Loyola Marymount Unviersity

Slides:

Advertisements

Similar presentations

Britain Southwick Nicole Anguiano March 29, 2014

Advertisements

Visualization of hidden node activity in a feed forward neural network Adam Arvay.

T T07-01 Sample Size Effect – Normal Distribution Purpose Allows the analyst to analyze the effect that sample size has on a sampling distribution.

T T18-09 Line Plot (by Observation) Purpose Allows the analyst to visually analyze up to 5 time series plots on a single graph data samples by.

Modeling the Gene Expression of Saccharomyces cerevisiae Δcin5 Under Cold Shock Conditions Kevin McKay Laura Terada Department of Biology Loyola Marymount.

Software Refactoring and Usability Enhancement for GRNmap, a Gene Regulatory Network Modeling Application Mathematical Model Equation 2. Equation 3. Future.

Determining the Identity and Dynamics of the Gene Regulatory Network Controlling the Response to Cold Shock in Saccharomyces cerevisiae June 24, 2015.

Improvements to GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks Nicole Anguiano*, Anindita Varshneya**, Kam D. Dahlquist**,

Deletion of ZAP1 as a transcriptional factor has minor effects on S. cerevisiae regulatory network in cold shock KARA DISMUKE AND KRISTEN HORSTMANN MAY.

A COMPREHENSIVE GENE REGULATORY NETWORK FOR THE DIAUXIC SHIFT IN SACCHAROMYCES CEREVISIAE GEISTLINGER, L., CSABA, G., DIRMEIER, S., KÜFFNER, R., AND ZIMMER,

GRNmap Testing Analysis Grace Johnson and Natalie Williams June 10, 2015.

GRNmap Testing Grace Johnson and Natalie Williams June 3, 2015.

Changes in Gene Regulation in Δ Zap1 Strain of Saccharomyces cerevisiae due to Cold Shock Jim McDonald and Paul Magnano.

GRNmap and GRNsight June 24, Systems Biology Workflow DNA microarray data: wet lab-generated or published Generate gene regulatory network Modeling.

Data Analysis and GRNmap Testing Grace Johnson and Natalie Williams June 24, 2015.

Creating a Gene Regulatory Network Comparing a Wild Type Strain with a Mutant ΔGLN3 Deletion in S. cerevisiae Showed that ΔGLN3 Exhibits No Meaningful.

Index Slide 2-5: Statistical testing results 6-14: Clustering results 15-17: GRNsight visualization of YEASTRACT results 18-20: GRNmap output visualization.

GRNmap Testing Grace Johnson and Natalie Williams June 17, 2015.

Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae Vu, T. T.,

Comparing the Dynamics of the Cold Shock Gene Regulatory Network in Yeast with a Random Network K. Grace Johnson 1, Natalie E. Williams 2, Kam D. Dahlquist.

Student: Trixie Anne M. Roque, Tessa A. Morris Faculty Mentors: Dr. Kam D. Dahlquist, Dr. Ben G. Fitzpatrick, & Dr. John David N. Dionisio SURP 2015 Final.

Within Strain ANOVA WTdHAP4 p < (38.4%)2387 (38.6%) p < (24.7%)1489 (24.1 %) p < (13.8%)679 (11.0%) p < (7.25%)240.

Comparison of the wild type of S. cerevisiae and S. paradoxus Karina Alvarez and Natalie Williams.

Outline S. cerevisiae, a eukaryote known for cold-shock adaption, used in cold-shock experiments Deletion strand HMO1 and the comparison of microarray.

Comparison of the wild type of S. cerevisiae and S. paradoxus Karina Alvarez and Natalie Williams.

Systems modeling and statistical analysis allows comparison in the response to cold shock in Saccharomyces cerevisiae between Δhap4-derived and randomly.

