Teaching

Code and Algorithms

Research

Outlook Mail

Home

Shared

Solar Power

Electrical Usage

Website Usage

Bandwidth Usage

Web Proxy {Internal}

Spam Management

Email Management

ComputerTime

Bio

Photographs

Cameras

Matthew Fricke

Teaching Assistantships

CS151: Computer Programming Fundamentals with C++
CS152: Computer Programming Fundamentals with Java
CS201: Discrete Mathematics
CS251: Intermediate Programming with Java
CS261: Mathematical Foundations of Computer Science
CS523: Complex Adaptive Systems

Interests and Research

T cell Motility
PowerSearch3D Videos (MP4, MOV)

Ant Colonies as a Model of Human Computation, Melanie Moses, Tatiana Flanagan, Kenneth Letendre and G. Matthew Fricke, Handbook of Human Computation, Springer, 2014.

From Microbiology to Microcontrollers: Robot search patterns inspired by T cell movement, G. Matthew Fricke, Francois Asperti-Boursin, Joshua P. Hecker, Cannon Judy L., and Melanie E. Moses. Proceedings of the 12th European Conference on Artificial Life, The MIT Press, 2013.

Quantifying the Effect of Colony Size and Food Distribution on Harvester Ant Foraging, Tatiana P. Flanagan, Kenneth Letendre, William R. Burnside, G. Matthew Fricke, and Melanie E. Moses, PLoS ONE, 2012.

Pogonomyrmex Rugosus Pogonomyrmex Rugosus

Ant Colony Algorithms (ACOs) are used in shortest-path problem domains such as chip design and network routing. We investigated how three species of the desert harvester ant (Pogonomyrmex Rugosus, Maricopa, and Desertorum) share information about their surroundings, especially with regard to the scaling of information sharing with colony size. This information was compared to computational models of various foraging strategies.

iAnt Autonomous Robot

The iAnt uses search patterns inspired by Pogonomymex sp.
iAnt Demo Video.
Instructions for building an iAnt.

GetBonNie for building, analyzing, and sharing rule-based models, Bin Hu; G. Matthew Fricke; James R. Faeder; Richard G. Posner; William S. Hlavacek Bioinformatics 2009; doi: 10.1093/bioinformatics/btp173 published by Oxford Journals

BioNetGen: Biochemical reaction networks are central to understanding and influencing the operation of biological cells. GetBonNie (http://getbonnie.org) and BioNetGen (http://www.bionetgen.org) automate the development of reaction networks so that biologists can create and study detailed models of cellular biochemistry. Poster. Video.

You can download the software here: OS X, Windows. Be warned however that these distributions are of RuleBuilder 1.50 Beta bundled with BioNetGen 2.0.28, which are out of date. For the latest builds please go to the bionetgen website.

Receptor aggregation by intermembrane interactions: A Monte Carlo study
G. Matthew Fricke; James L. Thomas
Biophysical Chemistry
Volume 119, Issue 2, 20 Jan 2006; Pages 205-211

T-Cell T-Cell Targeting Infected Cell CemPro: Macrophages and T-cells communicate with one another at the cell surface through ligands and receptors. The proteins that form the ligands and receptors cluster during signaling. The mechanism for this clustering is unknown.

We model proteins on the cell surface with a Metropolis Monte Carlo lattice and measure the conditions under which clustering occurs. We then bring two virtual cell membranes into contact and measure the cross membrane binding forces needed to cause protein phase separation. Paper in Biophysical Chemistry. PowerPoint Web Slideshow. PDF Slideshow.


KomPhy: Algorithms for reconstructing phylogenies from character sequences encounter enormous search spaces even for small numbers of taxa. Techniques are needed to speed up the exploration of these spaces so that larger problems can be approached.  In the past neural networks have provided a framework for tackling complex problems very quickly. My thesis describes and tests a new neural network approach to phylogenetic reconstruction called KomPhy. Thesis. Slideshow. Software.

 

CultureAL: Semester project for David Ackley's Artificial Life course at UNM. CulturAL uses a very simple framework to explore the interaction of learned traits (directly and through peer emulation) and biological traits. Processes such as 'shielding' and the Baldwin Effect are explored. Learning in this model benefits the population as a whole through emulation but is counterproductive for the learner who is much better off copying others and procreating. The population as a whole evolved to minimize direct learning and maximize emulation of peers relying on the background mutation rate to produce enough 'learners' to feed the emulation strategy.. The most prevalent impact shared knowledge has on genetic evolution is shielding, which allows populations to survive that would otherwise die out and allows genetic mutation to explore the fitness landscape with more freedom. Presentation.

 

Madcat: Extension of the Copycat AI framework to a robotic path finding problem which formed the basis of Joseph Lewis' PhD. Starcat is Joseph's current research. Madcat NASA Student Conference Paper.

Bio

I am a PhD graduate student at the University of New Mexico Computer Science Department.

BA in Anthropology from Appalachian State University.
BS in Math from the University of New Mexico.
MS in Computer Science from the University of New Mexico.

My wife, Suzanne, and I have four boys: Henry, Leo, Owen, and Tristan. We live in Albuquerque, New Mexico, USA. I was born and raised in Shrewsbury, UK, but have lived in Mount Airy, North Carolina and Albuquerque, New Mexico for most of my life. I have been interested in computers since playing with a Commodore 64 when I was ten years old.

Go Figure Software is my contracting company. Through Go Figure Software I have provided programming services to scientists at the UNM Physics Department and Los Alamos National Labs.

 

Photographs

Tikal, Guatemala

 

Monaghan, Ireland

 

 

New Mexico

 

 

 

Mt. St. Helens, Oregon

 

 

North Carolina

 

 

San Deigo, California

 

Yr Wyddfa (Snowdon) and Anglesey, Wales

 

 

Shrewsbury, England

 

                                                                    Charles Darwin's Childhood Home

 

Edinburgh, Scotland

 

 

Teotihuican, Mexico

 

 

Japan

 

 

Moscow, Russia

 

 

Martinique