BioControl - Week 6, Lecture 1

Size: px
Start display at page:

Download "BioControl - Week 6, Lecture 1"

Transcription

1 BioControl - Week 6, Lecture 1 Goals of this lecture Large metabolic networks organization Design principles for small genetic modules - Rules based on gene demand - Rules based on error minimization Suggested readings The large-scale organization of metabolic networks H Jeong, B Tombor, R Albert, ZN Oltvai, AL Barabási - Nature, 2000 Some protein interaction networks do not exhibit power law statistics Tanaka, R, Yi, TM and Doyle J Design principles for elementary gene circuits, Savageau MA, Chaos 2001 Rules for biological regulation based on error minimization Shinar G, Dekel E, Tlusty T and Alon U, PNAS 2006 Elisa Franco, Caltech 1

2 Looking for design principles LARGE NETWORK ORGANIZATION MODULES complexity Are there design principles in nature? If so, are they compatible with general engineering principles? If we were to design new networks from scratch, what design principles should we use? Elisa Franco, Caltech 2

3 Biological networks Metabolic (protein-protein) Genetic Interactions between enzymes and metabolites in the A. thaliana citric acid cycle. Enzymes and metabolites are the red dots and interactions between them are the lines. Systematic mapping of genetic interactions in C. elegans identifies common modifiers of diverse signaling pathways Lehner B, Crombie C, Tischler J, Fortunato A, Fraser AG Nat Genet Elisa Franco, Caltech 3

4 Metabolic networks organization The large-scale organization of metabolic networks H Jeong, B Tombor, R Albert, ZN Oltvai, AL Barabási - Nature, 2000 Random network theory (Erdös-Rény) Scale-free network (power-law) Connectivity (empirically measured) P (k) =k γ Connectivity: Poisson RV <k> <k>k P (k) =e k! Internet, social networks... and metabolic networks? Elisa Franco, Caltech 4

5 Metabolic networks are scale free The large-scale organization of metabolic networks H Jeong, B Tombor, R Albert, ZN Oltvai, AL Barabási - Nature, 2000 Numerical analysis of IWT database, across 43 organisms Metabolic pathway present if enzymes annotated in genome P(k) derived by histograms S1 S2 E1 = P1 P2 substrates enz-sub complex products E. coli A. fulgidus Nodes: substrates/enz/complexes/ products Links: metabolic reactions in-coming links (substrate=educt) vs out-going links (substrate=product) C. elegans Average over 43 organisms Elisa Franco, Caltech 5

6 Metabolic networks are scale free The large-scale organization of metabolic networks H Jeong, B Tombor, R Albert, ZN Oltvai, AL Barabási - Nature, 2000 Small-world networks Hubs, conserved across species (4% of all substrates considered) Distance between substrates is bounded Distance is robust wrt random node elimination Archaea Bacteria Eukaryotes Elimination of: M=60 => elimination of 8% of the total substrates in E. coli Elisa Franco, Caltech 6

7 ... aren t they? Some protein interaction data do not exhibit power law statistics Tanaka, R, Yi, TM and Doyle J Errors arise in frequency plots (differentiation), that do not arise in cumulative plots. P (X >x)=ce αx p(x) =c(e αx e α(x+1) )=c α e αx Discrete CDF and PDF have analytically compatible formulas, but binning and discrete frequency counting can alter the outcome. Cumulative ranking: exponential Frequency plot: incorrectly appears as a power-law α =0.1, x>10 y=-sort(floor(10*(-1+log(rand(1,n))))) loglog(y,1:n) u=unique(y); L=length(u); f=0*u; for k=1:l, f(k)=sum(y==u(k)); end; loglog(u,f) Elisa Franco, Caltech 7

