2. Mathematical descriptions. (i) the master equation (ii) Langevin theory. 3. Single cell measurements
|
|
- Kelly Sherman
- 6 years ago
- Views:
Transcription
1 1. Why stochastic?. Mathematical descriptions (i) the master equation (ii) Langevin theory 3. Single cell measurements 4. Consequences
2 Any chemical reaction is stochastic. k P d φ dp dt = k d P deterministic equation protein number (P) k/d 0 time (sec) 1000
3 Any chemical reaction is stochastic. k P d φ protein number (P) k/d 0 time (sec) 1000
4 Why are chemical reactions stochastic? f A B C 1. Reactants diffuse to find each other in solution.. They must overcome the energy barrier of the reaction. Both events are randomly affected by thermal fluctuations: collisions with other (solvent) molecules
5 When are chemical reactions significantly stochastic? As a reaction only increases or decreases the number of molecules by one or two, it is only when numbers of molecules are small that random timing of individual reactions will matter. noise = standard deviation mean
6 Noise depends on low numbers. 60 mean mean 10, protein number noise = noise = time (sec) time (sec)
7 Gene expression in bacteria
8 Transcription: DNA to mrna QuickTime and a Cinepak decompressor are needed to see this picture.
9 Translation: mrna to protein
10 Gene expression in bacteria
11 Measuring noise in single cells.
12 Measuring noise Integrate Green Fluorescent Protein OriC Escherichia coli chromosome
13
14 Example: RNAP varies from cell to cell but gene expression is deterministic P [ RNAP] hence η = P P P =
15 Two colour experiment OriC cfp yfp Look at difference between two colours η int ( C Y )
16 Each colour is controlled by identical regulatory sequences. cfp yfp Correlated expression ( Quiet ) Uncorrelated expression ( Noisy ) fluorescence time fluorescence time
17 Example: RNAP varies but gene expression is deterministic No intrinsic noise η = 1 N 1 N ( C Y ) int = 1 C N Y 0
18 Two types of noise: intrinsic and extrinsic Extrinsic Intrinsic Extrinsic variables: same for each gene e.g. number of free RNAPs, cellular environment Intrinsic variables: different for each gene e.g. number of transcribing RNAPs, number of mrnas
19 PHASE CONTRAST IMAGE
20 CFP IMAGE
21 YFP IMAGE
22 MERGED IMAGE
23 Extracting fluorescence values Phase image
24 Automatic Cell Identification Automatic cell identification
25 What results do we expect?
26 Simulation of constitutive gene expression (Gillespie method) protein number η tot protein conc ( M) mrna number η tot η tot time (units of cell cycles)
27 Experimental results YFP intensity steady state limit cycle extrinsic noise intrinsic noise CFP intensity
28 Experimentally then extrinsic average intrinsic average giving η = η + η tot int ext
29 Definitions total noise η tot = C + Y C Y C Y bracket denotes average over all cells intrinsic and extrinsic noise η int = which obey ( C Y ) C Y η = η + η tot int ext η ext = CY C Y C Y
30 Experimentally, two different noise sources can be distinguished. 1.6 M D Normalized Mean YFP Intensity Intrinsic Noise, η int Extrinsic Noise, η ext Normalized Mean CFP Intensity Constraint: η + η = η int ext tot
31 Intrinsic noise can be small Twin (strong) lac-regulated artificial promoters, based on λ P L yfp cfp yfp=cfp ΔlacI Intensity 1.0 η int = ( ) η ext = ( ) η tot = ( )
32 Negative transcriptional control P =Promoter (DNA sequence) R =Repressor (protein) Repressor gene R P R gene x Off On R R R mrna Nothing protein X
33 Intrinsic noise depends on expression level yfp cfp WT (RPR37) + IPTG WT (RPR37) decrease expression 3-fold Intensity = 1 η int = ( ) η ext = ( ) Intensity = 0.03 η int = 0.5 (0.-0.7) η ext = 0.3 ( )
34 1. Gene expression is both intrinsically and extrinsically stochastic.. Extrinsic noise is usually greater than intrinsic noise. 3. Intrinsic noise decreases as gene expression levels increase. Elowitz et al. (00) Swain et al. (00)
35 Red: protein Green spots: mrna scale bar: 1 μm
36 Variance in mrna against mrna numbers Naïve prediction:
37 Time course of mrna numbers: mrna is produced in bursts mrna mrna of sister cell piecewise linear fit time (min) smoothed version of red data
38 More complex model DNA off DNA on TRANSCRIPTION mrna TRANSLATION mrna + protein DEGRADATION DEGRADATION
39 Consequences
40 Noise affects the reliable function of genetic networks, by affecting timing. e.g. biological rhythms (Elowitz & Leibler, Barkai & Leibler) deterministic stochastic
41 Noise can also be exploited by cells, e.g. avoiding the immune response. Salmonella typhimurium from Molecular Cell Biology, 4 th ed
