regulatory motifs gene regulation
play

Regulatory Motifs Gene Regulation Promoter Gene -35 -10 RNA - PDF document

Sep 20, 2023 •371 likes •506 views

Regulatory Motifs Gene Regulation Promoter Gene -35 -10 RNA polymerase Negative Positive Regulation Rep Regulation Act RNA polymerase 1 What if we believed that a number of genes were regulated by the same transcription factor? TF

  1. Regulatory Motifs Gene Regulation Promoter Gene -35 -10 RNA polymerase Negative Positive Regulation Rep Regulation Act RNA polymerase 1

  2. What if we believed that a number of genes were regulated by the same transcription factor? TF “X” Gene1 Gene2 Gene3 Gene4 Gene5 What if we believed that a number of genes were orthologous? Gene EC Gene HI Gene VC Gene ST Gene PA 2

  3. How do we search upstream sequences for instances of a motif? > Escherichia coli TTGATTCCCTGAATGCCCGCTTAGTGTAACACTACTGTAACCGGCATTTTCTGCTTTTCC TGCCGATATTTTTTCTTATCTACCTCACAAAGGTTAGCAATAACTGCTGGGAAAATTCCG AGTTAGTCGTTATATTCTAT > Haemophilus influenzae ATCTAACGGTACGGATTCTCCAAAGGCCTATGGAATCTTGTAGAATATGAAACGTTCTAA TAAATCATAAAGTTGGAGCAAACGCTCGGCATAAGTAGTAAGTGCCGTGCCTCCGCCATT AGTTACACTAGTGGGACACC > Vibrio cholerae ATTTGTGGCGGTTTTCAAATGCTTGGAGAATGGGTACATGATCCGCTTGGCATTGAAGGT GAGGCTGGCAGCAGCGAAGGTCTGGGGCTGTTTGAACGTTACACGAGTGTAACCGCCGAA CCATGTTGACACGAATTCTG > Salmonella typhi GGTCGGCTTAGACTAGTGTGACCAAAAAGCTTTTGCTGAAGTTTCAGGGTAAGAAGAACC AGCTCCTAGTAAAAAGACTATTGTGACTGAAAAGCGCGTCAGCGCAAAGCCGACCGCACA AAACGCACAAGGAGTTACAG > Pseudomonas aeruginosa ACGCGGCCAGGGTCTTCTCCTGCGAGATCATGCGCGGCGCGCCGCGCATGCCGGCGCCGC TGCTGGAACGCCTCGACCCCAGGGCTACACTAGTTTAACCGGAACGCCGCCAGTGGATCG GCCTGCCCCAGCTATTGCTC If we knew where the motif instances were located in each sequence... > Escherichia coli TTGATTCCCTGAATGCCCGCTTAGT GTAACACTACTGTAAC CGGCATTTTCTGCTTTTCC TGCCGATATTTTTTCTTATCTACCTCACAAAGGTTAGCAATAACTGCTGGGAAAATTCCG AGTTAGTCGTTATATTCTAT > Haemophilus influenzae ATCTAACGGTACGGATTCTCCAAAGGCCTATGGAATCTTGTAGAATATGAAACGTTCTAA TAAATCATAAAGTTGGAGCAAACGCTCGGCATAAGTAGTAAGTGCCGTGCCTCCGCCATT A GTTACACTAGTGGGAC ACC > Vibrio cholerae ATTTGTGGCGGTTTTCAAATGCTTGGAGAATGGGTACATGATCCGCTTGGCATTGAAGGT GAGGCTGGCAGCAGCGAAGGTCTGGGGCTGTTTGAAC GTTACACGAGTGTAAC CGCCGAA CCATGTTGACACGAATTCTG > Salmonella typhi GGTCGG CTTAGACTAGTGTGAC CAAAAAGCTTTTGCTGAAGTTTCAGGGTAAGAAGAACC AGCTCCTAGTAAAAAGACTATTGTGACTGAAAAGCGCGTCAGCGCAAAGCCGACCGCACA AAACGCACAAGGAGTTACAG > Pseudomonas aeruginosa ACGCGGCCAGGGTCTTCTCCTGCGAGATCATGCGCGGCGCGCCGCGCATGCCGGCGCCGC TGCTGGAACGCCTCGACCCCAGG GCTACACTAGTTTAAC CGGAACGCCGCCAGTGGATCG GCCTGCCCCAGCTATTGCTC 3

