor If transcription takes place when TF is unbound d[x ] = V P u = VK K + [TF ] FYTN05/TEK267 Chemical Forces and Self Assembly 7

8 Michaelis-Menten - Example Gene Regulation Full Equation for activating [X ] d[x ] where τ TF is the TF half-life = V [TF ] K+[TF ] [TF ] τ TF FYTN05/TEK267 Chemical Forces and Self Assembly 8

9 Induced Pluripotent Stem (ips) cells FYTN05/TEK267 Chemical Forces and Self Assembly 9

10 MEF The Framework OCT4 NANOG MEF σ1 σ3 σ2 ES ips β 1 u h m μ1 Tet1 β 2 μ 2 OCT4 NANOG OCT4 NANOG Fraction of unmethylated CpG sites Tet1 Probability density ips MEF4 7 MEF ips MEF4 7 Probability density MEF ips MEF4 7 MEF NANOG N & T TET1 ES ES ips MEF4 7 & Methylation Methylation Methylation Methylation OCT4 FYTN05/TEK267 Chemical Forces and Self Assembly 10

11 Simplified Gene Regulatory Network Topology Young R.A. Cell(2011) Costa et al. Nature(2014) Koh et al. Cell Stem Cell(2011) FYTN05/TEK267 Chemical Forces and Self Assembly 11

12 Fast Complex Formation [N free ] + [T free ] [N T ] K d = [N free] [T free ] [N T ] [N total ] = [N free ] + [N T ] [T total ] = [T free ] + [N T ] [N T ] = K d + [N total ] + [T total ] 2 ( ) 2 K d + [N total ] + [T total ] [N total] [T total] 2 FYTN05/TEK267 Chemical Forces and Self Assembly 12

13 Slow Gene Regulation [N total ] t [O total ] t [T total ] t [O total ] = N over + LIF + p N K O [N total ] 1 + [O total] K O = [O total ] ( [N T ] ) n K O over + LIF + p O O K 1 + [O NT ( total] [N T ] ) [O total ] n 1 + K O K NT [O total ] ( [N T ] = T over + p T K O 1 + [O total] K O 1 + ) n K NT ( [N T ] ) n K NT [T total ] FYTN05/TEK267 Chemical Forces and Self Assembly 13

14 Reprogramming Simulation Results 1 OCT4 NANOG TET Expression Level Oct4 ON Oct4 OFF Nanog ON Nanog OFF Tet1 ON Tet1 OFF FYTN05/TEK267 Chemical Forces and Self Assembly 14

15 Expression Level ] pre-ips and Differentiation Simulation Results A 1 TET1 OCT4 NANOG B 1 TET1 OCT4 NANOG Expression Level Expression Level Over-expression = 0.1 Over-expression = 0.2 Over-expression=0.3 C Expression Level Expression Level Oct4 ON LIF Oct4 OFF Oct4+ Nanog ON LIF Withdrawal Oct4+ OFF Nanog TET1 OCT4 NANOG 0 Oc4 ON Oct4 OFF Nanog ON Nanog OFF Tet1 ON Tet1 OFF FYTN05/TEK267 Chemical Forces and Self Assembly 15

16 Shea-Akers Formalism - Multiple TF Depending on presence or absence of TF and/or RNAp the operator can be in various states denoted s. Each state has a statistical weight Z(s) depending on binding rates. The partition sum is the sum of all weights: Z = s Z(s) After normalization leading to the weight of the state with nothing bound to be 1 i.e. Z 0 = 1 the expression of states become Z(s) = e G(s) k b T [T i ] [T i ]-concentration of bound regulators, e G(s) k b T - parameter for binding affinity, related to loss of free energy at binding. i FYTN05/TEK267 Chemical Forces and Self Assembly 16

17 The bound fraction is: P T = α Z(bound) Z There are 5 possible states of the system thus partition sum is: all k are dissociation constants. Z = 1 + p k p + A k A + Ap k Ap + R k R P T = α p k p + Ap k Ap 1 + p k p + A k A + Ap k Ap + R k R FYTN05/TEK267 Chemical Forces and Self Assembly 17