the noisy gene Biology of the Universidad Autónoma de Madrid Jan 2008 Juan F. Poyatos Spanish National Biotechnology Centre (CNB)
Biology of the the noisy gene Universidad Autónoma de Madrid Jan 2008 Juan F. Poyatos Spanish National Biotechnology Centre (CNB) day III: noisy bacteria - Regulation of noise (B. subtilis) - Intrinsic/Extrinsic
More informationA synthetic oscillatory network of transcriptional regulators
A synthetic oscillatory network of transcriptional regulators Michael B. Elowitz & Stanislas Leibler, Nature, 403, 2000 igem Team Heidelberg 2008 Journal Club Andreas Kühne Introduction Networks of interacting
More informationBoulder 07: Modelling Stochastic Gene Expression
Boulder 7: Modelling Stochastic Gene Expression Peter Swain, Physiology, McGill University swain@cnd.mcgill.ca The whys and wherefores of stochasticity A system evolves stochastically if its dynamics is
More informationLecture 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 informationStochastic simulations!
Stochastic simulations! Application to biomolecular networks! Literature overview Noise in genetic networks! Origins! How to measure the noise and distinguish between the two sources of noise (intrinsic
More informationGene Expression as a Stochastic Process: From Gene Number Distributions to Protein Statistics and Back
Gene Expression as a Stochastic Process: From Gene Number Distributions to Protein Statistics and Back June 19, 2007 Motivation & Basics A Stochastic Approach to Gene Expression Application to Experimental
More informationLecture 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 informationModeling and Systems Analysis of Gene Regulatory Networks
Modeling and Systems Analysis of Gene Regulatory Networks Mustafa Khammash Center for Control Dynamical-Systems and Computations University of California, Santa Barbara Outline Deterministic A case study:
More informationStochastic 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 informationarxiv: v2 [q-bio.qm] 12 Jan 2017
Approximation and inference methods for stochastic biochemical kinetics - a tutorial review arxiv:1608.06582v2 [q-bio.qm] 12 Jan 2017 David Schnoerr 1,2,3, Guido Sanguinetti 2,3, and Ramon Grima 1,3,*
More informationGene 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 information56:198:582 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 informationStochastic Gene Expression: Modeling, Analysis, and Identification
Munsky; q-bio Stochastic Gene Expression: Modeling, Analysis, and Identification Mustafa Khammash University of California, Santa Barbara Center for Control, Dynamical Systems and Computation CC DC Outline
More informationName: 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 informationChapter 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 informationA Synthetic Oscillatory Network of Transcriptional Regulators
A Synthetic Oscillatory Network of Transcriptional Regulators Michael Elowitz & Stanislas Leibler Nature, 2000 Presented by Khaled A. Rahman Background Genetic Networks Gene X Operator Operator Gene Y
More informationBiomolecular 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 informationStochastically driven genetic circuits
Stochastically driven genetic circuits CHAOS 16, 026103 2006 L. S. Tsimring Institute for Nonlinear Science, University of California, San Diego, La Jolla, California 92093-0402 D. Volfson Institute for
More informationBE 150: Design Principles of Genetic Circuits
BE 50: Design Principles of Genetic Circuits Justin Bois Caltech Spring, 208 This document was prepared at Caltech with financial support from the Donna and Benjamin M. Rosen Bioengineering Center. 208
More informationarxiv: v2 [q-bio.mn] 20 Nov 2017
Models of protein production with cell cycle Renaud Dessalles, Vincent Fromion, Philippe Robert arxiv:7.6378v [q-bio.mn] Nov 7 Abstract Classical stochastic models of protein production usually do not
More informationStochastic simulations
Stochastic simulations Application to molecular networks Literature overview Noise in genetic networks Origins How to measure and distinguish between the two types of noise (intrinsic vs extrinsic)? What
More informationTopic 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 informationStochastic simulations Application to molecular networks
Stochastic simulations Application to molecular networks Didier Gonze May 1, 27 Les Houches - Spring School - April 9-2, 27 Contents 1 Introduction 3 1.1 Noise in biology.................................