  4. Then we could determine a motif model! GTAACACTACTGTAAC GTTACACTAGTGGGAC GTTACACGAGTGTAAC CTTAGACTAGTGTGAC GCTACACTAGTTTAAC A 0.0 0.0 .20 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 .60 1.0 0.0 C .20 .20 0.0 0.0 .80 0.0 1.0 0.0 0.0 .20 0.0 0.0 0.0 0.0 0.0 1.0 .80 0.0 0.0 0.0 .20 0.0 0.0 .20 0.0 .80 0.0 .80 .20 .40 0.0 0.0 G T 0.0 .80 .80 0.0 0.0 0.0 0.0 .80 0.0 0.0 1.0 .20 .80 0.0 0.0 0.0 G T T A C A C T A G T G T A A C Consensus Sequence But we don’t know the locations of the motif instances... > Escherichia coli TTGATTCCCTGAATGCCCGCTTAGTGTAACACTACTGTAACCGGCATTTTCTGCTTTTCC TGCCGATATTTTTTCTTATCTACCTCACAAAGGTTAGCAATAACTGCTGGGAAAATTCCG AGTTAGTCGTTATATTCTAT > Haemophilus influenzae ATCTAACGGTACGGATTCTCCAAAGGCCTATGGAATCTTGTAGAATATGAAACGTTCTAA TAAATCATAAAGTTGGAGCAAACGCTCGGCATAAGTAGTAAGTGCCGTGCCTCCGCCATT AGTTACACTAGTGGGACACC > Vibrio cholerae ATTTGTGGCGGTTTTCAAATGCTTGGAGAATGGGTACATGATCCGCTTGGCATTGAAGGT GAGGCTGGCAGCAGCGAAGGTCTGGGGCTGTTTGAACGTTACACGAGTGTAACCGCCGAA CCATGTTGACACGAATTCTG > Salmonella typhi GGTCGGCTTAGACTAGTGTGACCAAAAAGCTTTTGCTGAAGTTTCAGGGTAAGAAGAACC AGCTCCTAGTAAAAAGACTATTGTGACTGAAAAGCGCGTCAGCGCAAAGCCGACCGCACA AAACGCACAAGGAGTTACAG > Pseudomonas aeruginosa ACGCGGCCAGGGTCTTCTCCTGCGAGATCATGCGCGGCGCGCCGCGCATGCCGGCGCCGC TGCTGGAACGCCTCGACCCCAGGGCTACACTAGTTTAACCGGAACGCCGCCAGTGGATCG GCCTGCCCCAGCTATTGCTC 4

  5. What if we knew the motif model... A 0.0 0.0 .20 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 .60 1.0 0.0 C .20 .20 0.0 0.0 .80 0.0 1.0 0.0 0.0 .20 0.0 0.0 0.0 0.0 0.0 1.0 .80 0.0 0.0 0.0 .20 0.0 0.0 .20 0.0 .80 0.0 .80 .20 .40 0.0 0.0 G T 0.0 .80 .80 0.0 0.0 0.0 0.0 .80 0.0 0.0 1.0 .20 .80 0.0 0.0 0.0 We could determine the location of the motif instance which best matches the model... A 0.0 0.0 .20 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 .60 1.0 0.0 C .20 .20 0.0 0.0 .80 0.0 1.0 0.0 0.0 .20 0.0 0.0 0.0 0.0 0.0 1.0 .80 0.0 0.0 0.0 .20 0.0 0.0 .20 0.0 .80 0.0 .80 .20 .40 0.0 0.0 G T 0.0 .80 .80 0.0 0.0 0.0 0.0 .80 0.0 0.0 1.0 .20 .80 0.0 0.0 0.0 Score = 0.0 * .80 * 0.0 * 1.0 * 0.0 * 0.0 * 1.0 * 0.0 * 0.0 * 0.0 * 0.0 * 0.0 * 0.0 * 0.0 * 0.0 * 1.0 Score = 0.01 * .80 * 0.01 * 1.0 * 0.01 * 0.01 * 1.0 * 0.01 * 0.01 * 0.01 * 0.01 * 0.01 * 0.01 * 0.01 * 0.01 * 1.0 Score = 8.0 * 10 -27 TTGATTCCCTGAATGC CCGCTTAGTGTAACACTACTGTAA 5