18 The Core Gene Regulatory Network for ES cells LIF BMP4 FYTN05/TEK267 Chemical Forces and Self Assembly 18

19 LIF BMP4 Deterministic approach. Shea-Ackers equation d[n] d[os] d[fgf ] d[g] = k 0 [OS](c 0 + c 1 [N] 2 + k 0 [OS] + c 2 LIF ) (1 + (k 0 [OS](c 1 [N] 2 + k 0 [OS] + c 2 LIF + c 3 [FGF ] 2 )) + c 4 [OS][G] 2 ) γ[n], = α + = = (e 0 + e 1 [OS]) (1 + e 1 [OS] + e 2 [G] 2 γ[os], (9) ) (a 0 + a 1 [OS]) γ[fgf ], (1 + a 1 [OS] + a 2 I 3 ) (b 0 + b 1 [G] 2 + b 3 [OS]) (1 + b 1 [G] 2 + b 2 [N] 2 + b 3 [OS]) γ[g], FYTN05/TEK267 Chemical Forces and Self Assembly 19

20 LIF BMP4 Deterministic approach FYTN05/TEK267 Chemical Forces and Self Assembly 20

21 LIF BMP4 Stochastic Simulation Results -Time Series 150 OCT4 SOX2 NANOG LIF+BMP4 150 OCT4 SOX2 NANOG 2i Concentration Time x Time x 10 4 FYTN05/TEK267 Chemical Forces and Self Assembly 21

22 Stochastic Simulation Results - Distributions Nanog Oct4 Sox2 LIF+BMP Nanog Oct4 Sox2 2i/3i Density Concentration Concentration FYTN05/TEK267 Chemical Forces and Self Assembly 22

23 ES data Wray et al. FYTN05/TEK267 Chemical Forces and Self Assembly 23

24 THANK YOU! FYTN05/TEK267 Chemical Forces and Self Assembly 24

System Biology - Deterministic & Stochastic Dynamical Systems

System Biology - Deterministic & Stochastic Dynamical Systems System Biology - Deterministic & Stochastic Dynamical Systems System Biology - Deterministic & Stochastic Dynamical Systems 1 The Cell System Biology - Deterministic & Stochastic Dynamical Systems 2 The

More information

FYTN05/TEK267 Chemical Forces and Self Assembly

FYTN05/TEK267 Chemical Forces and Self Assembly FYTN5/TEK267 Chemical Forces and Self Assembly CBB - victor.olariu@thep.lu.se CBB - victor.olariu@thep.lu.se FYTN5/TEK267 Chemical Forces and Self Assembly 1 Introduction - Chapter 8 Reading material:

More information

After lectures by. disappearance of reactants or appearance of. measure a reaction rate we monitor the. Reaction Rates (reaction velocities): To

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

More information

Simulation of Chemical Reactions

Simulation of Chemical Reactions Simulation of Chemical Reactions Cameron Finucane & Alejandro Ribeiro Dept. of Electrical and Systems Engineering University of Pennsylvania aribeiro@seas.upenn.edu http://www.seas.upenn.edu/users/~aribeiro/

More information

2 Dilution of Proteins Due to Cell Growth

2 Dilution of Proteins Due to Cell Growth Problem Set 1 1 Transcription and Translation Consider the following set of reactions describing the process of maing a protein out of a gene: G β g G + M M α m M β m M + X X + S 1+ 1 X S 2+ X S X S 2

More information

BME 5742 Biosystems Modeling and Control

BME 5742 Biosystems Modeling and Control BME 5742 Biosystems Modeling and Control Lecture 24 Unregulated Gene Expression Model Dr. Zvi Roth (FAU) 1 The genetic material inside a cell, encoded in its DNA, governs the response of a cell to various

More information

Regulation of metabolism

Regulation of metabolism Regulation of metabolism So far in this course we have assumed that the metabolic system is in steady state For the rest of the course, we will abandon this assumption, and look at techniques for analyzing

More information

CS-E5880 Modeling biological networks Gene regulatory networks

CS-E5880 Modeling biological networks Gene regulatory networks CS-E5880 Modeling biological networks Gene regulatory networks Jukka Intosalmi (based on slides by Harri Lähdesmäki) Department of Computer Science Aalto University January 12, 2018 Outline Modeling gene

More information

Lecture 7: Simple genetic circuits I

Lecture 7: Simple genetic circuits I Lecture 7: Simple genetic circuits I Paul C Bressloff (Fall 2018) 7.1 Transcription and translation In Fig. 20 we show the two main stages in the expression of a single gene according to the central dogma.

