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Michigan State University - MMG - 433
Microbial Genomes Features Analysis Role of high-throughput sequencing Yeast - the eukaryotic model microbe Databases NCBI Microbial Genomes JGI (IMG) Wiki sitesGenome of the week Haemophilus influenzae First microbial genome completely sequenced. To
Michigan State University - MMG - 433
Genome sequencing and annotationWeek 2 reading assignments - pages 65-79, 110-122. Boxes 2.1, 2.2 and 2.3. Dont worry about the details of HMM. Hughes Functional Genomics Review. Metzger next generation sequencing review Sequencing - dideoxy method for
Michigan State University - MMG - 433
Genome of the week Bacillus subtilisGram-positive soil bacterium Genetically tractable, well-studied Developmental pathways (sporulation, genetic competence) Industrial and agricultural importance 4.2 Mb genome (sequence completed 1997) Close relative of
Michigan State University - MMG - 433
Protein complexes - why?Proteins often function as large, multisubunit complexes.RNA polymeraseCan get clues about the function of a protein by knowing what other proteins it contacts.Protein:protein interactionsGenetic approachYeast 2-hybrid Co-imm
Michigan State University - MMG - 433
Structural proteomics Nanoarchaeum equitans - archaea Hyperthermophile Diverged early in evolution from other archaea New kingdom of archaea? Obligate symbiont with Ignicoccus One of the smallest completely sequenced genome <500kB Genome reduction obs
Michigan State University - MMG - 433
Genetics and Genomics Forward genetics Reverse genetics Genetic tools for genome-wide analysis Genome scale reverse genetics Signature tagged mutagenesis Synthetic lethal screens Book - 300-304 (mid-page), 306-307, 310 Gain-offunction, 317 synthetic l
Michigan State University - MMG - 433
Course syllabus - MMG433 Microbial Genomics Spring Semester 2011 Tuesday/Thursday lecture 10:20-11:10 room 1420 Tuesday Lab 2245 BPS 11:45-2:15 Thursday Lab 2245 BPS 11:45-2:15 Course objective: An introduction to the concepts and experimental approaches
Michigan State University - MMG - 433
MMG 433: Review/Study GuideLecture 1: Two approaches to Genomics: Philosophical New way to approach biological problems Practical High throughput methods for analyzing biological systems DNA Sequencing: 1392 complete bacterial genomes currently in Entrez
Michigan State University - MMG - 433
Mic 433, Microbial Genomics Lab exercise #2 Due date: January 25 or 27Name: Mary Ellen HoinskiThe goal of this laboratory exercise is to explore some of the major gene and genome databases and to become familiar with retrieving information available at
Michigan State University - MMG - 433
Mic 433, Microbial Genomics Lab exercise #3 Due February 2nd or 4thName: Mary Ellen HoinskiThe goal of this session is to gain some practical experience in assembling and analyzing a consensus sequence, and to solidify your understanding of DNA sequenci
Michigan State University - MMG - 433
Lab #4 MMG433 Due Feb. 9 or 11Name:Investigating gene function using online database toolsThe goal of this lab is to use the various web based bioinformatics tools to probe the possible functions of proteins. In this lab we will use BLAST, CDD, pfam, C
Michigan State University - MMG - 433
Lab #5 DNA microarray image and data analysis Due Feb. 15 or 17Name: Mary Ellen HoinskiThe objective of this lab exercise is to become familiar with microarray images and basic data analysis. Today we are working with a microarray image that was produce
Penn State - PHYS - 597
Phys 597A CMPS 497E Phys 597A, CMPS 497E Graphs and Networks in Systems BiologyLecturer: Rka Albert Rka Albertralbert@phys.psu.edu 122 Davey Laboratory Davey LaboratoryNetworks, networks everywhere Network infrastructure, social networking Network - a
Penn State - PHYS - 597
Networks, networks everywhere Network infrastructure, social networking Network - a tool for understanding complex systems Many non-identical elements connected by diverse interactions E.g. interaction networks within cells: protein interactions, chemica
Penn State - PHYS - 597