  6. We could determine the location of the motif instance which best matches the model... A 0.0 0.0 .20 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 .60 1.0 0.0 C .20 .20 0.0 0.0 .80 0.0 1.0 0.0 0.0 .20 0.0 0.0 0.0 0.0 0.0 1.0 .80 0.0 0.0 0.0 .20 0.0 0.0 .20 0.0 .80 0.0 .80 .20 .40 0.0 0.0 G T 0.0 .80 .80 0.0 0.0 0.0 0.0 .80 0.0 0.0 1.0 .20 .80 0.0 0.0 0.0 Score = 0.0 * 0.0 * .20 * 0.0 * 0.0 * 0.0 * 1.0 * 0.0 * 0.0 * .80 * 0.0 * 0.0 * .80 * .40 * 0.0 * 1.0 Score = 0.01 * 0.01 * .20 * 0.01 * 0.01 * 0.01 * 1.0 * 0.01 * 0.01 * .80 * 0.01 * 0.01 * .80 * .40 * 0.01 * 1.0 Score = 5.12 * 10 -22 T TGATTCCCTGAATGCC CGCTTAGTGTAACACTACTGTAA We could determine the location of the motif instance which best matches the model... A 0.0 0.0 .20 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 .60 1.0 0.0 C .20 .20 0.0 0.0 .80 0.0 1.0 0.0 0.0 .20 0.0 0.0 0.0 0.0 0.0 1.0 .80 0.0 0.0 0.0 .20 0.0 0.0 .20 0.0 .80 0.0 .80 .20 .40 0.0 0.0 G T 0.0 .80 .80 0.0 0.0 0.0 0.0 .80 0.0 0.0 1.0 .20 .80 0.0 0.0 0.0 Score = 7.16 * 10 -28 TTGATTCCCTGAATGCCCGCTTAG TGTAACACTACTGTAA 6

  7. A 0.0 0.0 .20 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 .60 1.0 0.0 C .20 .20 0.0 0.0 .80 0.0 1.0 0.0 0.0 .20 0.0 0.0 0.0 0.0 0.0 1.0 .80 0.0 0.0 0.0 .20 0.0 0.0 .20 0.0 .80 0.0 .80 .20 .40 0.0 0.0 G T 0.0 .80 .80 0.0 0.0 0.0 0.0 .80 0.0 0.0 1.0 .20 .80 0.0 0.0 0.0 > Escherichia coli TTGATTCCCTGAATGCCCGCTTAGT GTAACACTACTGTAAC CGGCATTTTCTGCTTTTCC TGCCGATATTTTTTCTTATCTACCTCACAAAGGTTAGCAATAACTGCTGGGAAAATTCCG AGTTAGTCGTTATATTCTAT > Haemophilus influenzae ATCTAACGGTACGGATTCTCCAAAGGCCTATGGAATCTTGTAGAATATGAAACGTTCTAA TAAATCATAAAGTTGGAGCAAACGCTCGGCATAAGTAGTAAGTGCCGTGCCTCCGCCATT A GTTACACTAGTGGGAC ACC > Vibrio cholerae ATTTGTGGCGGTTTTCAAATGCTTGGAGAATGGGTACATGATCCGCTTGGCATTGAAGGT GAGGCTGGCAGCAGCGAAGGTCTGGGGCTGTTTGAAC GTTACACGAGTGTAAC CGCCGAA CCATGTTGACACGAATTCTG > Salmonella typhi GGTCGG CTTAGACTAGTGTGAC CAAAAAGCTTTTGCTGAAGTTTCAGGGTAAGAAGAACC AGCTCCTAGTAAAAAGACTATTGTGACTGAAAAGCGCGTCAGCGCAAAGCCGACCGCACA AAACGCACAAGGAGTTACAG > Pseudomonas aeruginosa ACGCGGCCAGGGTCTTCTCCTGCGAGATCATGCGCGGCGCGCCGCGCATGCCGGCGCCGC TGCTGGAACGCCTCGACCCCAGG GCTACACTAGTTTAAC CGGAACGCCGCCAGTGGATCG GCCTGCCCCAGCTATTGCTC Expectation-Maximization (EM) • Randomly guess the locations of each motif instance • Repeat until convergence – Calculate a new motif model from the motif instances – Calculate new locations for the motif instances from the motif model 7