More informationEngineering of Repressilator
The Utilization of Monte Carlo Techniques to Simulate the Repressilator: A Cyclic Oscillatory System Seetal Erramilli 1,2 and Joel R. Stiles 1,3,4 1 Bioengineering and Bioinformatics Summer Institute,
More informationColored extrinsic fluctuations and stochastic gene expression: Supplementary information
Colored extrinsic fluctuations and stochastic gene expression: Supplementary information Vahid Shahrezaei, Julien F. Ollivier, and Peter S. Swain Stochastic simulation algorithm with continuous and discontinuous
More informationAn analytical model of the effect of plasmid copy number on transcriptional noise strength
An analytical model of the effect of plasmid copy number on transcriptional noise strength William and Mary igem 2015 1 Introduction In the early phases of our experimental design, we considered incorporating
More informationComplete 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 informationProtein production. What can we do with ODEs
Protein production What can we do with ODEs 2018 Modeling protein production with ODE We have seen various examples of wiring networks. Once you know how a regulatory network is wired in a particular way,
More informationComputational Cell Biology Lecture 4
Computational Cell Biology Lecture 4 Case Study: Basic Modeling in Gene Expression Yang Cao Department of Computer Science DNA Structure and Base Pair Gene Expression Gene is just a small part of DNA.
More informationProblem 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 informationComputational Biology: Basics & Interesting Problems
Computational Biology: Basics & Interesting Problems Summary Sources of information Biological concepts: structure & terminology Sequencing Gene finding Protein structure prediction Sources of information
More informationA(γ A D A + γ R D R + γ C R + δ A )
Title: Mechanisms of noise-resistance in genetic oscillators Authors: José M. G. Vilar 1,2, Hao Yuan Kueh 1, Naama Barkai 3, and Stanislas Leibler 1,2 1 Howard Hughes Medical Institute, Departments of
More informationREGULATION 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 informationProkaryotic 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 information3.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 informationBioinformatics: 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 information13.4 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 informationSTOCHASTICITY IN GENE EXPRESSION: FROM THEORIES TO PHENOTYPES
STOCHASTICITY IN GENE EXPRESSION: FROM THEORIES TO PHENOTYPES Mads Kærn*, Timothy C. Elston, William J. Blake and James J. Collins Abstract Genetically identical cells exposed to the same environmental
More informationModelling Biochemical Pathways with Stochastic Process Algebra
Modelling Biochemical Pathways with Stochastic Process Algebra Jane Hillston. LFCS, University of Edinburgh 13th April 2007 The PEPA project The PEPA project started in Edinburgh in 1991. The PEPA project
More informationGENE REGULATION AND PROBLEMS OF DEVELOPMENT
GENE REGULATION AND PROBLEMS OF DEVELOPMENT By Surinder Kaur DIET Ropar Surinder_1998@ yahoo.in Mob No 9988530775 GENE REGULATION Gene is a segment of DNA that codes for a unit of function (polypeptide,
More informationWhen do diffusion-limited trajectories become memoryless?