8 Hub genes experimentally found in eukaryotes Systematic mapping of genetic interactions in C. elegans identifies common modifiers of diverse signaling pathways Lehner B et al Nat Genet Genetic networks seem to follow a scale-free node distribution in C. elegans Method: inhibit genes with RNAi target genes: transcription/signaling in development e.g. EGF/MAPK pathway Results: The study revealed new interactions of the EGF pathway Few hub genes, related to chromatin modification/ organization Hubs may be important to understand genetic diseases Elisa Franco, Caltech 8

9 Genetic networks: levels of design Design principles for elementary gene circuits, Savageau MA, Chaos 2001 Transcription unit: fan in/fan out of signals Modulators + Promoter + Genes + Terminator M1 M2 P G1 G2 T Mode of control: Positive - genes in high demand Negative - genes in low demand Verified in E. coli catabolic systems: - positive control if nutrient is common - negative control if nutrient is rare - antagonistic functions (e.g. biosynthesis and degradation) : opposite regulations - aligned functions (e.g. transport layers): same regulatory mode Input signaling: extra-cellular signals or network signals transmitted to the transcription unit Elisa Franco, Caltech 9

10 Genetic networks: levels of design Design principles for elementary gene circuits, Savageau MA, Chaos 2001 Logic unit: number of modulator sites and their logical combination M1 M2 P G Expression cascade: Gene->mRNA->Protein->Metabolite R1 R2 AND yes no OFF yes no ON no yes OFF no no OFF Connectivity: inputs and outputs of transcription units -> for coordinated gene expression - Autogenous regulation - Elementary gene circuits - motifs - E. coli: 2-3 inputs per transcription unit, some t.u. have up to 50 outputs - Operons (polycistronic mrna) - Regulons (separate txn units with same regulatory domain) Elisa Franco, Caltech 10

11 Comparing different designs Design principles for elementary gene circuits, Savageau MA, Chaos 2001 Deterministic continuous models are (usually) a good choice to gain intuition on design principles. Suggested method: Restrict differences to a single specific process Models are internally equivalent Parameters associated with the different process (to be compared) are free Reduce degrees of freedom by external equivalence Eliminate arbitrary model differences If symbolic solutions to the differential equations are available, no numerical analysis is required. Elisa Franco, Caltech 11

12 Comparing different designs Design principles for elementary gene circuits, Savageau MA, Chaos 2001 Generalized mass action models (GMA) allow in some cases explicit solutions dx i dt = k j complexes producing Xi α ik X g ijk j complexes in which Xi is a reactant β ik X h ijk j k j Synergistic (S-)systems dx i = α i X g ij j β i dt j Explicit solution for equilibria Explicit conditions for local stability j X h ij j Logarithmically differentiable functions IDEA for local linear analysis: Taylor series expansion: log Z = f(log X) log Z = log Z 0 + j log Z log X j Z 0 log X j X j0 In cartesian coordinates: (exponentiating) Z = a j X α j j Elisa Franco, Caltech 12

13 Example Design principles for elementary gene circuits, Savageau MA, Chaos 2001 Study g13, g43 (inducer) and g15, g45 (regulator). Other parameters held equal (internal equiv.) Steady states compatible for all possible models (external equiv.) mrna Enzyme Inducer mrna Regulator Elisa Franco, Caltech 13

14 Rules for biological regulation based on error minimization Shinar G, Dekel E, Tlusty T and Alon U, PNAS not just demand of genes (Savageau) TF Transcriptional control Activator or inhibitor M P G If transcription factor not bound to its site => Site exposed to nonspecific binding Binding of inactive TF Cross-talks from other TFs Lateral gene transfer... => ERRORS: reduction of fitness: error-load p - fraction of time a gene is expressed / in demand Fitness reduction Repressor E R = p f 1 1- high expression state Activator E A =(1 p) f 0 0- low expression state Elisa Franco, Caltech 14

15 Rules for biological regulation based on error minimization Repressors are advantageous when the demand is lower than a threshold: p< 1 1+ f 1 f 0 Replacement of mode of control due to selection pressure after random mutation E coli selection threshold s min minimum fitness advantage for a mutant to take over repressor fixation activator fixation E A E R >s min E R E A >s min Elisa Franco, Caltech 15

