EE430 Introduction to Systems Biology
|
|
- August Stewart
- 1 years ago
- Views:
Transcription
1 EE430 Introduction to Systems Biology Week 8 Course Notes Instructor: Bilge Karaçalı, PhD
2 Topics Optimal gene circuits Optimal protein expression levels Selection for regulation in variable environments Selection for regulatory modules EE430 Week 8 2
3 Selection for Regulation Everything about the structure and function of biomolecular mechanisms are subject to selection Anything observed beyond a random occurrence must confer a selection advantage Network motifs in gene expression and signal transduction networks The cells also operate in a constrained environment Resource constraints Nutrients Oxygen Metabolites Time constraints Realizing and maintaining regulatory constructs requires investment with non-negligible effort expenditures The question to be answered is this: When does it become beneficial to spend the effort to regulate? EE430 Week 8 3
4 Optimality of Gene Circuits Evolution operates by random changes to the genes The changes that offer extra benefits are likely to achieve fixation Wide-spread expression across the population after a few generations The non-beneficial changes are much less likely to achieve fixation Depletive changes are quickly selected against The rule of thumb: The mechanisms observed in cells must have accumulated through time specifically because they improved the likelihood of the organisms of surviving and leaving descendants The likelihood of leaving descendants is expressed as a fitness function Therefore, changes are more likely to be kept if they improve the fitness function EE430 Week 8 4
5 Fitness Optimization The evolutionary fitness of an organism is difficult to define explicitly Does not necessarily correspond to the physical strength or health The general approach is to take the average number of descendants as the fitness Works reasonably well for bacterial cells Awkward and severely inadequate for complex organisms Problems with defining evolutionary fitness based on the number of descendants in a human society EE430 Week 8 5
6 Fitness Optimization in Simple Organisms The changes that allow leaving more descendants are favored NB: Everything is, still and always, stochastic Making optimal use of the environmental resources is selected for with a high probability Because it improves the chances of leaving descendants Making optimal trade-offs also qualifies The stochastic nature of the selection process suggests a softer optimization than seeking the optimal configuration The configurations with sufficiently good performance survive Multiplicity of choices also provide alternative avenues of responding to environmental changes Never-ending cycle of variations Law of diminishing returns EE430 Week 8 6
7 Law of Diminishing Returns Cells do display a tendency for greater organization, but greater organization itself is ultimately not the goal Higher organization is costly Synthesis and maintenance of molecules involved in the regulatory mechanism Organization is worth the effort up until a point of maximal benefit for the effort spent After that level, more organization does not enjoy a selective advantage Same reasoning follows for the cost/benefit of intelligence in animals The law of diminishing returns in cellular organization Engine power vs. maximum speed (automotive industry) Detection rate vs. false alarm rate (pattern recognition) benefit cost EE430 Week 8 7
8 Optimal Protein Expression Expression levels of proteins are regulated by the biomolecular mechanism Gene transcription networks operating based on the input from the signal transduction networks The regulatory mechanism adjusts the expression of a protein at a specific level The level at which the expression is maintained ought to optimize the fitness of the organism The expression of a protein incurs a cost and provides a benefit To achieve an increase in the fitness The level itself is adjusted so that the benefit is worth the overall cost and the additional benefit is maximized Example: The Lac system EE430 Week 8 8
9 Optimal Expression of the LacZ Protein The LacZ gene is a component in the (E. coli) lactose metabolism The protein product of LacZ breaks down lactose for use as energy and carbon source The level of LacZ expression in the cell balances two outcomes: The benefit The cost Both the benefit and the cost are measured in terms of the increase and the decrease observed in a fitness function: the growth rate EE430 Week 8 9
10 The Benefit Function of LacZ The benefit of LacZ is the relative increase in the growth rate per additional amount of LacZ Hence, the benefit is proportional to the rate at which LacZ breaks down lactose b( Z,L) = δ Z L/(K+L) where Z denotes the number of LacZ proteins δ denotes maximal growth rate advantage per LacZ protein K is the Michaelis constant L is the amount of lactose present EE430 Week 8 10
11 The Benefit Function of LacZ Experimental setup: The cells are induced for maximal expression of LacZ Z WT is maximal The growth rate measured for varying levels of external lactose The maximum benefit in saturating amounts of lactose was observed to be around 17% The values for the parameters δ and K were determined as δ = 0.17 Z WT -1 K = 0.4mM b( Z,L) L EE430 Week 8 11
12 The Cost Function of LacZ The cost of LacZ was also measured experimentally The cell was induced to express LacZ at specific levels in complete absence of lactose The cost was measured as the relative reduction in the growth rate The cost of LacZ was determined to be a monotonically increasing function of LacZ expression Synthesis of LacZ reduces resources that otherwise would be available for other beneficial mechanisms A reasonably accurate model for the cost that incorporates this nonlinear behavior is c( Z ) = η Z /(1+ Z /M) where M denotes the level at which Z production overwhelms the cell, and η is a scalar constant When Z M, the cell begins to halt all other cellular operations and recruits all resources toward LacZ synthesis EE430 Week 8 12