  8. EM - Randomly guess the locations of each motif instance > Escherichia coli TTGATTCCCTGAATGCCCGCTTAGTGTAACACTACTGTAACCGGCATTTTCTGCTTTTCC TGCCGATATTTTTTCTTATCTACCTCACAAAGGTTAGCAATAACTGCTGGGAA AATTCCG AGTTAGTCG TTATATTCTAT > Haemophilus influenzae A TCTAACGGTACGGATT CTCCAAAGGCCTATGGAATCTTGTAGAATATGAAACGTTCTAA TAAATCATAAAGTTGGAGCAAACGCTCGGCATAAGTAGTAAGTGCCGTGCCTCCGCCATT AGTTACACTAGTGGGACACC > Vibrio cholerae ATTTGTGGCGGTTTTCAAATGCTTGGAGAATGGGTACATGATCCGCTTGGCATTGAAGGT GAGGCTGGCAGCAGCGAAGGTCTGGGGCTGTTTGAACGTTACACGAGTGTAACCGCCGAA CC ATGTTGACACGAATTC TG > Salmonella typhi GGTCGGCTTAGACTAGTGTGACCAAAAAGCTTTTGCTGAAGTTTCAGGGTAAGAAGAACC AGCTCCTAGTAAAAAGACTAT TGTGACTGAAAAGCGC GTCAGCGCAAAGCCGACCGCACA AAACGCACAAGGAGTTACAG > Pseudomonas aeruginosa ACGCGGCCAGGGTCTTCTCCTGCGAGATCATGCGCGGCGCGCCGCGCATG CCGGCGCCGC TGCTGG AACGCCTCGACCCCAGGGCTACACTAGTTTAACCGGAACGCCGCCAGTGGATCG GCCTGCCCCAGCTATTGCTC A .40 .20 0.0 .20 .40 0.0 .20 .20 .40 .40 .20 .60 .20 .20 0.0 0.0 C .20 .40 0.0 0.0 .40 .60 .20 .40 0.0 .40 .20 0.0 .20 0.0 .20 .40 0.0 .20 .40 .40 0.0 .40 .40 .40 .40 0.0 .20 .40 .60 .20 .40 .40 G T .40 .20 .60 .40 .20 0.0 .20 0.0 .20 .20 .40 0.0 0.0 .60 .40 .20 > Escherichia coli TTGATTCCCTGAATGCCCGCTTAGTGTAACACTACTGTAACCGGCATTTTCTGCTTTTCC TGCCGATATTTTTTCTTATCTACCTCACAAAGGTTAGCAATAACTGCTGGGAA AATTCCG AGTTAGTCG TTATATTCTAT > Haemophilus influenzae A TCTAACGGTACGGATT CTCCAAAGGCCTATGGAATCTTGTAGAATATGAAACGTTCTAA TAAATCATAAAGTTGGAGCAAACGCTCGGCATAAGTAGTAAGTGCCGTGCCTCCGCCATT AGTTACACTAGTGGGACACC > Vibrio cholerae ATTTGTGGCGGTTTTCAAATGCTTGGAGAATGGGTACATGATCCGCTTGGCATTGAAGGT GAGGCTGGCAGCAGCGAAGGTCTGGGGCTGTTTGAACGTTACACGAGTGTAACCGCCGAA CC ATGTTGACACGAATTC TG > Salmonella typhi GGTCGGCTTAGACTAGTGTGACCAAAAAGCTTTTGCTGAAGTTTCAGGGTAAGAAGAACC AGCTCCTAGTAAAAAGACTAT TGTGACTGAAAAGCGC GTCAGCGCAAAGCCGACCGCACA AAACGCACAAGGAGTTACAG > Pseudomonas aeruginosa ACGCGGCCAGGGTCTTCTCCTGCGAGATCATGCGCGGCGCGCCGCGCATG CCGGCGCCGC TGCTGG AACGCCTCGACCCCAGGGCTACACTAGTTTAACCGGAACGCCGCCAGTGGATCG GCCTGCCCCAGCTATTGCTC 8