16 Summary Networks - design principles - Metabolic and genetic networks seem to be scale-free Genetic modules and design of regulatory interaction - Rules for demand of gene expression - Rules for error minimization NEXT LECTURE - Network motifs - Dynamic role of motifs Elisa Franco, Caltech 16

Self Similar (Scale Free, Power Law) Networks (I)

Self Similar (Scale Free, Power Law) Networks (I) Self Similar (Scale Free, Power Law) Networks (I) E6083: lecture 4 Prof. Predrag R. Jelenković Dept. of Electrical Engineering Columbia University, NY 10027, USA {predrag}@ee.columbia.edu February 7, 2007

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

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

Biological Networks. Gavin Conant 163B ASRC

Biological Networks. Gavin Conant 163B ASRC Biological Networks Gavin Conant 163B ASRC conantg@missouri.edu 882-2931 Types of Network Regulatory Protein-interaction Metabolic Signaling Co-expressing General principle Relationship between genes Gene/protein/enzyme

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

Lecture 8: Temporal programs and the global structure of transcription networks. Chap 5 of Alon. 5.1 Introduction

Lecture 8: Temporal programs and the global structure of transcription networks. Chap 5 of Alon. 5.1 Introduction Lecture 8: Temporal programs and the global structure of transcription networks Chap 5 of Alon 5. Introduction We will see in this chapter that sensory transcription networks are largely made of just four

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

Proteomics. Yeast two hybrid. Proteomics - PAGE techniques. Data obtained. What is it?

Proteomics. Yeast two hybrid. Proteomics - PAGE techniques. Data obtained. What is it? Proteomics What is it? Reveal protein interactions Protein profiling in a sample Yeast two hybrid screening High throughput 2D PAGE Automatic analysis of 2D Page Yeast two hybrid Use two mating strains

More information

56:198:582 Biological Networks Lecture 9

56:198:582 Biological Networks Lecture 9 56:198:582 Biological Networks Lecture 9 The Feed-Forward Loop Network Motif Subgraphs in random networks We have discussed the simplest network motif, self-regulation, a pattern with one node We now consider

More information

Biological Networks Analysis

Biological Networks Analysis Biological Networks Analysis Degree Distribution and Network Motifs Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein Networks: Networks vs. graphs A collection of nodesand

More information

The architecture of complexity: the structure and dynamics of complex networks.

The architecture of complexity: the structure and dynamics of complex networks. SMR.1656-36 School and Workshop on Structure and Function of Complex Networks 16-28 May 2005 ------------------------------------------------------------------------------------------------------------------------

More information

Simplicity is Complexity in Masquerade. Michael A. Savageau The University of California, Davis July 2004

Simplicity is Complexity in Masquerade. Michael A. Savageau The University of California, Davis July 2004 Simplicity is Complexity in Masquerade Michael A. Savageau The University of California, Davis July 2004 Complexity is Not Simplicity in Masquerade -- E. Yates Simplicity is Complexity in Masquerade One

More information

Design principles for elementary gene circuits: Elements, methods, and examples

Design principles for elementary gene circuits: Elements, methods, and examples CHAOS VOLUME 11, NUMBER 1 MARCH 2001 Design principles for elementary gene circuits: Elements, methods, and examples Michael A. Savageau a) Department of Microbiology and Immunology, University of Michigan

More information

networks in molecular biology Wolfgang Huber

networks in molecular biology Wolfgang Huber networks in molecular biology Wolfgang Huber networks in molecular biology Regulatory networks: components = gene products interactions = regulation of transcription, translation, phosphorylation... Metabolic

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

Erzsébet Ravasz Advisor: Albert-László Barabási

Erzsébet Ravasz Advisor: Albert-László Barabási Hierarchical Networks Erzsébet Ravasz Advisor: Albert-László Barabási Introduction to networks How to model complex networks? Clustering and hierarchy Hierarchical organization of cellular metabolism The