More information

Basic Synthetic Biology circuits

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

More information

URL: <

URL:   < Citation: ngelova, Maia and en Halim, sma () Dynamic model of gene regulation for the lac operon. Journal of Physics: Conference Series, 86 (). ISSN 7-696 Published by: IOP Publishing URL: http://dx.doi.org/.88/7-696/86//7

More information

Program for the rest of the course

Program for the rest of the course Program for the rest of the course 16.4 Enzyme kinetics 17.4 Metabolic Control Analysis 19.4. Exercise session 5 23.4. Metabolic Control Analysis, cont. 24.4 Recap 27.4 Exercise session 6 etabolic Modelling

More information

Chapter 15 Active Reading Guide Regulation of Gene Expression

Chapter 15 Active Reading Guide Regulation of Gene Expression Name: AP Biology Mr. Croft Chapter 15 Active Reading Guide Regulation of Gene Expression The overview for Chapter 15 introduces the idea that while all cells of an organism have all genes in the genome,

More information

Biology. Biology. Slide 1 of 26. End Show. Copyright Pearson Prentice Hall

Biology. Biology. Slide 1 of 26. End Show. Copyright Pearson Prentice Hall Biology Biology 1 of 26 Fruit fly chromosome 12-5 Gene Regulation Mouse chromosomes Fruit fly embryo Mouse embryo Adult fruit fly Adult mouse 2 of 26 Gene Regulation: An Example Gene Regulation: An Example

More information

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) 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

More information

2. Mathematical descriptions. (i) the master equation (ii) Langevin theory. 3. Single cell measurements

2. Mathematical descriptions. (i) the master equation (ii) Langevin theory. 3. Single cell measurements 1. Why stochastic?. Mathematical descriptions (i) the master equation (ii) Langevin theory 3. Single cell measurements 4. Consequences Any chemical reaction is stochastic. k P d φ dp dt = k d P deterministic

More information

Grundlagen der Systembiologie und der Modellierung epigenetischer Prozesse

Grundlagen der Systembiologie und der Modellierung epigenetischer Prozesse Grundlagen der Systembiologie und der Modellierung epigenetischer Prozesse Sonja J. Prohaska Bioinformatics Group Institute of Computer Science University of Leipzig October 25, 2010 Genome-scale in silico

More information

12-5 Gene Regulation

12-5 Gene Regulation 12-5 Gene Regulation Fruit fly chromosome 12-5 Gene Regulation Mouse chromosomes Fruit fly embryo Mouse embryo Adult fruit fly Adult mouse 1 of 26 12-5 Gene Regulation Gene Regulation: An Example Gene

More information

3.B.1 Gene Regulation. Gene regulation results in differential gene expression, leading to cell specialization.

3.B.1 Gene Regulation. Gene regulation results in differential gene expression, leading to cell specialization. 3.B.1 Gene Regulation Gene regulation results in differential gene expression, leading to cell specialization. We will focus on gene regulation in prokaryotes first. Gene regulation accounts for some of

More information

ENZYME KINETICS. What happens to S, P, E, ES?

ENZYME KINETICS. What happens to S, P, E, ES? ENZYME KINETICS Go to lecture notes and/or supplementary handouts for the following: 1 Basic observations in enzyme inetics 2 Michaelis-Menten treatment of enzyme inetics 3 Briggs-Haldane treatment of

More information

Final Chem 4511/6501 Spring 2011 May 5, 2011 b Name

Final Chem 4511/6501 Spring 2011 May 5, 2011 b Name Key 1) [10 points] In RNA, G commonly forms a wobble pair with U. a) Draw a G-U wobble base pair, include riboses and 5 phosphates. b) Label the major groove and the minor groove. c) Label the atoms of

More information

Biomolecular Feedback Systems

Biomolecular Feedback Systems Biomolecular Feedback Systems Domitilla Del Vecchio MIT Richard M. Murray Caltech Version 1.0b, September 14, 2014 c 2014 by Princeton University Press All rights reserved. This is the electronic edition

More information

REGULATION OF GENE EXPRESSION. Bacterial Genetics Lac and Trp Operon

REGULATION OF GENE EXPRESSION. Bacterial Genetics Lac and Trp Operon REGULATION OF GENE EXPRESSION Bacterial Genetics Lac and Trp Operon Levels of Metabolic Control The amount of cellular products can be controlled by regulating: Enzyme activity: alters protein function

More information

56:198:582 Biological Networks Lecture 8

56:198:582 Biological Networks Lecture 8 56:198:582 Biological Networks Lecture 8 Course organization Two complementary approaches to modeling and understanding biological networks Constraint-based modeling (Palsson) System-wide Metabolism Steady-state

More information

Interactions between proteins, DNA and RNA. The energy, length and time coordinate system to find your way in the cell

Interactions between proteins, DNA and RNA. The energy, length and time coordinate system to find your way in the cell Interactions between proteins, DNA and RNA The energy, length and time coordinate system to find your way in the cell Scanning/atomic force microscope (SFM/AFM) mirror laser beam photo diode fluid cell

