The Standard Genetic Code Enhances Adaptive Evolution of Proteins
March 11-21, 2004
Clients: Wen Zhu and Dr. Stephen Freeland, Department of Biological Sciences, UMBC
Consultant: Dr. Matthias K. Gobbert, Mathematics
Description: The standard genetic code, by which most organisms translate genetic material into protein metabolism, is non- randomly organized. The Error Minimization hypothesis posits that natural selection produced a code that buffers genomes against the impact of mutations. However, previous studies supporting this hypothesis treat the code as an isolated trait, ignoring its influence on the evolution of the protein coding genome that it serves. A population genetic model of molecular evolution was developed to test the rate of adaptive gene evolution under different genetic codes. It is shown that the pattern of codon assignments has a profound effect on the speed of adaptive evolution, and a fundamental re-interpretation of the adaptive genetic code is offered, from one that minimizes errors to one that enhances the efficacy of natural selection. CIRC helped this work by performing the extensive numerical simulations required.
Additional outcomes: This project led to a publication:
- Wen Zhu and Stephen Freeland. The Standard Genetic Code Enhances Adaptive Evolution of Proteins. Submitted.
Numerical Simulations of a Current Neural Network Model
March 18, 2004 to May 10, 2004
Client: Dr. Jonathan Bell, Department of Mathematics and Statistics, UMBC
Consultant: Zorayr Manukyan, Mathematics
Description: A certain integro-differential equation provides a model describing electrical activity of neural net. A numerical method for solving it was developed. The relationship between parameters in the model resulted in different types of wave phenomena.
This project was conducted as part of the consulting class Math/Stat 750 in Spring 2004 under the supervision of facilitator Dr. Matthias K. Gobbert.
Cluster Analysis for Compositional Data
March 18, 2004 to May 10, 2004
Client: Dr. Erle Ellis, Department of Geography and Environmental Systems, UMBC
Consultant: Ronny O. Vallejos, Statistics
Description: Cluster analysis is a statistical technique of interest in many different fields for grouping data. This work presented a clustering technique to deal with compositional data, i.e. data consisting of vectors in which the components are proportions adding up to one. Various strategies for replacing zeros in the data were discussed. To illustrate the method we presented the analysis of a data set from the Yangtze Plain region in China.
This project was conducted as part of the consulting class Math/Stat 750 in Spring 2004 under the supervision of the facilitator Dr. Nagaraj K. Neerchal.
Data Analysis for Concentration of Atrazine in Ground Water and Area of Influence
March 18, 2004 to May 10, 2004
Client: Earl Greene, U.S. Geological Survey, Baltimore, MD
Consultants: Justin Newcomer and Rupa Bhensdadia, Statistics
Description: Atrazine (a pesticide) is used to control broadleaf and grassy weeds. The U.S. Environmental Protection Agency has set some standards for maximum contaminant level of Atrazine in drinking water. It is assumed that the response variable, concentration of Atrazine, can be predicted with logistic regression. The model was fit using various explanatory variables, collected for sample units. Also, an analysis was done to find the area of influence for the above stated study.
This project was conducted as part of the consulting class Math/Stat 750 in Spring 2004 under the supervision of facilitator Dr. Nagaraj K. Neerchal.
Sensitivity Analysis for a Plague Model
March 18, 2004 to May 10, 2004
Client: Dr. Holly Gaff, Dynamics Technology, Inc., Arlington, VA
Consultants: Stephen Clark and Jonathan Desi, Mathematics
Description: Plague is considered a prominent disease threat. In the past, outbreaks have affected large amounts of the population. Today, the threat of plague being used as a biological warfare and terrorism agent is a realistic possibility. Researchers have studied this topic and developed a mathematical model of differential equations to understand how plague behaves. Sensitivity analysis on certain parameters in the model was performed to see how the behavior is affected. This analysis will hopefully help researchers in this area better understand the behavior of plague.
This project was conducted as part of the consulting class Math/Stat 750 in Spring 2004 under the supervision of facilitator Dr. Matthias K. Gobbert.