Graph conceptsGraphs are made up by vertices (nodes) and edges (links). (li An edge connects two vertices, or a vertex with itself loop. AC, AC - multiple edges BB loop The shape of the graph does not matter, only the way the nodes are connected to each
Penn State - PHYS - 597
Graph conceptsGraphs are made up by vertices (nodes) and edges (links). An edge connects two vertices, or a vertex with itself loop. AC, AC - multiple edges BB loop The shape of the graph does not matter, only the way the nodes are connected to each othe
Penn State - PHYS - 597
Properties of real networks: degree di distributionNodes with small degrees are most frequent. The fraction of highly connected nodes decreases, but is not zero. Look closer: use a logarithmic plot.10 10 10 10 100 -1 -2 -3 -4100101102103.51 l
Penn State - PHYS - 597
Properties of real networks: degree distribution10 10 10 10 100 -1 -2 -3 -4100101102103.51 loglog1 0.6semilogPlotting power laws and exponentials Nodes with small degrees are most frequent. The fraction of highly connected nodes decreases,
Penn State - PHYS - 597
Community structure in networks Many real-world networks, especially social ones, exhibit community structure (also called modularity). Intuitively community structure can be defined as the th existence of subgraphs that are densely connected but sparsel
Penn State - PHYS - 597
Community structure in networks Many real-world networks, especially social ones, exhibit community structure (also called modularity). Intuitively community structure can be defined as the existence of subgraphs that are densely connected but sparsely i
Penn State - PHYS - 597
Ecological Networks Ecological Networks15 September 2009Types of ecological networks Types of ecological networks Community nodes: species links: interactions between species p Population nodes: populations of one species nodes: populations of one s
Penn State - PHYS - 597
9/11/2009Typesofecologicalnetworks Community nodes:species links:interactionsbetweenspeciesEcologicalNetworks15September2009 Population nodes:populationsofonespecies links:dispersalbetweenpopulations Individual nodes:individualorganisms links:gen
Penn State - PHYS - 597
The The structure of molecular & cellular networks networksTo be able to construct and analyze a cellular network, we need to clearly define what we identify as a node and what we represent with an edge. The nodes and edges have to be at least similar to
Penn State - PHYS - 597
The structure of molecular & cellular networksTo be able to construct and analyze a cellular network, we need to clearly define what we identify as a node and what we represent with an edge. The nodes and edges have to be at least similar to each other,
Penn State - PHYS - 597
9/24/2009GraphrepresentationsV numberofvertices(nodes),E numberofedgesAdjacencymatrixa b c d e f g h a 0 1 0 0 1 0 1 0 b 1 0 1 0 0 0 0 1 c 0 1 0 1 0 1 1 0 d 0 0 1 0 1 0 0 0 e 1 0 0 1 0 0 0 0 f 0 0 1 0 0 0 0 0 g 1 0 1 0 0 1 0 0 h 0 1 0 0 0 0 0 0Edgear
Penn State - PHYS - 597
GraphrepresentationsV numberofvertices(nodes),E numberofedgesEdgearray size:E Adjacencymatrix size:V*V Adjacencylists size:V+E purerepresentationsAdjacencymatrixa b c d e f g h a 0 1 0 0 1 0 1 0 b 1 0 1 0 0 0 0 1 c 0 1 0 1 0 1 1 0 d 0 0 1 0 1 0 0 0 e
Penn State - PHYS - 597
Network models random graphsProperties common to many large-scale networks, independently of their origin and function: 1. The degree and betweenness distribution are decreasing functions, usually power-laws. 2. The distances scale logarithmically with t
Penn State - PHYS - 597
Network models random graphsProperties common to many large-scale networks, independently of their origin and function: 1. The degree and betweenness distribution are decreasing functions, usually power-laws. 2. The distances scale logarithmically with t
Penn State - PHYS - 597
Network modelsProperties common to many large-scale networks, independently of their origin and function: 1. The degree and betweenness distribution are decreasing Th di scale - free functions, usually power-laws. 2. The distances scale logarithmically w
Penn State - PHYS - 597