More information

Written Exam 15 December Course name: Introduction to Systems Biology Course no

Written Exam 15 December Course name: Introduction to Systems Biology Course no Technical University of Denmark Written Exam 15 December 2008 Course name: Introduction to Systems Biology Course no. 27041 Aids allowed: Open book exam Provide your answers and calculations on separate

More information

Introduction. Gene expression is the combined process of :

Introduction. 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 information

Introduction to Bioinformatics

Introduction 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 information

L3.1: Circuits: Introduction to Transcription Networks. Cellular Design Principles Prof. Jenna Rickus

L3.1: Circuits: Introduction to Transcription Networks. Cellular Design Principles Prof. Jenna Rickus 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 information

Bioinformatics 2. Yeast two hybrid. Proteomics. Proteomics

Bioinformatics 2. Yeast two hybrid. Proteomics. Proteomics GENOME Bioinformatics 2 Proteomics protein-gene PROTEOME protein-protein METABOLISM Slide from http://www.nd.edu/~networks/ Citrate Cycle Bio-chemical reactions What is it? Proteomics Reveal protein Protein

More information

Name Period The Control of Gene Expression in Prokaryotes Notes

Name 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 information

Regulation of Gene Expression

Regulation 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 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

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

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

Understanding Science Through the Lens of Computation. Richard M. Karp Nov. 3, 2007

Understanding Science Through the Lens of Computation. Richard M. Karp Nov. 3, 2007 Understanding Science Through the Lens of Computation Richard M. Karp Nov. 3, 2007 The Computational Lens Exposes the computational nature of natural processes and provides a language for their description.

More information

56:198:582 Biological Networks Lecture 10

56:198:582 Biological Networks Lecture 10 56:198:582 Biological Networks Lecture 10 Temporal Programs and the Global Structure The single-input module (SIM) network motif The network motifs we have studied so far all had a defined number of nodes.

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

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

V14 Graph connectivity Metabolic networks

V14 Graph connectivity Metabolic networks V14 Graph connectivity Metabolic networks In the first half of this lecture section, we use the theory of network flows to give constructive proofs of Menger s theorem. These proofs lead directly to algorithms

More information

GENE REGULATION AND PROBLEMS OF DEVELOPMENT

GENE 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 information

Regulation of Gene Expression

Regulation of Gene Expression Chapter 18 Regulation of Gene Expression Edited by Shawn Lester PowerPoint Lecture Presentations for Biology Eighth Edition Neil Campbell and Jane Reece Lectures by Chris Romero, updated by Erin Barley

More information

Measuring TF-DNA interactions

Measuring TF-DNA interactions Measuring TF-DNA interactions How is Biological Complexity Achieved? Mediated by Transcription Factors (TFs) 2 Regulation of Gene Expression by Transcription Factors TF trans-acting factors TF TF TF TF

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

Cell biology traditionally identifies proteins based on their individual actions as catalysts, signaling

Cell biology traditionally identifies proteins based on their individual actions as catalysts, signaling Lethality and centrality in protein networks Cell biology traditionally identifies proteins based on their individual actions as catalysts, signaling molecules, or building blocks of cells and microorganisms.

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

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

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

Regulation of gene expression. Premedical - Biology

Regulation of gene expression. Premedical - Biology Regulation of gene expression Premedical - Biology Regulation of gene expression in prokaryotic cell Operon units system of negative feedback positive and negative regulation in eukaryotic cell - at any

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

Computational Cell Biology Lecture 4

Computational 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 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

Biological Pathways Representation by Petri Nets and extension

Biological Pathways Representation by Petri Nets and extension Biological Pathways Representation by and extensions December 6, 2006 Biological Pathways Representation by and extension 1 The cell Pathways 2 Definitions 3 4 Biological Pathways Representation by and