13 The Cost Function of LacZ By experimental evidence η = 0.02 Z WT M = The cost when Z = Z WT is about 4.5% For small Z, the cost is approximately linear with a slope of η As Z grows, the monotonically increasing behavior becomes more apparent Note that the cost is measured by the relative reduction in the growth rate c( Z ) Z / Z WT EE430 Week 8 13
14 The Cost-Benefit Analysis of LacZ For a given external lactose concentration of L, the optimal Z level is determined by maximizing the fitness function The fitness function is defined by the relative growth rate with the benefit and the cost of expressing Z at a given level f( Z ) = b( Z,L) c( Z ) = δ Z L/(K+L) η Z /(1+ Z /M) = Z (δl/(k+l) η/(1+ Z /M)) Note that this function does have a maximum at f/ ( Z ) = δl/(k+l) η/(1+ Z /M) + Z η/(m(1+ Z /M) 2 ) =0 The benefit grows linearly The cost grows exponentially Thus, however small it starts, the cost eventually grows much larger than the benefit The level of Z that maximizes this function provides the optimal level of LacZ protein The maximal value of the fitness function itself is the relative growth rate achieved at the optimal Z EE430 Week 8 14
15 The Cost-Benefit Analysis of LacZ L=0.05 L=0.10 L=0.30 L=0.60 L=0.90 f( Z ) Z / Z WT EE430 Week 8 15
16 Remarks The cost-benefit analysis of LacZ reveals several behavior types When there is little or no LacZ, the cells produce no LacZ at all The benefit of LacZ does not exceed its cost If the cells are maintained for sufficiently many generations, they would tend to loose the useless lactose metabolism When lactose is present in significant amounts, the cells determine an optimal expression level for LacZ with respect to the maximal wild-type rate This is achieved in a few hundreds of generations The model also predicts that if the cells are kept in environments with lactose concentrations consistently higher than 0.6, they would evolve a higher wild-type expression level EE430 Week 8 16
17 Optimal Regulation in Variable Environments Regulation endows the biomolecular mechanism with the ability to determine when a gene product is needed But not all genes are regulated Some genes are always expressed Other (hypothetical) genes are never expressed (and have thus been lost through generations) The answer to the question of whether a gene is regulated or expressed at all is again subject to costbenefit analysis If a gene is always beneficial or needed, it would always be expressed without a need for a regulatory mechanism On the other hand, if a gene is never beneficial, it is not needed at all and would gradually be lost through generations Regulation would be selected for if determining when it is beneficial would improve the fitness EE430 Week 8 17
18 Optimal Regulation in Variable Environments The cost-benefit analysis for maintaining a regulatory mechanism: Regulation entails a cost Synthesis and maintenance of all molecules taking part in a regulatory mechanism requires expenditure in raw materials, energy, and processing time The cost is compensated for if the regulated system provides sufficient benefits when the favorable conditions are present EE430 Week 8 18
19 Optimal Regulation in Variable Environments Consider a gene product Z Let p be the fraction of time when the conditions in which Z is utilized are present Hence, p signifies the demand for the gene product Z The fitness with respect to Z is then given by f Z = pb pc r where b and c denote the benefit and the cost of utilizing Z during the favorable times, and r denotes the cost of maintaining its regulatory mechanism EE430 Week 8 19
20 Optimal Regulation in Variable Environments The alternatives to regulation are Constant expression without regulation: f Z = pb c No expression: f Z = 0 Regulation will be selected when pb pc r > pb c and pb pc r > 0 Constant expression will be selected when pb c > pb pc r and pb c > 0 No expression will be selected when 0 > pb pc r and 0 > pb c EE430 Week 8 20
21 Optimal Regulation in Variable Environments b=b 0, c=c 0 (b 0 > c 0 ) no expression constant expression r regulation p EE430 Week 8 21
22 Selection for Regulatory Modules A regulatory system is selected for as long as the net benefit in favorable times compensates for the cost of maintaining it But among different regulatory mechanisms, some seem to be selected for instead of others These modules are recognizable as network motifs Network motifs confer specific benefits to the network Quicker response Sign-sensitive delay Temporal programming Their benefit must be calculated with respect to those of alternative (simpler) regulatory mechanisms EE430 Week 8 22
23 Selection for C1-FFL Consider two alternatives for expressing the gene Z only when the signals for both genes X and Y are present Simple regulation The signals trigger the expression of their respective genes When both genes pass their respective thresholds, the gene Z starts to be expressed Feed-forward loop The signals for the gene X triggers its expression Only after the gene product X is above a threshold does the expression of Y begins The expression of the gene Z begins after a delay S X X Y Z S Y S X feed-forward loop simple regulation X Y S Y EE430 Week 8 23 Z