Network modelsProperties common to many large-scale networks, independently of their origin and function: 1. The degree and betweenness distribution are decreasing scale - free functions, usually power-laws. 2. The distances scale logarithmically with th
Penn State - PHYS - 597
Topological perturbation of complex networksPerturbations in complex systems can deactivate some of the edges or nodes. Edge loss: the edge is deleted Node loss: the node and all its edges are deleted Effects on the global topology:Resilience to perturb
Penn State - PHYS - 597
Topological perturbation of complex networks networksPerturbations in complex systems can deactivate some of the edges or nodes. Edge loss: the edge is deleted Edge loss: the edge is deleted Node loss: the node and all its edges are deleted Effects on th
Penn State - PHYS - 597
The two faces of network dynamicsEvolving network models describe the dynamics/assembly/evolution network models describe the dynamics/assembly/evolution of networks by the addition/removal of nodes and edges. It is possible to have network dynamics even
Penn State - PHYS - 597
The two faces of network dynamicsEvolving network models describe the dynamics/assembly/evolution of networks by the addition/removal of nodes and edges. It is possible to have network dynamics even if there are no node/edge additions/removals, i.e. the
Penn State - PHYS - 597
Modeling signal transduction networks by continuous and deterministic modelsReceptor - ligand binding - assumed to be elementary reaction Methylation, phosphorylation reactions catalyzed by enzymes, Michaelis-Menten kinetics assumed Dephosphorylation, pr
Penn State - PHYS - 597
Modeling signal transduction networks by continuous and deterministic modelsReceptor - ligand binding - assumed to be elementary reaction Methylation, phosphorylation reactions catalyzed by enzymes, Michaelis-Menten kinetics assumed Dephosphorylation, pr
Penn State - PHYS - 597
Spreading ProcessesModeling Infectious Disease Dynamics with NetworksWhat is epidemiology? Terms Susceptible Infected Epidemic Questions asked: will an epidemic occur? what is the typical size of an outbreak? what determines the probability of an epide
Penn State - PHYS - 597
11/2/2009What is epidemiology? Terms Susceptible Infected Epidemic Questions asked: will an epidemic occur? what is the typical size of an outbreak? what determines the probability of an epidemic? How do we control the spread?Spreading ProcessesModeli
Penn State - PHYS - 597
Discrete dynamic modeling of biological systems The functional form of regulatory relationships and kinetic parameters are often unknown Increasing evidence for robustness to changes in kinetic parameters. bistability (two steady states)Hypothesis: the
Penn State - PHYS - 597
Discrete dynamic modeling of biological systems The functional form of regulatory relationships and kinetic parameters are often unknown Increasing evidence for robustness to changes in kinetic parameters. bistability (two steady states)Boolean modeling
Penn State - PHYS - 597
Network inference from dynamic (state) informationInput: components; states of components (in time) Hypotheses: regulatory framework Output: proposed regulatory network Validation: capture known interactions known interactionsFor inference of gene regul
Penn State - PHYS - 597
Network inference from dynamic (state) informationInput: components; states of components (in time) Hypotheses: regulatory framework Output: proposed regulatory network Validation: capture known interactionsFor inference of gene regulatory networks, the
Penn State - PHYS - 597
CMPSC 497E: Graphs and networks in systems biologyHomework assignment 1, due Thursday Sept. 31. Find an example for a network in your research area or everyday life. Dene the nodes and edges and give/estimate their numbers. Are the edges directed or not
Penn State - PHYS - 597
PHYS 597A: Graphs and networks in systems biologyHomework assignment 1, due Thursday Sept. 31. Find an example for a network in your research area or everyday life. Dene the nodes and edges and give/estimate their numbers. Are the edges directed or not?