When do diffusion-limited trajectories become memoryless? Maciej Dobrzyński CWI (Center for Mathematics and Computer Science) Kruislaan 413, 1098 SJ Amsterdam, The Netherlands Abstract Stochastic description
More informationModelling Stochastic Gene Expression
Modelling Stochastic Gene Expression Peter Swain Centre for Systems Biology at Edinburgh University of Edinburgh peter.swain@ed.ac.uk The whys and wherefores of stochasticity A system evolves stochastically
More informationThe Effect of Stochasticity on the Lac Operon: An Evolutionary Perspective
The Effect of Stochasticity on the Lac Operon: An Evolutionary Perspective Milan van Hoek *, Paulien Hogeweg Theoretical Biology/Bioinformatics Group, Utrecht University, Utrecht, The Netherlands The role
More informationINVESTIGATION OF STOCHASTICITY IN GENE EXPRESSION IN RESPONSE TO OSMOTIC STRESS
INVESTIGATION OF STOCHASTICITY IN GENE EXPRESSION IN RESPONSE TO OSMOTIC STRESS By GAYATHRI BALANDARAM A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE
More informationBi 1x Spring 2014: LacI Titration
Bi 1x Spring 2014: LacI Titration 1 Overview In this experiment, you will measure the effect of various mutated LacI repressor ribosome binding sites in an E. coli cell by measuring the expression of a
More informationBiology. 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 informationURL: <
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 informationControlling 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 informationREVIEW 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 informationBiomolecular reaction networks: gene regulation & the Repressilator Phys 682/ CIS 629: Computational Methods for Nonlinear Systems
Biomolecular reaction networks: gene regulation & the Repressilator Phys 682/ CIS 629: Computational Methods for Nonlinear Systems Processing information to coordinate activity regulatory & signaling networks
More informationProkaryotic 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 informationAnalog Electronics Mimic Genetic Biochemical Reactions in Living Cells
Analog Electronics Mimic Genetic Biochemical Reactions in Living Cells Dr. Ramez Daniel Laboratory of Synthetic Biology & Bioelectronics (LSB 2 ) Biomedical Engineering, Technion May 9, 2016 Cytomorphic
More informationName Period The Control of Gene Expression in Prokaryotes Notes
Bacterial DNA contains genes that encode for many different proteins (enzymes) so that many processes have the ability to occur -not all processes are carried out at any one time -what allows expression
More informationIntroduction to Bioinformatics
Systems biology Introduction to Bioinformatics Systems biology: modeling biological p Study of whole biological systems p Wholeness : Organization of dynamic interactions Different behaviour of the individual
More informationBME 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 informationGLOBEX Bioinformatics (Summer 2015) Genetic networks and gene expression data
GLOBEX Bioinformatics (Summer 2015) Genetic networks and gene expression data 1 Gene Networks Definition: A gene network is a set of molecular components, such as genes and proteins, and interactions between
More informationStochastic Processes around Central Dogma
Stochastic Processes around Central Dogma Hao Ge haoge@pku.edu.cn Beijing International Center for Mathematical Research Biodynamic Optical Imaging Center Peking University, China http://www.bicmr.org/personal/gehao/
More informationControl 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 information2 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 informationCS-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 informationStochastic Processes around Central Dogma
Stochastic Processes around Central Dogma Hao Ge haoge@pu.edu.cn Beijing International Center for Mathematical Research Biodynamic Optical Imaging Center Peing University, China http://www.bicmr.org/personal/gehao/
More informationIntroduction. Gene expression is the combined process of :
1 To know and explain: Regulation of Bacterial Gene Expression Constitutive ( house keeping) vs. Controllable genes OPERON structure and its role in gene regulation Regulation of Eukaryotic Gene Expression
More informationNoise is often perceived as being undesirable and unpredictable;
Intrinsic noise in gene regulatory networks Mukund Thattai and Alexander van Oudenaarden* Department of Physics, Room 13-2010, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge,
More informationLesson Overview. Gene Regulation and Expression. Lesson Overview Gene Regulation and Expression
13.4 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
More informationBrief contents. Chapter 1 Virus Dynamics 33. Chapter 2 Physics and Biology 52. Randomness in Biology. Chapter 3 Discrete Randomness 59
Brief contents I First Steps Chapter 1 Virus Dynamics 33 Chapter 2 Physics and Biology 52 II Randomness in Biology Chapter 3 Discrete Randomness 59 Chapter 4 Some Useful Discrete Distributions 96 Chapter
More informationL3.1: Circuits: Introduction to Transcription Networks. Cellular Design Principles Prof. Jenna Rickus
L3.1: Circuits: Introduction to Transcription Networks Cellular Design Principles Prof. Jenna Rickus In this lecture Cognitive problem of the Cell Introduce transcription networks Key processing network
More informationThe effect of transcription and translation initiation frequencies on the. stochastic fluctuations in prokaryotic gene expression.