24 Selection for C1-FFL Suppose that the expression of the gene product Z has a certain relative fitness f t The dependence on time indicates the varying cost-benefit structure of Z as the level of Z rises in response to expression signals Let the triggering of the Z expression for D units of time be represented by a pulse of duration D From the time when S X and S Y are both turned on until S X is turned off In case of a pulse of duration D, the total acquired relative fitness φ D can be computed by φ D = f t dd 0 Considering that pulses can have random durations, the average relative fitness is Φ = E φ D = φ D p D dd 0 where p D is the probability distribution associated with the pulse durations D D EE430 Week 8 24
25 Selection for C1-FFL For simple regulation: Supposing that the gene product Z becomes available after a time delay T following the signal, the fitness function is given by f t = b 1 t T > 0 ββ b is the benefit, β is the production rate, and η is the cost per produced protein product The accumulated fitness from a pulse of duration D is then φ D = b D T βββ if D T, and φ D = βββ otherwise The corresponding average accumulated benefit is given by Φ ss = b D T p D dd T βββp D dd 0 EE430 Week 8 25
26 Selection for C1-FFL For feed-forward loop regulation: The expression of Z begins after a delay of τ, and the gene product Z becomes available T + τ times after the pulse begins The associated fitness function becomes f t = b 1 t T τ > 0 ββ 1 t τ > 0 The accumulated fitness becomes φ D = b D T τ ββ(d τ) if D T + τ, φ D = ββ(d τ) if τ < D < T + τ, and φ D = 0 if D < τ The corresponding average accumulated fitness is then given by Φ fff = T+τ b D T τ p D dd ββ D τ p D dd τ EE430 Week 8 26
27 Selection for C1-FFL The selection between simple regulation and feedforward loop regulation is determined based on the distribution p D of pulse durations If all pulses are very short, the simple regulation would incur costs with no benefits, while the feed forward loop would prevent these costs If all pulses last very long, then the simple regulation would be preferred since the feed-forward loop would always loose the benefits to be acquired during the extra delay τ If, on the other hand, the probability mass in p D is accumulated on a combination of very short pulses and very long ones, then the feed-forward loop would be selected for Due to its ability to filter out the costly and non-beneficial short pulses EE430 Week 8 27
28 Selection for C1-FFL Example: Consider the probability distribution p D = 1 λ p 1 D + λp 2 D where the elementary distributions p 1 D and p 2 D are shown to the right By varying the level of λ, we can compute the two average accumulated fitness functions Φ ss and Φ fff The values of λ for which one is greater than the other then determine when the former would be selected for At the expense of the other EE430 Week 8 28
29 Selection for C1-FFL b = 1.25ββ EE430 Week 8 29
30 Summary Bacterial cells must make optimal use of the resources available to them and maximize their growth rates Otherwise, they loose out to the others who do manage to maximize their growth rates in the same conditions and thus come to dominate the populations By this token, among all possible ways of carrying out a required operation, the one that is most suitable in the given environmental conditions are favored Maintaining an optimal protein expression level Regulating gene expression Identifying the optimal regulatory mechanism The principle of optimality transcends organisms and species All cells, in single-cellular and multi-cellular organisms are endowed with similar mechanisms to ensure effective use of limited resources This suggests Common lineage Convergent evolution EE430 Week 8 30
A Brief Introduction to Systems Biology: Gene Regulatory Networks Rajat K. De
A Brief Introduction to Systems Biology: Gene Regulatory Networks Rajat K. De Machine Intelligence Unit, Indian Statistical Institute 203 B. T. Road Kolkata 700108 email id: rajat@isical.ac.in 1 Informatics
Feed Forward Loops in Biological Systems
Feed Forward Loops in Biological Systems Dr. M. Vijayalakshmi School of Chemical and Biotechnology SASTRA University Joint Initiative of IITs and IISc Funded by MHRD Page 1 of 7 Table of Contents 1 INTRODUCTION...
Understanding the dynamics and function of cellular networks
Understanding the dynamics and function of cellular networks Cells are complex systems functionally diverse elements diverse interactions that form networks signal transduction-, gene regulatory-, metabolic-
transcription networks
Global l structure t of sensory transcription networks 02/7/2012 Counting possible graph patterns in an n-node graph One 1-node Three 2-node graph pattern graph patterns Thirteen 3-node graph patterns
Transcriptional Regulatory Networks 01/12/12
Transcriptional Regulatory Networks 01/12/12 Molecular interaction networks change constantly All the phenomena of life are the manifestation of the interactions of molecules that constitute the organism.
Gene regulation in prokaryotes
GENE REGULATION 1 GENE REGULATION Gene regulation refers to the ability of cells to control their level of gene expression Structural genes are regulated so proteins are only produced at certain times
Chapter 7 Gene Regulation in Prokaryotes
Chapter 7 Gene Regulation in Prokaryotes 1 Outline 1. Principles of Transcriptional Regulation 2. Regulation of Transcription Initiation: Lac Operon 3. The Case of Phage λ: Layers of Regulation 2 Part
Bioinformatics: Network Analysis
Bioinformatics: Network Analysis Graph-theoretic Properties of Biological Networks COMP 572 (BIOS 572 / BIOE 564) - Fall 2013 Luay Nakhleh, Rice University 1 Outline Architectural features Motifs, modules,
What makes cells different from each other? How do cells respond to information from environment?