Penn State - PHYS - 597
CMPSC 497E: Graphs and networks in systems biologyHomework assignment 2, due Thursday Sept. 101. Construct a graph or digraph with 10 nodes and 15 edges. Extra credit will be given for using the digraph framework. Determine (a) the degree distribution o
Penn State - PHYS - 597
PHYS 597A: Graphs and networks in systems biologyHomework assignment 2, due Thursday Sept. 101. Construct a graph or digraph with 15 nodes and 20 edges. Extra credit will be given for using the digraph framework. Determine (a) the degree distribution of
Penn State - PHYS - 597
CMPSC 497E: Graphs and networks in systems biologyHomework assignment 3, due Thursday Sept. 17Construct a graph with 8 nodes and 12 undirected edges. Determine (a) the distance distribution (remember that not having a path corresponds to an innite dista
Penn State - PHYS - 597
PHYS 597A: Graphs and networks in systems biologyHomework assignment 3, due Thursday Sept. 17Construct a graph with 10 nodes and 15 undirected edges. Determine (a) the distance distribution (remember that not having a path corresponds to an innite dista
Penn State - PHYS - 597
PHYS 597A, CMPSC 497E: Graphs and networks in systems biologyHomework assignment 4, due Tuesday Sept. 29Read Systems-level insights into cellular regulation: inferring, analyzing, and modeling intracellular networks.1. Write down four questions or idea
Penn State - PHYS - 597
PHYS 597A, CMPSC 497E: Graphs and networks in systems biologyHomework assignment 5, due Tuesday Oct. 61. The Sept. 24 lecture surveyed a number of graph processing and visualization tools. Install one of them and use it to draw and analyze a graph. Prov
Penn State - PHYS - 597
PHYS 597A, CMPSC 497E: Graphs and networks in systems biologyHomework assignment 6, due Tuesday Oct 13Read chapter VII, Scale-free Networks, sections A-D.3 (pages 71-75) and chapter VIII, The Theory of Evolving Networks, sections A-F (pages 7683) of Sta
Penn State - PHYS - 597
PHYS 597A, CMPSC 497E: Graphs and networks in systems biologyHomework assignment 7, due Tuesday Oct. 271. Based on what we have learned in class so far, answer the following questions in your own words. You can support your answer with formulas but form
Penn State - PHYS - 597
PHYS 597A, CMPSC 497E, Graphs and Networks in Systems BiologyHomework 8, due Tuesday Nov. 31.Perform a topological analysis of the network below. Consider that edges terminating in edges are directed toward the endpoint of the edge (e.g. the edge from
Penn State - PHYS - 597
PHYS 597A, CMPSC 497E: Graphs and networks in systems biologyHomework assignment 9, due Tuesday Nov 171. Read the article Boolean network simulations for life scientists by I. Albert, J. Thakar, S. Li, R Zhang and R. Albert, in Source Code for Biology a
Penn State - PHYS - 597
PHYS 597A, CMPSC 497E: Graphs and networks in systems biologyProject assignment 2, due Tuesday Nov. 8This assignment represents the rst component of your term project. The goal is to write a rst draft of your paper, with a clear motivation and denition
Penn State - PHYS - 597
CMPSC 497E: Graphs and networks in systems biologyProject assignment 1, due Tuesday Oct. 20It is now time to decide your term project topics. As a reminder, your will write a term paper due on the Monday after classes end. Your term paper should be a co
Penn State - PHYS - 597
PHYS 597A: Graphs and networks in systems biologyProject assignment 1, due Tuesday Oct. 20It is now time to decide your term project topics. As a reminder, you will give an in-class presentation on your projects during the last two weeks of the semester
Penn State - IE - 597b
Anal yt i c Hi er ar chy Pr ocess PrA Br i ef I nt r oduct i onAbout AHP By Thom as Saat y ( 1980) M t i pl e- cr i t er i a deci si on- m ul aki ng m hod et Power f ul b ut danger ous Fl exi bl e b ut l abor i ousAppl i cat i ons and Sof t war e1. A
Penn State - IE - 597b
Prof. Hong lecture Mon 4pm here Casestudy format QuantityCulture and CommunicationMichael L. Hecht, Ph.D. Jeong Kyu Lee Communication Arts & Sciences Penn State UniversityDefinition of cultureCode: systems of symbols, rules, meanings, beliefs, value
Penn State - IE - 597b
Integrated HMI SolutionsOct. 2009Delphis Vision of a Cockpit with Integrated HMI2Vision: Integrated HMI solutionsSide/Rear View DisplaysPersonalized vehicle environment Reflects individual driver demographics HMI adaptive to driver needs Consistent,
Penn State - IE - 597b
Electronic dictionaryProduct Electronic dictionary Define 1.Name: Kim, TaeHee person 2.Gender: Girl 3. Age: 18 years 4. Profession: High school student in Seoul 5. Objective function: Entering the Penn. State university 6. Subjective function: No abroad