More information

CHAPTER 13 PROKARYOTE GENES: E. COLI LAC OPERON

CHAPTER 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 information

Big Idea 3: Living systems store, retrieve, transmit and respond to information essential to life processes. Tuesday, December 27, 16

Big Idea 3: Living systems store, retrieve, transmit and respond to information essential to life processes. Tuesday, December 27, 16 Big Idea 3: Living systems store, retrieve, transmit and respond to information essential to life processes. Enduring understanding 3.B: Expression of genetic information involves cellular and molecular

More information

Lecture 6: The feed-forward loop (FFL) network motif

Lecture 6: The feed-forward loop (FFL) network motif Lecture 6: The feed-forward loop (FFL) network motif Chapter 4 of Alon x 4. Introduction x z y z y Feed-forward loop (FFL) a= 3-node feedback loop (3Loop) a=3 Fig 4.a The feed-forward loop (FFL) and the

More information

Chapter 7: Metabolic Networks

Chapter 7: Metabolic Networks Chapter 7: Metabolic Networks 7.1 Introduction Prof. Yechiam Yemini (YY) Computer Science epartment Columbia University Introduction Metabolic flux analysis Applications Overview 2 1 Introduction 3 Metabolism:

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

Types of biological networks. I. Intra-cellurar networks

Types of biological networks. I. Intra-cellurar networks Types of biological networks I. Intra-cellurar networks 1 Some intra-cellular networks: 1. Metabolic networks 2. Transcriptional regulation networks 3. Cell signalling networks 4. Protein-protein interaction

More information

Chapter 8: The Topology of Biological Networks. Overview

Chapter 8: The Topology of Biological Networks. Overview Chapter 8: The Topology of Biological Networks 8.1 Introduction & survey of network topology Prof. Yechiam Yemini (YY) Computer Science Department Columbia University A gallery of networks Small-world

More information

Supplementary Information

Supplementary Information Supplementary Information For the article"comparable system-level organization of Archaea and ukaryotes" by J. Podani, Z. N. Oltvai, H. Jeong, B. Tombor, A.-L. Barabási, and. Szathmáry (reference numbers

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

GLOBEX Bioinformatics (Summer 2015) Genetic networks and gene expression data

GLOBEX 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 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

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

Course plan Academic Year Qualification MSc on Bioinformatics for Health Sciences. Subject name: Computational Systems Biology Code: 30180

Course plan Academic Year Qualification MSc on Bioinformatics for Health Sciences. Subject name: Computational Systems Biology Code: 30180 Course plan 201-201 Academic Year Qualification MSc on Bioinformatics for Health Sciences 1. Description of the subject Subject name: Code: 30180 Total credits: 5 Workload: 125 hours Year: 1st Term: 3

More information

Department of Pathology, Northwestern University Medical School, Chicago, IL 60611

Department of Pathology, Northwestern University Medical School, Chicago, IL 60611 The large-scale organization of metabolic networks H. Jeong 1, B. Tombor 2, R. Albert 1, Z. N. Oltvai 2 and A.-L. Barabási 1 1 Department of Physics, University of Notre Dame, Notre Dame, IN 46556, and

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

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

Evidence for dynamically organized modularity in the yeast protein-protein interaction network Evidence for dynamically organized modularity in the yeast protein-protein interaction network Sari Bombino Helsinki 27.3.2007 UNIVERSITY OF HELSINKI Department of Computer Science Seminar on Computational

More information

Complex (Biological) Networks

Complex (Biological) Networks Complex (Biological) Networks Today: Measuring Network Topology Thursday: Analyzing Metabolic Networks Elhanan Borenstein Some slides are based on slides from courses given by Roded Sharan and Tomer Shlomi

More information

Chapter 18: Control of Gene Expression

Chapter 18: Control of Gene Expression Chapter 18: Control of Gene Expression 海洋生物研究所 曾令銘 海事大樓 426 室分機 : 5326 Differential Expression of Genes Prokaryotes and eukaryotes precisely regulate gene expression in response to environmental conditions