What makes cells different from each other? How do cells respond to information from environment? Regulation of: - Transcription - prokaryotes - eukaryotes - mrna splicing - mrna localisation and translation
Overview: Conducting the Genetic Orchestra. Prokaryotes and eukaryotes alter gene expression in response to their changing environment
Overview: Conducting the Genetic Orchestra Prokaryotes and eukaryotes alter gene expression in response to their changing environment In multicellular eukaryotes, gene expression regulates development
ANIMAL PHYSIOLOGY. BIOL 3151: Principles of Animal Physiology
ANIMAL PHYSIOLOGY BIOL 3151: Principles of Animal Physiology Dr. Tyler Evans Email: tyler.evans@csueastbay.edu Phone: 510-885-3475 Office Hours: M,W 10:30-12:00 or appointment Website: http://evanslabcsueb.weebly.com/
Many Routes to Pluripotency and the Frequency of Individual Solutions
Many Routes to Pluripotency and the Frequency of Individual Solutions A review of A Model for Genetic and Epigenetic Regulatory Networks Identifies Rare Pathways for Transcription Factor Induced Pluripotency.
Induction of Enzyme Activity in Bacteria:The Lac Operon. Preparation for Laboratory: Web Tutorial - Lac Operon - submit questions
Induction of Enzyme Activity in Bacteria:The Lac Operon Preparation for Laboratory: Web Tutorial - Lac Operon - submit questions I. Background: For the last week you explored the functioning of the enzyme
Gene Regulation. Chapter 16. Levels of Gene Regulation. Bacterial Gene Regulation. Genes vs. Regulatory Elements. Unicellular flexibility
Gene Regulation Chapter 16 Control of Gene Expression (Part 2) Unicellular flexibility Genes turned on and off in response to environment Multicellular specialization Genes for one cell type are not expressed
Perceptron Learning Algorithm
Perceptron Learning Algorithm Department of Statistics The Pennsylvania State University Email: jiali@stat.psu.edu Separating Hyperplanes Construct linear decision boundaries that explicitly try to separate
Gene expression. Most genes are not expressed at a particular time. BIO 5099: Molecular Biology for Computer Scientists (et al)
BIO 5099: Molecular Biology for Computer Scientists (et al) Lecture 14: Gene expression and regulation http://compbio.uchsc.edu/hunter/bio5099 Larry.Hunter@uchsc.edu Gene expression Some gene products
Biology Performance Level Descriptors
Limited A student performing at the Limited Level demonstrates a minimal command of Ohio s Learning Standards for Biology. A student at this level has an emerging ability to describe genetic patterns of
Lecture 1 MODULE 3 GENE EXPRESSION AND REGULATION OF GENE EXPRESSION. Professor Bharat Patel Office: Science 2, 2.36 Email: b.patel@griffith.edu.
Lecture 1 MODULE 3 GENE EXPRESSION AND REGULATION OF GENE EXPRESSION Professor Bharat Patel Office: Science 2, 2.36 Email: b.patel@griffith.edu.au What is Gene Expression & Gene Regulation? 1. Gene Expression
Evolution of Phenotypes
Evolution of Phenotypes [Background section about the Grant s study of finches on the Galapagos] [see Beak of the Finch].. Daphne Island in the Galapagos Four species of Galapagos finches Peter Grant with
11.1 KEY CONCEPT A population shares a common gene pool.
11.1 KEY CONCEPT A population shares a common gene pool. Why it s beneficial: Genetic variation leads to phenotypic variation. It increases the chance that some individuals will survive Phenotypic variation
The irony of molecular biology: informatics replaces mechanics
The irony of molecular biology: informatics replaces mechanics James A. Shapiro University of Chicago jsha@uchicago.edu, http://shapiro.bsd.uchicago.edu Historical Context of Molecular Genetics 1. Early
Bioinformatics: Network Analysis
Bioinformatics: Network Analysis Molecular Cell Biology: A Brief Review COMP 572 (BIOS 572 / BIOE 564) - Fall 2013 Luay Nakhleh, Rice University 1 The Tree of Life 2 Prokaryotic vs. Eukaryotic Cell Structure
Gene Transcription in Prokaryotes
Gene Transcription in Prokaryotes Operons: in prokaryotes, genes that encode protein participating in a common pathway are organized together. This group of genes, arranged in tandem, is called an OPERON.
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
Macroevolution: Sympatric Speciation
Macroevolution: Sympatric Speciation Types of Speciation: A Review Allopatric speciation is the evolution of geographically isolated populations into distinct species. There is no gene flow, which tends
Chapter 6: Biological Networks
Chapter 6: Biological Networks 6.4 Engineering Synthetic Networks Prof. Yechiam Yemini (YY) Computer Science Department Columbia University Overview Constructing regulatory gates A genetic toggle switch;
Estimating Dynamics for (DC-motor)+(1st Link) of the Furuta Pendulum
Estimating Dynamics for (DC-motor)+(1st Link) of the Furuta Pendulum 1 Anton and Pedro Abstract Here the steps done for identification of dynamics for (DC-motor)+(1st Link) of the Furuta Pendulum are described.
