The Lattice Project, center for bioinformatics and computational biology, is a Grid computing project that carries out scientific research for many different organizations that study DNA sequence, Rapid Microorganism Identification, Avian Influenza, biological diversity in nature, protein docking algorithms, Molecular Biology and Evolution.
Lattice project project URL; http://boinc.umiacs.umd.edu/
About the Lattice project
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The Lattice Project is a Grid computing research project conducted by the Laboratory of Molecular Evolution at the University of Maryland. Michael Cummings has directed The Lattice Project from its inception in late 2003 to the present, and Adam Bazinet has been the primary developer.
The Lattice BOINC Project is hooked up to a general purpose shared grid system and no single project or application dominates the landscape. We will try to briefly outline the current science projects using the Lattice project grid.
The Cummings Laboratory (GARLI)
Genetic Algorithm for Rapid Likelihood Inference (GARLI) performs heuristic phylogenetic searches under the General Time Reversible (GTR) model of nucleotide substitution, with gamma distributed rate heterogeneity and a proportion of invariant sites. The Cummings Laboratory and others are using GARLI to infer evolutionary relationships based on DNA sequence data.
GARLI is loosely based on the program GAML (Lewis 1998). It uses a stochastic genetic algorithm-like approach to simultaneously find the topology, branch lengths and substitution model parameters that maximize the log-likelihood (lnL). This involves the evolution of a population of solutions termed individuals, with each individual encoding a tree topology, a set of branch lengths and a set of model parameters. Each individual is assigned a fitness based on its lnL score. Each generation random mutations are applied to some of the components of the individuals, and their fitness's are recalculated. The individuals are then chosen to be the parents of the individuals of the next generation, in proportion to their fitness's. This process is repeated many times, and the population of individuals evolves toward higher fitness solutions.
For more information, visit the Cummings Laboratory, the Marine Biological Laboratory or the GARLI website.
The Cummings Laboratory (GSI)
The Genealogical Sorting Index (GSI) is a statistic to quantify the common ancestry of labeled species on the tips of a tree. The Cummings Laboratory is using GSI to assess the performance of the statistic in a variety of situations.
There are several situations in which it is desirable to quantify the degree of exclusive association among observations in groups that are represented on a tree. For example, in the context of evolutionary biology, quantifying the degree of genealogical sorting and allelic histories is important for assessing the degree of differentiation of species and populations or sub populations, and is essential for understanding conservation status and priority of particular organisms. The genealogical sorting index (GSI) quantifies the historical relationships among groups for any genealogy to enable novel insight into the evolutionary process. It is intuitive, simple, and easily calculated. The software that calculates the genealogical sorting index and measures its significance is made freely available.
For more information, visit the Cummings Laboratory or the GSI web site or leptree.net.
The Edwards Laboratory (HMMPfam)
The Edwards Laboratory is using the Hidden Markov Model Protein Families (HMMPfam) service to compute Pfam assignments for all bacterial, plasmid, and virus protein sequences from Swiss-Prot, TrEMBL, GenBank, RefSeq, and TIGR's CMR, plus an inclusive set of all plausible Glimmer predictions from RefSeq bacterial genomes. These protein sequences, and their Pfam assignments, are used in the Rapid Microorganism Identification Database (www.RMIDb.org). The HMMPfam service is also being used as a model for "data-heavy" bioinformatics applications on the Lattice Grid infrastructure and is a collaboration between the Cummings and Edwards laboratories.
For more information, visit the Edwards Laboratory, Rapid Microorganism Identification Database ( www.rmidb.org ), the HMMER website or view a pdf file here 
.
Dr. Catherine Dibble's Computational Laboratories Group (Complab)
Dr. Catherine Dibble's Computational Laboratories Group (CompLab) uses agent-based simulation models to study the geographic spread of Avian Influenza across the United States, to quantify the relative pandemic risk of US cities and determine optimal intervention strategies. The Grid service being used is Complab.