More information

Control of Gene Expression in Prokaryotes

Control of Gene Expression in Prokaryotes Why? Control of Expression in Prokaryotes How do prokaryotes use operons to control gene expression? Houses usually have a light source in every room, but it would be a waste of energy to leave every light

More information

Warm-Up. Explain how a secondary messenger is activated, and how this affects gene expression. (LO 3.22)

Warm-Up. Explain how a secondary messenger is activated, and how this affects gene expression. (LO 3.22) Warm-Up Explain how a secondary messenger is activated, and how this affects gene expression. (LO 3.22) Yesterday s Picture The first cell on Earth (approx. 3.5 billion years ago) was simple and prokaryotic,

More information

Oscillations in Michaelis-Menten Systems

Oscillations in Michaelis-Menten Systems Oscillations in Michaelis-Menten Systems Hwai-Ray Tung Brown University July 18, 2017 Hwai-Ray Tung (Brown University) Oscillations in Michaelis-Menten Systems July 18, 2017 1 / 22 Background A simple

More information

Activation of a receptor. Assembly of the complex

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

More information

Simple model of mrna production

Simple model of mrna production Simple model of mrna production We assume that the number of mrna (m) of a gene can change either due to the production of a mrna by transcription of DNA (which occurs at a constant rate α) or due to degradation

More information

Complete all warm up questions Focus on operon functioning we will be creating operon models on Monday

Complete all warm up questions Focus on operon functioning we will be creating operon models on Monday Complete all warm up questions Focus on operon functioning we will be creating operon models on Monday 1. What is the Central Dogma? 2. How does prokaryotic DNA compare to eukaryotic DNA? 3. How is DNA

More information

Biochemistry. Lecture 8 Enzyme Kinetics

Biochemistry. Lecture 8 Enzyme Kinetics Biochemistry Lecture 8 Enzyme Kinetics 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 -

More information

Multivariate point process models

Multivariate point process models Faculty of Science Multivariate point process models Niels Richard Hansen Department of Mathematical Sciences January 8, 200 Slide /20 Ideas and outline General aim: To build and implement a flexible (non-parametric),

More information

Stochastic dynamics of small gene regulation networks. Lev Tsimring BioCircuits Institute University of California, San Diego

Stochastic dynamics of small gene regulation networks. Lev Tsimring BioCircuits Institute University of California, San Diego Stochastic dynamics of small gene regulation networks Lev Tsimring BioCircuits Institute University of California, San Diego Nizhni Novgorod, June, 2011 Central dogma Activator Gene mrna Protein Repressor

More information

Welcome to Class 21!

Welcome to Class 21! Welcome to Class 21! Introductory Biochemistry! Lecture 21: Outline and Objectives l Regulation of Gene Expression in Prokaryotes! l transcriptional regulation! l principles! l lac operon! l trp attenuation!

More information

Problem Set 5. 1 Waiting times for chemical reactions (8 points)

Problem Set 5. 1 Waiting times for chemical reactions (8 points) Problem Set 5 1 Waiting times for chemical reactions (8 points) In the previous assignment, we saw that for a chemical reaction occurring at rate r, the distribution of waiting times τ between reaction

More information

Chapter 1. Modeling in systems biology. 1.1 Introduction. Aims

Chapter 1. Modeling in systems biology. 1.1 Introduction. Aims Chapter 1 Modeling in systems biology 1.1 Introduction An important aspect of systems biology is the concept of modeling the dynamics of biochemical networks where molecules are the nodes and the molecular

More information

Overview of Kinetics

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

More information

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 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

More information

From cell biology to Petri nets. Rainer Breitling, Groningen, NL David Gilbert, London, UK Monika Heiner, Cottbus, DE

From cell biology to Petri nets. Rainer Breitling, Groningen, NL David Gilbert, London, UK Monika Heiner, Cottbus, DE From cell biology to Petri nets Rainer Breitling, Groningen, NL David Gilbert, London, UK Monika Heiner, Cottbus, DE Biology = Concentrations Breitling / 2 The simplest chemical reaction A B irreversible,

More information

FUNDAMENTALS of SYSTEMS BIOLOGY From Synthetic Circuits to Whole-cell Models

FUNDAMENTALS of SYSTEMS BIOLOGY From Synthetic Circuits to Whole-cell Models FUNDAMENTALS of SYSTEMS BIOLOGY From Synthetic Circuits to Whole-cell Models Markus W. Covert Stanford University 0 CRC Press Taylor & Francis Group Boca Raton London New York Contents /... Preface, xi