Scientific Process Skills: Scientific Process Skills:
Texas University Interscholastic League Contest Event: Science The contest challenges students to read widely in biology, to understand the significance of experiments rather than to recall obscure details,
Gene Regulation in Prokaryotes. Lectures 19-21
Gene Regulation in Prokaryotes Lectures 19-21 76 III. Gene Regulation Questions How to make different proteins at different times? How to make different cells that do different things? How to make different
Genetic Regulatory Mechanisms in the Synthesis of Proteins François Jacob and Jacques Monod. Journal of Molecular Biology (1961) 3:
Genetic Regulatory Mechanisms in the Synthesis of Proteins François Jacob and Jacques Monod. Journal of Molecular Biology (1961) 3: 318-356 " In describing genetic mechanisms, there is a choice between
LESSON 3.5 WORKBOOK. How do cancer cells evolve? Workbook Lesson 3.5
LESSON 3.5 WORKBOOK How do cancer cells evolve? In this unit we have learned how normal cells can be transformed so that they stop behaving as part of a tissue community and become unresponsive to regulation.
Lecture 11 Feedback: static analysis
S. Boyd EE102 Lecture 11 Feedback: static analysis feedback: overview, standard configuration, terms static linear case sensitivity static nonlinear case linearizing effect of feedback 11 1 Feedback: general
Biology 1406 - Notes for exam 5 - Population genetics Ch 13, 14, 15
Biology 1406 - Notes for exam 5 - Population genetics Ch 13, 14, 15 Species - group of individuals that are capable of interbreeding and producing fertile offspring; genetically similar 13.7, 14.2 Population
Modules 5: Behavior Genetics and Evolutionary Psychology
Modules 5: Behavior Genetics and Evolutionary Psychology Source of similarities and differences Similarities with other people such as developing a languag, showing similar emotions, following similar
SOUND ACTIVATED SWITCH
EE-389 Electronic Design Lab-II Project Report, EE Dept, IIT Bombay, submitted on 02-12-2005. SOUND ACTIVATED SWITCH Group No. D15 Mamidipally Chandrasekhar (96D07008) Supervisor:
Curriculum Map: Life Sciences Grade 8. Month. Unit. Essential Questions (List 1-3 Essential Questions)
Month Unit 2016-2017 Curriculum Map: Life Sciences Grade 8 September Scientific Inquiry October Origin of Life/Cellular Processes Essential Questions (List 1-3 Essential Questions) Learning Outcomes/Objectives
Module 3 Questions. 7. Chemotaxis is an example of signal transduction. Explain, with the use of diagrams.
Module 3 Questions Section 1. Essay and Short Answers. Use diagrams wherever possible 1. With the use of a diagram, provide an overview of the general regulation strategies available to a bacterial cell.
Ch. 13 How Populations Evolve Period. 4. Describe Lamarck s proposed theory of evolution, The Theory of Acquired Traits.
Ch. 13 How Populations Evolve Name Period California State Standards covered by this chapter: Evolution 7. The frequency of an allele in a gene pool of a population depends on many factors and may be stable
Essentials of Human Anatomy & Physiology 11 th Edition, 2015 Marieb
A Correlation of Essentials of Human Anatomy Marieb To the Next Generation Science Standards Life A Correlation of, HS-LS1 From Molecules to Organisms: Structures and Processes HS-LS1-1. Construct an explanation
1. Introduction Gene regulation Genomics and genome analyses Hidden markov model (HMM)
1. Introduction Gene regulation Genomics and genome analyses Hidden markov model (HMM) 2. Gene regulation tools and methods Regulatory sequences and motif discovery TF binding sites, microrna target prediction
2.3 Cantilever linear oscillations
.3 Cantilever linear oscillations Study of a cantilever oscillation is a rather science - intensive problem. In many cases the general solution to the cantilever equation of motion can not be obtained
6.254 : Game Theory with Engineering Applications Lecture 10: Evolution and Learning in Games
6.254 : Game Theory with Engineering Applications Lecture 10: and Learning in Games Asu Ozdaglar MIT March 9, 2010 1 Introduction Outline Myopic and Rule of Thumb Behavior arily Stable Strategies Replicator
MCB142/IB163 Mendelian and Population Genetics 9/19/02
MCB142/IB163 Mendelian and Population Genetics 9/19/02 Practice questions lectures 5-12 (Hardy Weinberg, chi-square test, Mendel s second law, gene interactions, linkage maps, mapping human diseases, non-random
Using chromosomal laci Q1 to control. high copy number plasmids in Escherichia coli. Weickert; Gene 223; 1998 : 221 231
Using chromosomal laci Q1 to control expression of genes on high copy number plasmids in Escherichia coli Christopher B Glascock Michael J Christopher B. Glascock, Michael J. Weickert; Gene 223; 1998 :
C1. A gene pool is all of the genes present in a particular population. Each type of gene within a gene pool may exist in one or more alleles.