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Gene regulation, protein networks and disease a computational perspective Ron Shamir School

Gene regulation, protein networks and disease a computational perspective Ron Shamir School

Gene regulation, protein networks and disease a computational perspective Ron Shamir School of Computer Science Tel Aviv University CPM Helsinki July 3 2012 1 1 Outline Finding regulatory motifs I, II, III Utilizing

923 views • 41 slides
Lecture 6: Regulatory genomics Gene regulation, chromatin accessibility, DNA regulatory code

Lecture 6: Regulatory genomics Gene regulation, chromatin accessibility, DNA regulatory code

6.874, 6.802, 20.390, 20.490, HST.506 Computational Systems Biology Deep Learning in the Life Sciences Lecture 6: Regulatory genomics Gene regulation, chromatin accessibility, DNA regulatory code Prof. Manolis Kellis Slides credit: 6.047,

946 views • 92 slides
Staphylococcus aureus Pathogenesis - Gene exchanges - Gene regulation - Gene products - Gene

Staphylococcus aureus Pathogenesis - Gene exchanges - Gene regulation - Gene products - Gene

Staphylococcus aureus Pathogenesis - Gene exchanges - Gene regulation - Gene products - Gene product cooperation Brussels 07 Some Staphylococcal Species from Mammals Host Species Coagulase Clumping factor Virulence Human ++ ++ +++ S.

678 views • 43 slides
Lecture 10 Gene Regulation I: Promoters and Control Circuits SPs: Figs 12-27, 28, 29, 32, 40, 44

Lecture 10 Gene Regulation I: Promoters and Control Circuits SPs: Figs 12-27, 28, 29, 32, 40, 44

Lecture 10 Gene Regulation I: Promoters and Control Circuits SPs: Figs 12-27, 28, 29, 32, 40, 44 Vocabulary:operon/regulatory gene/repressor/inducer/ co-repressor/derepression/positive vs. negative control/ cis vs. trans acting factors/deletion

443 views • 23 slides
Simultaneous Emergence of Cooperative Response and Mutational Robustness in Gene Regulatory

Simultaneous Emergence of Cooperative Response and Mutational Robustness in Gene Regulatory

Simultaneous Emergence of Cooperative Response and Mutational Robustness in Gene Regulatory Networks Macoto Kikuchi and Shintaro Nagata Cybermedia Center, Osaka University Outline Setup of the problem Gene expression and gene regulatory

712 views • 44 slides
Gene Regulation Bioinformatics Wyeth W. Wasserman University of British Columbia www.cisreg.ca

Gene Regulation Bioinformatics Wyeth W. Wasserman University of British Columbia www.cisreg.ca

Gene Regulation Bioinformatics Wyeth W. Wasserman University of British Columbia www.cisreg.ca The Grand Challenge: Reliably Define Cis-Regulatory Mechanisms of Regulons CO-EXPRESSED GROUPS EXPRESSION DATA SEQUENCE ANALYSIS BIRS 2006 2