More information

Biomolecular Feedback Systems

Biomolecular Feedback Systems Biomolecular Feedback Systems Domitilla Del Vecchio MIT Richard M. Murray Caltech DRAFT v0.4a, January 1, 2011 c California Institute of Technology All rights reserved. This manuscript is for review purposes

More information

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 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

More information

Michaelis-Menten Kinetics. Lecture 13: Kinetics II. Enzyme Reactions. Margaret A. Daugherty. Fall Substrates bind to the enzyme s active site

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

More information

Gene regulation II Biochemistry 302. Bob Kelm February 28, 2005

Gene regulation II Biochemistry 302. Bob Kelm February 28, 2005 Gene regulation II Biochemistry 302 Bob Kelm February 28, 2005 Catabolic operons: Regulation by multiple signals targeting different TFs Catabolite repression: Activity of lac operon is restricted when

More information

Prokaryotic Regulation

Prokaryotic Regulation Prokaryotic Regulation Control of transcription initiation can be: Positive control increases transcription when activators bind DNA Negative control reduces transcription when repressors bind to DNA regulatory

More information

Chemical Kinetics. Topic 7

Chemical Kinetics. Topic 7 Chemical Kinetics Topic 7 Corrosion of Titanic wrec Casón shipwrec 2Fe(s) + 3/2O 2 (g) + H 2 O --> Fe 2 O 3.H 2 O(s) 2Na(s) + 2H 2 O --> 2NaOH(aq) + H 2 (g) Two examples of the time needed for a chemical

More information

The Riboswitch is functionally separated into the ligand binding APTAMER and the decision-making EXPRESSION PLATFORM

The Riboswitch is functionally separated into the ligand binding APTAMER and the decision-making EXPRESSION PLATFORM The Riboswitch is functionally separated into the ligand binding APTAMER and the decision-making EXPRESSION PLATFORM Purine riboswitch TPP riboswitch SAM riboswitch glms ribozyme In-line probing is used

More information

APGRU6L2. Control of Prokaryotic (Bacterial) Genes

APGRU6L2. Control of Prokaryotic (Bacterial) Genes APGRU6L2 Control of Prokaryotic (Bacterial) Genes 2007-2008 Bacterial metabolism Bacteria need to respond quickly to changes in their environment STOP u if they have enough of a product, need to stop production

More information

Bioinformatics: Network Analysis

Bioinformatics: Network Analysis Bioinformatics: Network Analysis Kinetics of Gene Regulation COMP 572 (BIOS 572 / BIOE 564) - Fall 2013 Luay Nakhleh, Rice University 1 The simplest model of gene expression involves only two steps: the

More information

Name: SBI 4U. Gene Expression Quiz. Overall Expectation:

Name: SBI 4U. Gene Expression Quiz. Overall Expectation: Gene Expression Quiz Overall Expectation: - Demonstrate an understanding of concepts related to molecular genetics, and how genetic modification is applied in industry and agriculture Specific Expectation(s):

More information

Lecture 27. Transition States and Enzyme Catalysis

Lecture 27. Transition States and Enzyme Catalysis Lecture 27 Transition States and Enzyme Catalysis Reading for Today: Chapter 15 sections B and C Chapter 16 next two lectures 4/8/16 1 Pop Question 9 Binding data for your thesis protein (YTP), binding

More information

2013 W. H. Freeman and Company. 6 Enzymes

2013 W. H. Freeman and Company. 6 Enzymes 2013 W. H. Freeman and Company 6 Enzymes CHAPTER 6 Enzymes Key topics about enzyme function: Physiological significance of enzymes Origin of catalytic power of enzymes Chemical mechanisms of catalysis

More information

Tala Saleh. Mohammad Omari. Dr. Ma moun

Tala Saleh. Mohammad Omari. Dr. Ma moun 20 0 Tala Saleh Mohammad Omari Razi Kittaneh Dr. Ma moun Quick recap The rate of Chemical Reactions Rises linearly as the substrate concentration [S] increases. The rate of Enzymatic Reactions Rises rapidly

More information

10-810: Advanced Algorithms and Models for Computational Biology. microrna and Whole Genome Comparison

10-810: Advanced Algorithms and Models for Computational Biology. microrna and Whole Genome Comparison 10-810: Advanced Algorithms and Models for Computational Biology microrna and Whole Genome Comparison Central Dogma: 90s Transcription factors DNA transcription mrna translation Proteins Central Dogma:

More information

Previous Class. Today. Cosubstrates (cofactors)