Departments of Biology and Mathematics

Student: Trixie Anne M. Roque, Tessa A. Morris

Eddie Azinge, John Lopez, and Corinne Wong

From: Rat performance on visual detection task modeled with divisive normalization and adaptive decision thresholds Journal of Vision. 2011;11(9):1. doi: /

UML Activity Diagram Documents the Flow the of Program

Evaluating Hap4’s Role in the Gene Regulatory Network that Controls the Response to Cold Shock in Saccharomyces cerevisiae using GRNmap K. Grace Johnson1,

AP ECONOMICS: October 4 Warm-up: List the Aggregate Demand and Aggregate Supply curve shifters (aka determinants) by memory A.P. Economics.

Week 14 Assignment Kara Dismuke.

Ahnert, S. E., & Fink, T. M. A. (2016). Form and function in gene regulatory networks: the structure of network motifs determines fundamental properties.

Evaluating Hap4’s Role in the Gene Regulatory Network that Controls the Response to Cold Shock in Saccharomyces cerevisiae using GRNmap K. Grace Johnson1,

Deletion of ZAP1 as a transcriptional factor has minor effects on S

Week 14 Assignment Kara Dismuke.

شاخصهای عملکردی بیمارستان

Modeling Nitrogen Metabolism in Yeast

1 Department of Engineering, 2 Department of Mathematics,

Biomathematical Modeling: The Deletion of HMO1 and its Effect on Cold Shock Reaction in S. cerevisiae Lauren Magee & Lucia Ramirez Department of Mathematics.

UML Activity Diagram Documents the Flow the of Program

dCIN5 and Wildtype Transcription Factor Mapping in Cold Shock

1 Department of Engineering, 2 Department of Mathematics,

Change in Expression after modifying fix_b?

مدل زنجیره ای در برنامه های سلامت

Week 14 Output Figures Kristen Horstmann.

فرق بین خوب وعالی فقط اندکی تلاش بیشتر است

1 Department of Engineering, 2 Department of Mathematics,

Loyola Marymount University

Change in Expression after modifying fix_b?

Change in Expression after modifying fix_b?

Loyola Marymount University

GRNmap Testing and Results

Nicole Anguiano and Anu Varshneya

Change in Expression after modifying fix_b?

Week 11 Assignment Figures

AP ECONOMICS: October 11 Warm-up List the AD and AS shifters by memory A.P. Economics Learning Target: In order to understand how equilibrium national.

dCIN5 and Wildtype Transcription Factor Mapping in Cold Shock

Consolidation of Gephi Stats

Evaluating Hap4’s Role in the Gene Regulatory Network that Controls the Response to Cold Shock in Saccharomyces cerevisiae using GRNmap K. Grace Johnson1,

Week 11 Assignment Figures

(Within-Strain ANOVA)

Week 14 – Network Modeling

AP ECONOMICS: February 22

Consolidation of Gephi Stats

There are 16 Different Combinations for the Test Inputs

Loyola Marymount University

Presentation transcript:

Loyola Marymount Unviersity Running GRNmap to analyze S. cerevisiae and S. paradoxus’ response to cold shock Natalie Williams Biology Department Loyola Marymount Unviersity May 26, 2015

Outline Estimated b seemed to have sharper effects, seen in the output graphs. Changing the threshold (b) value altered the normalized weights of specific target-regulator interactions. Estimation of b values also resulted in changes in the magnitudes of influence for each interaction.

YHP1 Fixed b Estimated b

STB5 Fixed b Estimated b

CIN5 Fixed b Estimated b

HAP4 Fixed b Estimated b

HMO1 Fixed b Estimated b

Outline Estimated b seemed to have sharper effects, seen in the output graphs. Changing the threshold value (b) altered the normalized weights of specific target-regulator interactions. Estimation of b values also resulted in changes in the magnitudes of influence for each interaction.

For this GRN, the b value impacts the optimized weights, seen when they normalized and then visualized in GRNSight. Estimated b Fixed b These are not from the input files being run separately - MATLAB gave red error messages after the individual strains had be run through it

Outline Estimated b seemed to have sharper effects, seen in the output graphs. Changing the threshold value (b) altered the normalized weights of specific target-regulator interactions. Estimation of b values also resulted in changes in the magnitudes of influence for each interaction.

Summary Estimation of b has a noticeable effect on this constructed GRN The two strains have different behaviors, but the output graphs did not exemplify those divergences