792 views • 55 slides
Gene Regulation Bioinformatics Wyeth W. Wasserman University of British Columbia www.cisreg.ca

Gene Regulation Bioinformatics Wyeth W. Wasserman University of British Columbia www.cisreg.ca

Gene Regulation Bioinformatics Wyeth W. Wasserman University of British Columbia www.cisreg.ca The Grand Challenge: Reliably Define Cis-Regulatory Mechanisms of Regulons CLUSTERING EXPRESSION DATA SEQUENCE ANALYSIS Lake Barkley 2006 2

990 views • 83 slides
Bioinformatics: Network Analysis Kinetics of Gene Regulation COMP 572 (BIOS 572 / BIOE 564) -

Bioinformatics: Network Analysis Kinetics of Gene Regulation COMP 572 (BIOS 572 / BIOE 564) -

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

352 views • 20 slides
of Regulatory Network Ping Ma Department of Statistics & Institute for Genomic Biology

of Regulatory Network Ping Ma Department of Statistics & Institute for Genomic Biology

Nonparametric Modeling of Regulatory Network Ping Ma Department of Statistics & Institute for Genomic Biology University of Illinois Urbana-Champaign Central Dogma of Molecular Biology Transcription Regulation Gene A Gene B TF TF

475 views • 24 slides
Detecting Network Motifs in Gene Co-expression Networks Xinxia Peng Genome Science &

Detecting Network Motifs in Gene Co-expression Networks Xinxia Peng Genome Science &

Detecting Network Motifs in Gene Co-expression Networks Xinxia Peng Genome Science & Technology Program The University of Tennessee Oak Ridge Natl. Lab Motivation Modularity of Biological Networks Co-expression Network

424 views • 29 slides
Boolean Network Modeling Bioinformatics: Sequence Analysis COMP 571 - Spring 2015 Luay Nakhleh,

Boolean Network Modeling Bioinformatics: Sequence Analysis COMP 571 - Spring 2015 Luay Nakhleh,

Boolean Network Modeling Bioinformatics: Sequence Analysis COMP 571 - Spring 2015 Luay Nakhleh, Rice University Gene Regulatory Networks Gene regulatory networks describe the molecules involved in gene regulation, as well as their

910 views • 17 slides
Boolean models of gene regulatory networks Matthew Macauley Math 4500: Mathematical Modeling

Boolean models of gene regulatory networks Matthew Macauley Math 4500: Mathematical Modeling

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 a functional gene product (e.g.,

734 views • 22 slides
1 An old, but timely regulatory problem When I started working on regulation in the 1970s the

1 An old, but timely regulatory problem When I started working on regulation in the 1970s the

Regulation incentives for investment and technological change Ingo Vogelsang, Boston University Tenth ACCC Regulatory Conference The regulation of infrastructure in a time of transition Surfers Paradise, July 30, 2009 1

486 views • 15 slides
Regulatory Motif Prediction in DNA Regulatory Motif Prediction in DNA Introduction: toward

Regulatory Motif Prediction in DNA Regulatory Motif Prediction in DNA Introduction: toward

Regulatory Motif Prediction in DNA Regulatory Motif Prediction in DNA Introduction: toward transcription regulatory networks Ab initio discovery of motifs by over-representation of regular expressions The weight matrix

1.19k views • 65 slides
OVERVIEW: Regulation of gene expression by auxin 1. Intracellular binding of auxin 2. Targeted

OVERVIEW: Regulation of gene expression by auxin 1. Intracellular binding of auxin 2. Targeted

2018 CDB Part IB Plant Development Lecture 3 Regulation of gene expression by auxin Jim Haseloff Department of Plant Sciences (www.haseloff-lab.org/education) OVERVIEW: Regulation of gene expression by auxin 1. Intracellular binding

754 views • 41 slides
Regulation Update Emma Gilpin, Head of Regulatory Policy Judith Joy, Regulatory Lawyer

Regulation Update Emma Gilpin, Head of Regulatory Policy Judith Joy, Regulatory Lawyer