Previous Class. Today. Cosubstrates (cofactors) Previous Class Cosubstrates (cofactors) Today Proximity effect Basic equations of Kinetics Steady state kinetics Michaelis Menten equations and parameters Enzyme Kinetics Enzyme kinetics implies characterizing

More information

A First Course on Kinetics and Reaction Engineering. Class 9 on Unit 9

A First Course on Kinetics and Reaction Engineering. Class 9 on Unit 9 A First Course on Kinetics and Reaction Engineering Class 9 on Unit 9 Part I - Chemical Reactions Part II - Chemical Reaction Kinetics Where We re Going A. Rate Expressions - 4. Reaction Rates and Temperature

More information

Gene regulation II Biochemistry 302. February 27, 2006

Gene regulation II Biochemistry 302. February 27, 2006 Gene regulation II Biochemistry 302 February 27, 2006 Molecular basis of inhibition of RNAP by Lac repressor 35 promoter site 10 promoter site CRP/DNA complex 60 Lewis, M. et al. (1996) Science 271:1247

More information

Lecture 4: Transcription networks basic concepts

Lecture 4: Transcription networks basic concepts Lecture 4: Transcription networks basic concepts - Activators and repressors - Input functions; Logic input functions; Multidimensional input functions - Dynamics and response time 2.1 Introduction The

More information

Translation and Operons

Translation and Operons Translation and Operons You Should Be Able To 1. Describe the three stages translation. including the movement of trna molecules through the ribosome. 2. Compare and contrast the roles of three different

More information

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. 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,

More information

REVIEW SESSION. Wednesday, September 15 5:30 PM SHANTZ 242 E

REVIEW SESSION. Wednesday, September 15 5:30 PM SHANTZ 242 E REVIEW SESSION Wednesday, September 15 5:30 PM SHANTZ 242 E Gene Regulation Gene Regulation Gene expression can be turned on, turned off, turned up or turned down! For example, as test time approaches,

More information

dynamic processes in cells (a systems approach to biology)

dynamic processes in cells (a systems approach to biology) dynamic processes in cells (a systems approach to biology) jeremy gunawardena department of systems biology harvard medical school lecture 8 1 october 2015 cellular identity can be re-programmed III adult

More information

Networks in systems biology

Networks in systems biology Networks in systems biology Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 4500, Spring 2017 M. Macauley (Clemson) Networks in systems

More information

+ regulation. ribosomes

+ regulation. ribosomes central dogma + regulation rpl DNA tsx rrna trna mrna ribosomes tsl ribosomal proteins structural proteins transporters enzymes srna regulators RNAp DNAp tsx initiation control by transcription factors

More information

Enzyme Kinetics: The study of reaction rates. For each very short segment dt of the reaction: V k 1 [S]

Enzyme Kinetics: The study of reaction rates. For each very short segment dt of the reaction: V k 1 [S] Enzyme Kinetics: The study of reaction rates. For the one-way st -order reaction: S the rate of reaction (V) is: V P [ P] moles / L t sec For each very short segment dt of the reaction: d[ P] d[ S] V dt

More information

Enzymes II. Dr. Mamoun Ahram Summer, 2017

Enzymes II. Dr. Mamoun Ahram Summer, 2017 Enzymes II Dr. Mamoun Ahram Summer, 2017 Kinetics Kinetics is deals with the rates of chemical reactions. Chemical kinetics is the study of the rates of chemical reactions. For the reaction (A P), The

More information

Hybrid Model of gene regulatory networks, the case of the lac-operon

Hybrid Model of gene regulatory networks, the case of the lac-operon Hybrid Model of gene regulatory networks, the case of the lac-operon Laurent Tournier and Etienne Farcot LMC-IMAG, 51 rue des Mathématiques, 38041 Grenoble Cedex 9, France Laurent.Tournier@imag.fr, Etienne.Farcot@imag.fr

More information

Prokaryotic Gene Expression (Learning Objectives)

Prokaryotic Gene Expression (Learning Objectives) Prokaryotic Gene Expression (Learning Objectives) 1. Learn how bacteria respond to changes of metabolites in their environment: short-term and longer-term. 2. Compare and contrast transcriptional control

More information

Nonlinear pharmacokinetics

Nonlinear pharmacokinetics 5 Nonlinear pharmacokinetics 5 Introduction 33 5 Capacity-limited metabolism 35 53 Estimation of Michaelis Menten parameters(v max andk m ) 37 55 Time to reach a given fraction of steady state 56 Example:

More information

A. One-Substrate Reactions (1) Kinetic concepts

A. One-Substrate Reactions (1) Kinetic concepts A. One-Substrate Reactions (1) Kinetic concepts (2) Kinetic analysis (a) Briggs-Haldane steady-state treatment (b) Michaelis constant (K m ) (c) Specificity constant (3) Graphical analysis (4) Practical

More information

Statistical mechanics of biological processes

Statistical mechanics of biological processes Statistical mechanics of biological processes 1 Modeling biological processes Describing biological processes requires models. If reaction occurs on timescales much faster than that of connected processes

More information

Bi 8 Lecture 11. Quantitative aspects of transcription factor binding and gene regulatory circuit design. Ellen Rothenberg 9 February 2016

Bi 8 Lecture 11. Quantitative aspects of transcription factor binding and gene regulatory circuit design. Ellen Rothenberg 9 February 2016 Bi 8 Lecture 11 Quantitative aspects of transcription factor binding and gene regulatory circuit design Ellen Rothenberg 9 February 2016 Major take-home messages from λ phage system that apply to many

More information

Sample Question Solutions for the Chemistry of Life Topic Test

Sample Question Solutions for the Chemistry of Life Topic Test Sample Question Solutions for the Chemistry of Life Topic Test 1. Enzymes play a crucial role in biology by serving as biological catalysts, increasing the rates of biochemical reactions by decreasing

More information

Topic 4 - #14 The Lactose Operon

Topic 4 - #14 The Lactose Operon Topic 4 - #14 The Lactose Operon The Lactose Operon The lactose operon is an operon which is responsible for the transport and metabolism of the sugar lactose in E. coli. - Lactose is one of many organic

More information

13.4 Gene Regulation and Expression

13.4 Gene Regulation and Expression 13.4 Gene Regulation and Expression Lesson Objectives Describe gene regulation in prokaryotes. Explain how most eukaryotic genes are regulated. Relate gene regulation to development in multicellular organisms.

More information

Bistability and a differential equation model of the lac operon

Bistability and a differential equation model of the lac operon Bistability and a differential equation model of the lac operon Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 4500, Fall 2016 M. Macauley

More information

Gene Regulation and Expression

Gene Regulation and Expression THINK ABOUT IT Think of a library filled with how-to books. Would you ever need to use all of those books at the same time? Of course not. Now picture a tiny bacterium that contains more than 4000 genes.

More information

Three types of RNA polymerase in eukaryotic nuclei

Three types of RNA polymerase in eukaryotic nuclei Three types of RNA polymerase in eukaryotic nuclei Type Location RNA synthesized Effect of α-amanitin I Nucleolus Pre-rRNA for 18,.8 and 8S rrnas Insensitive II Nucleoplasm Pre-mRNA, some snrnas Sensitive

More information

Michaelis-Menten Kinetics

Michaelis-Menten Kinetics Michaelis-Menten Kinetics Two early 20th century scientists, Leonor Michaelis and Maud Leonora Menten, proposed the model known as Michaelis-Menten Kinetics to account for enzymatic dynamics. The model

More information

Multi-modality in gene regulatory networks with slow promoter kinetics. Abstract. Author summary

Multi-modality in gene regulatory networks with slow promoter kinetics. Abstract. Author summary Multi-modality in gene regulatory networks with slow promoter kinetics M. Ali Al-Radhawi 1, D. Del Vecchio 2, E. D. Sontag 3*, 1 Department of Electrical and Computer Engineering, Northeastern University,

More information

Are DNA Transcription Factor Proteins Maxwellian Demons?

Are DNA Transcription Factor Proteins Maxwellian Demons? Are DNA Transcription Factor Proteins Maxwellian Demons? * physics as a design constraint on molecular evolution. Alexander Grosberg, Longhua Hu, U. Minnesota, NYU U. Minnesota Biophysical Journal 2008

More information

Enzymes Part III: Enzyme kinetics. Dr. Mamoun Ahram Summer semester,

Enzymes Part III: Enzyme kinetics. Dr. Mamoun Ahram Summer semester, Enzymes Part III: Enzyme kinetics Dr. Mamoun Ahram Summer semester, 2015-2016 Kinetics Kinetics is deals with the rates of chemical reactions. Chemical kinetics is the study of the rates of chemical reactions.