C1. A gene pool is all of the genes present in a particular population. Each type of gene within a gene pool may exist in one or more alleles. The prevalence of an allele within the gene pool is described
Ch.16-17 Review. Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question.
Name: Class: Date: Ch.16-17 Review Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Which of the following statements describe what all members of a population
Feedforward and Ratio Control
Introduction to Feedforward Controller Design Based on Steady-State Models Feedforward Controller Design Based on Dynamic Models Tuning Feedforward Controllers 1 2 Feedback control is an important technique
Supplementary Information
Supplementary Information S1: Degree Distribution of TFs in the E.coli TRN and CRN based on Operons 1000 TRN Number of TFs 100 10 y = 619.55x -1.4163 R 2 = 0.8346 1 1 10 100 1000 Degree of TFs CRN 100
GENE EXPRESSION Transcription (Video)
GENE EXPRESSION Is the process by which information from a gene is used in the synthesis of a functional gene product. These products are often proteins, but in non-protein coding genes such as rrna genes
Report Assignment 1: A Cancer Model
Report Assignment 1: A Cancer Model The first report assignment concerns a mathematical model of cancer. It is based on an article by John D. Nagy [Competition and natural selection in a mathematical model
Gene Regulation -- The Lac Operon
Gene Regulation -- The Lac Operon Specific proteins are present in different tissues and some appear only at certain times during development. All cells of a higher organism have the full set of genes:
Name Class Date. Figure 13 1. 2. Which nucleotide in Figure 13 1 indicates the nucleic acid above is RNA? a. uracil c. cytosine b. guanine d.
13 Multiple Choice RNA and Protein Synthesis Chapter Test A Write the letter that best answers the question or completes the statement on the line provided. 1. Which of the following are found in both
FOGA I - IS THERE SUCH A THING AS A GENE? = FORMATTING THE GENOME FOR PROTEIN SYNTHESIS
FOGA I - IS THERE SUCH A THING AS A GENE? = FORMATTING THE GENOME FOR PROTEIN SYNTHESIS Dilemma in choosing lecture style: Schematic presentation of all exceptions to classical view In-depth examination
Markov chains and Markov Random Fields (MRFs)
Markov chains and Markov Random Fields (MRFs) 1 Why Markov Models We discuss Markov models now. This is the simplest statistical model in which we don t assume that all variables are independent; we assume
Population Regulation. Limiting and Regulating Factors. Limitation, Regulation in Reality. Populations can t increase forever. Limitation Regulation
Population Regulation A realistic basis for dealing with the changes of numbers in populations would include the following propositions: (a) All populations are constantly changing in size; (b) The environments
Chapter 11. MRP and JIT
Chapter 11 MRP and JIT (Material Resources Planning and Just In Time) 11.1. MRP Even if MRP can be applied among several production environments, it has been chosen here as a preferential tool for the
NEUROEVOLUTION OF AUTO-TEACHING ARCHITECTURES
NEUROEVOLUTION OF AUTO-TEACHING ARCHITECTURES EDWARD ROBINSON & JOHN A. BULLINARIA School of Computer Science, University of Birmingham Edgbaston, Birmingham, B15 2TT, UK e.robinson@cs.bham.ac.uk This
A Critique of Cosmological Natural Selection
A Critique of Cosmological Natural Selection Gordon McCabe March 7, 2004 Abstract Mathematical physicist Lee Smolin has proposed a theory of Cosmological natural selection which, he argues, explains the
Introduction to Game Theory Evolution Games Theory: Population Games
Introduction to Game Theory Evolution Games Theory: Population Games John C.S. Lui Department of Computer Science & Engineering The Chinese University of Hong Kong www.cse.cuhk.edu.hk/ cslui John C.S.
Capacitors and Resistors: Capacitor Time Constants
Not the Flux Kind A capacitor is comprised of a pair of conductors surrounding some non-conducting region (either empty space or a dielectric). The point of these things is to store energy in the electric
Generalized Linear Model Theory
Appendix B Generalized Linear Model Theory We describe the generalized linear model as formulated by Nelder and Wedderburn (1972), and discuss estimation of the parameters and tests of hypotheses. B.1
1. Matter that originates from a living organism or the products of their life processes.
Bio Vocabulary Review Name: Directions: Use the MONSTROUS word bank below to pair vocabulary terms with their definition. Each word will be used only once. A. Antibiotics B. Artificial Selection C. ATP
Minimizing Power Supply Transient Voltage at a Digital Wireless Telecommunications Products' Test Fixture
Minimizing Power Supply Transient Voltage at a Digital Wireless Telecommunications Products' Test Fixture By Jim Gallo and Ed Brorein Agilent Technologies, Inc. Abstract This paper addresses the problem
Modern Physics Laboratory Geiger-Muller Tube Characteristics and Radioactive Decay
Modern Physics Laboratory Geiger-Muller Tube Characteristics and Radioactive Decay Josh Diamond and John Cummings Fall 2009 Abstract In this experiment the influence of tube voltage and resolving time
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.