JBC Papers in Press. Published on November 2, 2000 as Manuscript M006264200 The effect of transcription and translation initiation frequencies on the stochastic fluctuations in prokaryotic gene expression.
More informationUNIT 6 PART 3 *REGULATION USING OPERONS* Hillis Textbook, CH 11
UNIT 6 PART 3 *REGULATION USING OPERONS* Hillis Textbook, CH 11 REVIEW: Signals that Start and Stop Transcription and Translation BUT, HOW DO CELLS CONTROL WHICH GENES ARE EXPRESSED AND WHEN? First of
More informationStochastic and delayed stochastic models of gene expression. and regulation
*Manuscript Stochastic and delayed stochastic models of gene expression and regulation Andre S. Ribeiro a,b a Computational Systems Biology Research Group, Dept. of Signal Processing, Tampere University
More informationSlow protein fluctuations explain the emergence of growth phenotypes and persistence in clonal bacterial populations
Slow protein fluctuations explain the emergence of growth phenotypes and persistence in clonal bacterial populations Andrea Rocco, Andrzej M. Kierzek, Johnjoe McFadden Faculty of Health and Medical Sciences,
More informationBasic modeling approaches for biological systems. Mahesh Bule
Basic modeling approaches for biological systems Mahesh Bule The hierarchy of life from atoms to living organisms Modeling biological processes often requires accounting for action and feedback involving
More informationCHAPTER 13 PROKARYOTE GENES: E. COLI LAC OPERON
PROKARYOTE GENES: E. COLI LAC OPERON CHAPTER 13 CHAPTER 13 PROKARYOTE GENES: E. COLI LAC OPERON Figure 1. Electron micrograph of growing E. coli. Some show the constriction at the location where daughter
More informationColored Noise Induced Synchronized Switching in the Genetic 2011 年 Toggle 4 月 27 Switch 日 Systems 1 / 27Co. Quorum Sensing
Colored Noise Induced Synchronized Switching in the Genetic Toggle Switch Systems Coupled by Quorum Sensing 王沛, 吕金虎 E-mail: wp0307@126.com School of Mathematics and Statistics, Wuhan University 第 Ô3 国
More informationBiology 105/Summer Bacterial Genetics 8/12/ Bacterial Genomes p Gene Transfer Mechanisms in Bacteria p.
READING: 14.2 Bacterial Genomes p. 481 14.3 Gene Transfer Mechanisms in Bacteria p. 486 Suggested Problems: 1, 7, 13, 14, 15, 20, 22 BACTERIAL GENETICS AND GENOMICS We still consider the E. coli genome
More informationLecture 1 Modeling in Biology: an introduction
Lecture 1 in Biology: an introduction Luca Bortolussi 1 Alberto Policriti 2 1 Dipartimento di Matematica ed Informatica Università degli studi di Trieste Via Valerio 12/a, 34100 Trieste. luca@dmi.units.it
More informationSlide 1 / 7. Free Response
Slide 1 / 7 Free Response Slide 2 / 7 Slide 3 / 7 1 The above diagrams illustrate the experiments carried out by Griffith and Hershey and Chaserespectively. Describe the hypothesis or conclusion that each
More information12-5 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 informationToward in vivo digital synchronous sequential circuits
Toward in vivo digital synchronous seuential circuits MIHA MOŠON 1, MONIA CIGLIČ 2, NIOLAJ ZIMIC 1 and MIHA MRAZ 1 1 Computer Structures and Systems Laboratory Faculty of Computer and Information Science
More informationUnit 3: Control and regulation Higher Biology
Unit 3: Control and regulation Higher Biology To study the roles that genes play in the control of growth and development of organisms To be able to Give some examples of features which are controlled
More informationCombined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology
Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology The MIT Faculty has made this article openly available. Please share how this
More informationStochastic 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 informationRegulation of Gene Expression
Chapter 18 Regulation of Gene Expression PowerPoint Lecture Presentations for Biology Eighth Edition Neil Campbell and Jane Reece Lectures by Chris Romero, updated by Erin Barley with contributions from
More informationIntrinsic Noise in Nonlinear Gene Regulation Inference
Intrinsic Noise in Nonlinear Gene Regulation Inference Chao Du Department of Statistics, University of Virginia Joint Work with Wing H. Wong, Department of Statistics, Stanford University Transcription
More informationIntroduction to Bioinformatics
CSCI8980: Applied Machine Learning in Computational Biology Introduction to Bioinformatics Rui Kuang Department of Computer Science and Engineering University of Minnesota kuang@cs.umn.edu History of Bioinformatics
More informationHonors Biology Reading Guide Chapter 11
Honors Biology Reading Guide Chapter 11 v Promoter a specific nucleotide sequence in DNA located near the start of a gene that is the binding site for RNA polymerase and the place where transcription begins
More informationTranslation 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 informationBasic 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 information4. Why not make all enzymes all the time (even if not needed)? Enzyme synthesis uses a lot of energy.