More information

Computational Biology 1

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

More information

Persistence and Stationary Distributions of Biochemical Reaction Networks

Persistence and Stationary Distributions of Biochemical Reaction Networks Persistence and Stationary Distributions of Biochemical Reaction Networks David F. Anderson Department of Mathematics University of Wisconsin - Madison Discrete Models in Systems Biology SAMSI December

More information

Lecture 15 (10/20/17) Lecture 15 (10/20/17)

Lecture 15 (10/20/17) Lecture 15 (10/20/17) Reading: Ch6; 98-203 Ch6; Box 6- Lecture 5 (0/20/7) Problems: Ch6 (text); 8, 9, 0,, 2, 3, 4, 5, 6 Ch6 (study guide-facts); 6, 7, 8, 9, 20, 2 8, 0, 2 Ch6 (study guide-applying); NEXT Reading: Ch6; 207-20

More information

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 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

More information

Stochastic Processes at Single-molecule and Single-cell levels

Stochastic Processes at Single-molecule and Single-cell levels Stochastic Processes at Single-molecule and Single-cell levels Hao Ge haoge@pu.edu.cn Beijing International Center for Mathematical Research 2 Biodynamic Optical Imaging Center Peing University, China

More information

Modelling chemical kinetics

Modelling chemical kinetics Modelling chemical kinetics Nicolas Le Novère, Institute, EMBL-EBI n.lenovere@gmail.com Systems Biology models ODE models Reconstruction of state variable evolution from process descriptions: Processes

More information

Stochastic Transcription Elongation via Rule Based Modelling

Stochastic Transcription Elongation via Rule Based Modelling Stochastic Transcription Elongation via Rule Based Modelling Masahiro HAMANO hamano@jaist.ac.jp SASB, Saint Malo 2015 1 Purpose of This Talk 2 mechano-chemical TE as Rule Based Modelling 3 Table of Contents

More information

Bistability in ODE and Boolean network models

Bistability in ODE and Boolean network models Bistability in ODE and Boolean network models Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 4500, Spring 2017 M. Macauley (Clemson)

More information

Controlling Gene Expression

Controlling Gene Expression Controlling Gene Expression Control Mechanisms Gene regulation involves turning on or off specific genes as required by the cell Determine when to make more proteins and when to stop making more Housekeeping

More information

Chapter 5 Metabolism: Energy & Enzymes

Chapter 5 Metabolism: Energy & Enzymes Energy Energy is the capacity to do work Kinetic energy Energy of motion Potential energy Stored energy What do you use for energy? Where do you think the energy is stored these molecules? The BONDS! Every

More information

Sig2GRN: A Software Tool Linking Signaling Pathway with Gene Regulatory Network for Dynamic Simulation

Sig2GRN: A Software Tool Linking Signaling Pathway with Gene Regulatory Network for Dynamic Simulation Sig2GRN: A Software Tool Linking Signaling Pathway with Gene Regulatory Network for Dynamic Simulation Authors: Fan Zhang, Runsheng Liu and Jie Zheng Presented by: Fan Wu School of Computer Science and

More information

TOPIC 6: Chemical kinetics

TOPIC 6: Chemical kinetics TOPIC 6: Chemical kinetics Reaction rates Reaction rate laws Integrated reaction rate laws Reaction mechanism Kinetic theories Arrhenius law Catalysis Enzimatic catalysis Fuente: Cedre http://loincognito.-iles.wordpress.com/202/04/titanic-

More information

CHEM 341 PHYSICAL CHEMISTRY FINAL EXAM. Name

CHEM 341 PHYSICAL CHEMISTRY FINAL EXAM. Name CHEM 341 PHYSICAL CHEMISTRY FINAL EXAM Name Do not open this exam until told to do so. The exam consists of 6 pages, including this one. Count them to insure that they are all there. Constants: R = 8.31

More information

Rex-Family Repressor/NADH Complex

Rex-Family Repressor/NADH Complex Kasey Royer Michelle Lukosi Rex-Family Repressor/NADH Complex Part A The biological sensing protein that we selected is the Rex-family repressor/nadh complex. We chose this sensor because it is a calcium

More information

56:198:582 Biological Networks Lecture 11

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.

More information

Division Ave. High School AP Biology

Division Ave. High School AP Biology Control of Prokaryotic (Bacterial) Genes 20072008 Bacterial metabolism n Bacteria need to respond quickly to changes in their environment u if they have enough of a product, need to stop production n why?

More information

AP Bio Module 16: Bacterial Genetics and Operons, Student Learning Guide

AP Bio Module 16: Bacterial Genetics and Operons, Student Learning Guide Name: Period: Date: AP Bio Module 6: Bacterial Genetics and Operons, Student Learning Guide Getting started. Work in pairs (share a computer). Make sure that you log in for the first quiz so that you get

More information