More information

Bacterial Genetics & Operons

Bacterial 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 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

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

UNIT 6 PART 3 *REGULATION USING OPERONS* Hillis Textbook, CH 11

UNIT 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 information

Introduction to Bioinformatics

Introduction 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 information

Network motifs in the transcriptional regulation network (of Escherichia coli):

Network motifs in the transcriptional regulation network (of Escherichia coli): Network motifs in the transcriptional regulation network (of Escherichia coli): Janne.Ravantti@Helsinki.Fi (disclaimer: IANASB) Contents: Transcription Networks (aka. The Very Boring Biology Part ) Network

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

Unit 3: Control and regulation Higher Biology

Unit 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 information

7.32/7.81J/8.591J: Systems Biology. Fall Exam #1

7.32/7.81J/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 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

Emergence of gene regulatory networks under functional constraints

Emergence of gene regulatory networks under functional constraints under functional constraints Institute of Physics, Jagiellonian University, Kraków, Poland in collaboration with Zdzislaw Burda, Andre Krzywicki and Olivier C. Martin 30 November 2012, Leipzig, CompPhys12

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

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 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:

More information

Honors Biology Reading Guide Chapter 11

Honors 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 information

32 Gene regulation, continued Lecture Outline 11/21/05

32 Gene regulation, continued Lecture Outline 11/21/05 32 Gene regulation, continued Lecture Outline 11/21/05 Review the operon concept Repressible operons (e.g. trp) Inducible operons (e.g. lac) Positive regulation of lac () Practice applying the operon concept

More information

Newly made RNA is called primary transcript and is modified in three ways before leaving the nucleus:

Newly made RNA is called primary transcript and is modified in three ways before leaving the nucleus: m Eukaryotic mrna processing Newly made RNA is called primary transcript and is modified in three ways before leaving the nucleus: Cap structure a modified guanine base is added to the 5 end. Poly-A tail

More information

Bi 1x Spring 2014: LacI Titration

Bi 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 information

Control of Prokaryotic (Bacterial) Gene Expression. AP Biology

Control of Prokaryotic (Bacterial) Gene Expression. AP Biology Control of Prokaryotic (Bacterial) Gene Expression Figure 18.1 How can this fish s eyes see equally well in both air and water? Aka. Quatro ojas Regulation of Gene Expression: Prokaryotes and eukaryotes

More information

Basic modeling approaches for biological systems. Mahesh Bule

Basic 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 information

16 CONTROL OF GENE EXPRESSION

16 CONTROL OF GENE EXPRESSION 16 CONTROL OF GENE EXPRESSION Chapter Outline 16.1 REGULATION OF GENE EXPRESSION IN PROKARYOTES The operon is the unit of transcription in prokaryotes The lac operon for lactose metabolism is transcribed

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

SPA for quantitative analysis: Lecture 6 Modelling Biological Processes

SPA for quantitative analysis: Lecture 6 Modelling Biological Processes 1/ 223 SPA for quantitative analysis: Lecture 6 Modelling Biological Processes Jane Hillston LFCS, School of Informatics The University of Edinburgh Scotland 7th March 2013 Outline 2/ 223 1 Introduction

More information

The Eukaryotic Genome and Its Expression. The Eukaryotic Genome and Its Expression. A. The Eukaryotic Genome. Lecture Series 11

The Eukaryotic Genome and Its Expression. The Eukaryotic Genome and Its Expression. A. The Eukaryotic Genome. Lecture Series 11 The Eukaryotic Genome and Its Expression Lecture Series 11 The Eukaryotic Genome and Its Expression A. The Eukaryotic Genome B. Repetitive Sequences (rem: teleomeres) C. The Structures of Protein-Coding

More information

Chapter 16 Lecture. Concepts Of Genetics. Tenth Edition. Regulation of Gene Expression in Prokaryotes