1 C2005/F2401 '10-- Lecture 15 -- Last Edited: 11/02/10 01:58 PM Copyright 2010 Deborah Mowshowitz and Lawrence Chasin Department of Biological Sciences Columbia University New York, NY. Handouts: 15A
More informationBacterial Genetics & Operons
Bacterial Genetics & Operons The Bacterial Genome Because bacteria have simple genomes, they are used most often in molecular genetics studies Most of what we know about bacterial genetics comes from the
More informationInitiation of translation in eukaryotic cells:connecting the head and tail
Initiation of translation in eukaryotic cells:connecting the head and tail GCCRCCAUGG 1: Multiple initiation factors with distinct biochemical roles (linking, tethering, recruiting, and scanning) 2: 5
More information7.32/7.81J/8.591J: Systems Biology. Fall Exam #1
7.32/7.81J/8.591J: Systems Biology Fall 2013 Exam #1 Instructions 1) Please do not open exam until instructed to do so. 2) This exam is closed- book and closed- notes. 3) Please do all problems. 4) Use
More informationCHAPTER : Prokaryotic Genetics
CHAPTER 13.3 13.5: Prokaryotic Genetics 1. Most bacteria are not pathogenic. Identify several important roles they play in the ecosystem and human culture. 2. How do variations arise in bacteria considering
More informationBacterial growth control & control by bacterial growth. PICB, Shanghai July 18, 2008
Bacterial growth control & control by bacterial growth PICB, Shanghai July 18, 2008 Systems biology: from molecules to physiology Physiology: the working of a living organism; reproduction & adaptation
More informationStatistical Physics approach to Gene Regulatory Networks
Statistical Physics approach to Gene Regulatory Networks Jonathan Fiorentino Supervisors: Andrea Crisanti, Andrea De Martino February 16, 2017 Scuola di dottorato Vito Volterra, XXXI ciclo 1 Introduction
More informationAP 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 informationGENES AND CHROMOSOMES III. Lecture 5. Biology Department Concordia University. Dr. S. Azam BIOL 266/
GENES AND CHROMOSOMES III Lecture 5 BIOL 266/4 2014-15 Dr. S. Azam Biology Department Concordia University CELL NUCLEUS AND THE CONTROL OF GENE EXPRESSION OPERONS Introduction All cells in a multi-cellular
More informationPart 3: Introduction to Master Equation and Complex Initial Conditions in Lattice Microbes
Part 3: Introduction to Master Equation Cells: and Complex Initial Conditions in Lattice re cells Microbes en Biophysics, and UC urgh, June 6-8, 2016 rson Joseph R. Peterson and Michael J. Hallock Luthey-Schulten
More informationSingle cell experiments and Gillespie s algorithm
Single cell experiments and Gillespie s algorithm Fernand Hayot Department of Neurology, Mount Sinai School of Medicine, New York, NY, 10029 email: fernand.hayot@mssm.edu August 18, 2008 1 Introduction
More informationMultiple Choice Review- Eukaryotic Gene Expression
Multiple Choice Review- Eukaryotic Gene Expression 1. Which of the following is the Central Dogma of cell biology? a. DNA Nucleic Acid Protein Amino Acid b. Prokaryote Bacteria - Eukaryote c. Atom Molecule
More informationRegulation of gene Expression in Prokaryotes & Eukaryotes
Regulation of gene Expression in Prokaryotes & Eukaryotes 1 The trp Operon Contains 5 genes coding for proteins (enzymes) required for the synthesis of the amino acid tryptophan. Also contains a promoter
More information