Energy and Muscle Contraction
Energy and Muscle Contraction A primer on the Energetics of Muscle Contraction and Oxygen Debt By Noel Ways Energy and Muscle Contraction Our study of muscle contraction ("Sliding Filament Theory") made
Chapter 16 How Populations Evolve
Title Chapter 16 How Populations Evolve Copyright The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Population Genetics A population is all of the members of a single species
Compensation Methods in Voltage Regulators. An Evolution from Analog to Digital
An Evolution from Analog to Digital Feedback signals are used in voltage regulator circuits in order to produce a controlled output voltage. When properly implemented, feedback will improve the performance
Topology and Convergence by: Daniel Glasscock, May 2012
Topology and Convergence by: Daniel Glasscock, May 2012 These notes grew out of a talk I gave at The Ohio State University. The primary reference is [1]. A possible error in the proof of Theorem 1 in [1]
10 Population Dynamics
10 Population Dynamics 10 Population Dynamics Time delays Demographic stochasticity Figure 10.8 A Population Cycle Patterns of Population Growth Different factors may drive population cycles in rodents.
AP Biology Essential Knowledge Student Diagnostic
AP Biology Essential Knowledge Student Diagnostic Background The Essential Knowledge statements provided in the AP Biology Curriculum Framework are scientific claims describing phenomenon occurring in
Population Genetics and Evolution - Practice
Name: Period: Date: Population Genetics and Evolution - Practice Multiple Choice Identify the choice that best completes the statement or answers the question. Indicate your answer choice with an UPPER
Topic 7: METABOLISM: THERMODYNAMICS, CHEMICAL EQUILIBRIA, ENERGY COUPLING and CATALYSIS (lectures 9-10)
Topic 7: METABOLISM: THERMODYNAMICS, CHEMICAL EQUILIBRIA, ENERGY COUPLING and CATALYSIS (lectures 9-10) OBJECTIVES: 1. Understand the concepts of kinetic vs. potential energy. 2. Understand the concepts
Lab Exercise: Transformation
Lab Exercise: Transformation Background Genetic transformation is used in many areas of biotechnology, and, at its heart, requires two things: Donor DNA and recipient cells. Cells which receive the donor
The effect of population history on the distribution of the Tajima s D statistic
The effect of population history on the distribution of the Tajima s D statistic Deena Schmidt and John Pool May 17, 2002 Abstract The Tajima s D test measures the allele frequency distribution of nucleotide
In developmental genomic regulatory interactions among genes, encoding transcription factors
JOURNAL OF COMPUTATIONAL BIOLOGY Volume 20, Number 6, 2013 # Mary Ann Liebert, Inc. Pp. 419 423 DOI: 10.1089/cmb.2012.0297 Research Articles A New Software Package for Predictive Gene Regulatory Network
Gene Trees in Species Trees
Computational Biology Class Presentation Gene Trees in Species Trees Wayne P. Maddison Published in Systematic Biology, Vol. 46, No.3 (Sept 1997), pp. 523-536 Overview Gene Trees and Species Trees Processes
Synthetic gene networks on paper
Synthetic gene networks on paper Journal club Caihong Zhu 25.11.2014 Synthetic biology Definition: "designing and constructing biological devices and biological systems for useful purposes. _Wekipedia
Hidden Markov Models for biological systems
Hidden Markov Models for biological systems 1 1 1 1 0 2 2 2 2 N N KN N b o 1 o 2 o 3 o T SS 2005 Heermann - Universität Heidelberg Seite 1 We would like to identify stretches of sequences that are actually
Muscle Physiology. Lab 5. Human Muscle Physiology
Lab 5 Human At the beginning of lab you will have the opportunity for 2 bonus points! You must guess which person in the class will have: 1) Maximum Grip Force 2) Longest time to half-max Force (longest
Chapter 4. Saving and Investment. 4.1 The Model Structure
Chapter 4 Saving and Investment The models I have discussed so far are missing a central component of the General Theory; the idea that investment is the driving force of business cycles. Chapter 4 introduces
Biofilm Growth (2011)
MASSACHUSETTS INSTITUTE OF Technology Department of Biology 2011 Teachers' Workshop Biofilm Growth (2011) Ken Bateman (Wellesley High School) Mary Brunson (Brookline High School) Doug Brown (Wellesley
Bacterial Transformation Post lab Questions:
Bacterial Transformation Post lab Questions: 1. This graph represents typical bacteria growth and death on any culture plate. This trend occurs in both Luria Broth/ agarose and Luria broth/ Agarose/ Ampicillin/Arabinose
Per Svedensten, Plant Simulation Specialist Sandvik Mining and Construction, Svedala, Sweden