Computational Laboratories Group is based in the Department of Geography at the University of Maryland. A computational laboratory is a well-specified simulation model coupled with careful experimental design and thorough testing.
Our group uses our GeoGraph agent-based computational laboratory extensions to RePast, and sometimes Genetic Algorithms, to conduct theory-driven explorations of distributed dynamic processes on richly-structured landscapes. Some of the dynamic processes we study include:
- agent-based models of epidemics and of the emergence of infectious diseases,
- economic geography and geographical economics,
- sector-driven models of long-run urban and regional development on networks,
- human-landscape interactions such as deforestation or the evolution of networks,
- shared-resource games and sustainability,
- spatial evolutionary game theory and the evolution of inequality,
- civil violence, and effective approaches to peacekeeping, and
- models of human and wildlife interactions surrounding parks and nature reserves.
Our landscapes range from synthetic network landscapes based on spatial small-worlds and scale-free networks to dynamic models of real world landscapes derived from Geographic Information Systems (GIS) or Remote Sensing (RS).
For more information, visit the Computational Laboratories Group website or view a pdf file here .
Maile Neel and Joanna Grand (Marxan)
Maile Neel and Joanna Grand are using Marxan to quantify the effects of poor and incomplete data on the ability to capture biological diversity in nature reserves.
MARXAN is software that delivers decision support for reserve system design. MARXAN finds reasonably efficient solutions to the problem of selecting a system of spatially cohesive sites that meet a suite of biodiversity targets. Given reasonably uniform data on species, habitats and/or other relevant biodiversity features and surrogates for a number of planning units (as many as 20,000) MARXAN minimizes the cost (a weighted sum of area and boundary length, Possingham, Ball and Andelman 2001) while meeting user-defined biodiversity targets.
For more information, visit the MARXAN website or view a pdf file here .
The Laboratory of David Fushman (CNS)
The Laboratory of David Fushman runs Crystallography & NMR Systems (CNS) which are protein docking algorithms on the Lattice grid. When driven by experimentally derived constraints, these will help in modeling the structures of large multi-subunit proteins, and the interactions of such proteins with various ligands. CNS is the featured Grid service in this project.
Crystallography & NMR System (CNS) is the result of an international collaborative effort among several research groups. The program has been designed to provide a flexible multi-level hierarchical approach for the most commonly used algorithms in macromolecular structure determination. Highlights include heavy atom searching, experimental phasing (including MAD and MIR), density modification, crystallographic refinement with maximum likelihood targets, and NMR structure calculation using NOEs, J-coupling, chemical shift, and dipolar coupling data.
For more information, visit the CNS web site, the laboratory of David fushman or view a pdf file here .
Laboratory of Sarah Tishkoff (MDIV and IM)
Floyd Reed and Holly Mortensen from the Laboratory of Sarah Tishkoff have run a number of Migration Divergence (MDIV) and Isolation Model (IM) simulations through The Lattice Project. These are studies in molecular population genetics that seek to use DNA sequence polymorphism to estimate the times of divergence and migration rates among ethnically diverse human populations in Africa.
Migration Divergence (MDIV) is a program that will simultaneously estimate divergence times and migration rates between two populations under the infinite sites model or under a finite sites model. Please note that this resource is not considered part of the Lattice project.
Isolation Model (IM) is a program, written with Rasmus Nielsen, for the fitting of an isolation model with migration to haplotype data drawn from two closely related species or populations. IM is based on a method originally developed by Rasmus Nielsen and John Wakeley (Nielsen and Wakeley 2001 GENETICS 158:885). Large numbers of loci can be studied simultaneously, and different mutation models can be used.
For more information, visit the Migration Divergence (MDIV) website or the Isolation Model (IM) website or read History of Click-Speaking Populations of Africa.
Evolution - the Journey of Life - Living Together
This is a documentary by the BBC about nature and evolution. GARLI by the Cummings Laboratory attempts infer evolutionary relationships based on DNA sequence data.
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