Chapter 16 Lecture. Concepts Of Genetics. Tenth Edition. Regulation of Gene Expression in Prokaryotes Chapter 16 Lecture Concepts Of Genetics Tenth Edition Regulation of Gene Expression in Prokaryotes Chapter Contents 16.1 Prokaryotes Regulate Gene Expression in Response to Environmental Conditions 16.2

More information

Cellular Biophysics SS Prof. Manfred Radmacher

Cellular Biophysics SS Prof. Manfred Radmacher SS 20007 Manfred Radmacher Ch. 12 Systems Biology Let's recall chemotaxis in Dictiostelium synthesis of camp excretion of camp external camp gradient detection cell polarity cell migration 2 Single cells

More information

Random Boolean Networks

Random Boolean Networks Random Boolean Networks Boolean network definition The first Boolean networks were proposed by Stuart A. Kauffman in 1969, as random models of genetic regulatory networks (Kauffman 1969, 1993). A Random

More information

Analysis of Complex Systems

Analysis of Complex Systems Analysis of Complex Systems Lecture 1: Introduction Marcus Kaiser m.kaiser@ncl.ac.uk www.dynamic-connectome.org Preliminaries - 1 Lecturers Dr Marcus Kaiser, m.kaiser@ncl.ac.uk Practicals Frances Hutchings

More information

Biology 112 Practice Midterm Questions

Biology 112 Practice Midterm Questions Biology 112 Practice Midterm Questions 1. Identify which statement is true or false I. Bacterial cell walls prevent osmotic lysis II. All bacterial cell walls contain an LPS layer III. In a Gram stain,

More information

Lecture 18 June 2 nd, Gene Expression Regulation Mutations

Lecture 18 June 2 nd, Gene Expression Regulation Mutations Lecture 18 June 2 nd, 2016 Gene Expression Regulation Mutations From Gene to Protein Central Dogma Replication DNA RNA PROTEIN Transcription Translation RNA Viruses: genome is RNA Reverse Transcriptase

More information

Stochastic simulations

Stochastic 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 information

4. Why not make all enzymes all the time (even if not needed)? Enzyme synthesis uses a lot of energy.

4. 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 information

Analysis of Biological Networks: Network Robustness and Evolution

Analysis of Biological Networks: Network Robustness and Evolution Analysis of Biological Networks: Network Robustness and Evolution Lecturer: Roded Sharan Scribers: Sasha Medvedovsky and Eitan Hirsh Lecture 14, February 2, 2006 1 Introduction The chapter is divided into

More information

Gene Control Mechanisms at Transcription and Translation Levels

Gene Control Mechanisms at Transcription and Translation Levels Gene Control Mechanisms at Transcription and Translation Levels Dr. M. Vijayalakshmi School of Chemical and Biotechnology SASTRA University Joint Initiative of IITs and IISc Funded by MHRD Page 1 of 9

More information

Chapter 6- An Introduction to Metabolism*

Chapter 6- An Introduction to Metabolism* Chapter 6- An Introduction to Metabolism* *Lecture notes are to be used as a study guide only and do not represent the comprehensive information you will need to know for the exams. The Energy of Life

More information

Boolean models of gene regulatory networks. Matthew Macauley Math 4500: Mathematical Modeling Clemson University Spring 2016

Boolean models of gene regulatory networks. Matthew Macauley Math 4500: Mathematical Modeling Clemson University Spring 2016 Boolean models of gene regulatory networks Matthew Macauley Math 4500: Mathematical Modeling Clemson University Spring 2016 Gene expression Gene expression is a process that takes gene info and creates

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

Complex (Biological) Networks

Complex (Biological) Networks Complex (Biological) Networks Today: Measuring Network Topology Thursday: Analyzing Metabolic Networks Elhanan Borenstein Some slides are based on slides from courses given by Roded Sharan and Tomer Shlomi

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