CRUSHING PLANT PERFORMANCE OPTIMIZATION Per Svedensten, Plant Simulation Specialist Sandvik Mining and Construction, Svedala, Sweden per.svedensten@sandvik.com ABSTRACT The crushing plant system of crusher,
Mechanisms of Evolution
page 2 page 3 Teacher's Notes Mechanisms of Evolution Grades: 11-12 Duration: 28 mins Summary of Program Evolution is the gradual change that can be seen in a population s genetic composition, from one
Population and Community Dynamics
Population and Community Dynamics Part 1. Genetic Diversity in Populations Pages 676 to 701 Part 2. Population Growth and Interactions Pages 702 to 745 Review Evolution by Natural Selection new variants
2015 NATIONAL SCIENCE OLYMPIAD AND NEXT GENERATION SCIENCE STANDARDS ALIGNMENT
2015 NATIONAL SCIENCE OLYMPIAD AND NEXT GENERATION SCIENCE STANDARDS ALIGNMENT C (HIGH SCHOOL) DIVISION AIR TRAJECTORY Prior to the competition, teams will design, construct, and calibrate a single device
Localised Sex, Contingency and Mutator Genes. Bacterial Genetics as a Metaphor for Computing Systems
Localised Sex, Contingency and Mutator Genes Bacterial Genetics as a Metaphor for Computing Systems Outline Living Systems as metaphors Evolutionary mechanisms Mutation Sex and Localized sex Contingent
Moral Hazard. Itay Goldstein. Wharton School, University of Pennsylvania
Moral Hazard Itay Goldstein Wharton School, University of Pennsylvania 1 Principal-Agent Problem Basic problem in corporate finance: separation of ownership and control: o The owners of the firm are typically
Designing a Poor Man s Square Wave Signal Generator. EE-100 Lab: Designing a Poor Man s Square Wave Signal Generator - Theory
EE-100 Lab: - Theory 1. Objective The purpose of this laboratory is to introduce nonlinear circuit measurement and analysis. Your measurements will focus mainly on limiters and clamping amplifiers. During
Homework 5: There Were Giants in the Earth in Those Days
Homework 5: There Were Giants in the Earth in Those Days 36-402, Spring 2015 Due at 11:59 pm on Monday, 16 February 2015 AGENDA: Explicitly: splines, bootstrap, simulation, comparing a simulation to data;
of interrelatedness and decent with modification. However, technological advances are opening another powerful avenue of comparison: DNA sequences.
SCI115SC Module 3 AVP Transcript Title: Genetic Similarity Title Slide Narrator: Welcome to this presentation on genetic similarity. Slide 2 Title: Underlying the Outwardly Visible Similarities, We Find
Economic Growth I: Capital Accumulation and Population Growth
CHAPTER 8 : Capital Accumulation and Population Growth Modified for ECON 2204 by Bob Murphy 2016 Worth Publishers, all rights reserved IN THIS CHAPTER, YOU WILL LEARN: the closed economy Solow model how
Plant Biology Learning Framework
Plant Biology Learning Framework Topic Plant Structure and Function Plant Growth and Reproduction Organization of Matter and Energy Flow Information Processing/Signal Transduction Plants in Ecosystems
ESTIMATING EFFECTIVE TAX RATES ON CORPORATE INCOME. Peter Birch Sørensen Department of Economics, University of Copenhagen November 2008
ESTIMATING EFFECTIVE TAX RATES ON CORPORATE INCOME Peter Birch Sørensen Department of Economics, University of Copenhagen November 28 This note explains the analytical framework and assumptions underlying
Random Portfolios for Evaluating Trading Strategies
Random Portfolios for Evaluating Trading Strategies Patrick Burns 13th January 2006 Abstract Random portfolios can provide a statistical test that a trading strategy performs better than chance. Each run
Kinetic Analysis of β-galactosidase Activity using the PowerWave HT and Gen5 Data Analysis Software
Kinetic Analysis of β-galactosidase Activity using the PowerWave HT and Gen5 Data Analysis Software Basic enzyme kinetic determinations Paul Held Ph. D. Senior Scientist, Applications Dept., BioTek Instruments,
Genetic Drift Simulation. Experimental Question: How do random events cause evolution (a change in the gene pool)?
Genetic Drift Simulation Experimental Question: How do random events cause evolution (a change in the gene pool)? Hypothesis: Introduction: What is Genetic Drift? Let's examine a simple model of a population
The burning candle. Overview. Aims. Teaching sequence. Experiments about plant growth MODULE 1
Experiments about plant growth MODULE 1 The burning candle Timing 1-2 hours Materials per group 1 Tea candle 1 Jar 1 Stop-watch 1 Straw Matches Photocopies of sheets E1, E2, E3 Skills Observation Measurement
Theory of the Firm. Itay Goldstein. Wharton School, University of Pennsylvania
Theory of the Firm Itay Goldstein Wharton School, University of Pennsylvania 1 Boundaries of the Firm Firms are economic units that make decisions, produce, sell, etc. What determines the optimal size