Regulation Update Emma Gilpin, Head of Regulatory Policy Judith Joy, Regulatory Lawyer Association of Consulting Actuaries: Sessional Meeting 13 September 2018 Headlines from Regulation Revised Actuaries Code Quality Assurance

403 views • 21 slides
Berhad Investor Relations Briefing 16 March 2020 Disclaimer This presentation may contain

Berhad Investor Relations Briefing 16 March 2020 Disclaimer This presentation may contain

Duopharma Biotech Berhad Investor Relations Briefing 16 March 2020 Disclaimer This presentation may contain forward-looking statements by Duopharma Biotech Berhad that reflect managements current expectations, beliefs, intentions or

1.01k views • 46 slides
What to do when there are no breakpoints? Christian G. Giske, MD/PhD Chair of EUCAST

What to do when there are no breakpoints? Christian G. Giske, MD/PhD Chair of EUCAST

What to do when there are no breakpoints? Christian G. Giske, MD/PhD Chair of EUCAST Professor/Chief consultant physician Karolinska Institutet and Karolinska University Hospital RICAI, Paris 18 December 2018 EUCAST developing

408 views • 30 slides
Proteomics pathway Proteomics pathway Sample Data Analysis Separation Selection of spot(s) G

Proteomics pathway Proteomics pathway Sample Data Analysis Separation Selection of spot(s) G

Proteomics pathway Proteomics pathway Sample Data Analysis Separation Selection of spot(s) G Q R E N K T M E Post-separation analysis ... NRTKGG ... Databases Data Processing Major steps in gel image Major steps in gel image

417 views • 19 slides
La nuova classifi ficazione WHO delle Sindromi mi Mielo Mielodisplas8c displas8che he Gina

La nuova classifi ficazione WHO delle Sindromi mi Mielo Mielodisplas8c displas8che he Gina

La nuova classifi ficazione WHO delle Sindromi mi Mielo Mielodisplas8c displas8che he Gina Zini, MD. PhD. Hematology Prof. FPG - Universit Ca>olica, Roma Learning Learning obje jec8ves 2016 WHO Updated Classification of

409 views • 20 slides
Library of synthetic 5' secondary structures to manipulate mRNA stability in Escherichia coli.

Library of synthetic 5' secondary structures to manipulate mRNA stability in Escherichia coli.

Library of synthetic 5' secondary structures to manipulate mRNA stability in Escherichia coli. Carrier TA, Keasling JD. Biotechnol Prog. 1999 Jan-Feb;15(1):58-64. Library of synthetic 5' secondary structures to manipulate mRNA stability in

467 views • 17 slides
Data integration Tyler M. Earnest July 19, 2018 Hands-On Workshop on Cell Scale Simulations,

Data integration Tyler M. Earnest July 19, 2018 Hands-On Workshop on Cell Scale Simulations,

Data integration Tyler M. Earnest July 19, 2018 Hands-On Workshop on Cell Scale Simulations, Urbana, IL 1 Introduction Not ab initio ! Required data Reactions Rate parameters Diffusion coefficients Geometry 2 Common

705 views • 34 slides
Networks: what? what for? how? https://mia.toulouse.inra.fr/NETBIO Julien Chiquet, tienne

Networks: what? what for? how? https://mia.toulouse.inra.fr/NETBIO Julien Chiquet, tienne

Networks: what? what for? how? https://mia.toulouse.inra.fr/NETBIO Julien Chiquet, tienne Delannoy, Marie-Laure Martin-Magniette, Franoise Monger, Guillem Rigaill & Nathalie Villa-Vialaneix Formation LIPM, Toulouse - April 27th 2017

1.17k views • 85 slides
The clinical and economic outcomes of whole genome sequencing availability on containing a

The clinical and economic outcomes of whole genome sequencing availability on containing a

The clinical and economic outcomes of whole genome sequencing availability on containing a hospital outbreak of resistant Escherichia coli Presented by Thomas Elliott 16 October 2019 Acknowledgments PA Hospital Infection Control Team Other

498 views • 21 slides

More recommend