Free biological warfare software for linux

BioJava is an open-source project dedicated to providing a Java framework for processing biological data. It include objects for manipulating sequences, file parsers, DAS client and server suport, access to BioSQL and Ensembl databases, and powerful analysis and statistical routines including a dynamic programming toolkit.

Bio::GMOD is a unified API for Model Organism Databases.

SYNOPSIS

Check the installed version of a MOD
use Bio::GMOD::Util::CheckVersions.pm
my $mod = Bio::GMOD::Util::CheckVersions->new(-mod=>WormBase);
my $version = $mod->live_version;
Update a MOD installation
use Bio::GMOD::Update;
my $mod = Bio::GMOD::Update->new(-mod=>WormBase);
$gmod->update();
Fetch a list of genes from a MOD
use Bio::GMOD::Query;
my $mod = Bio::GMOD::Query->new(-mod=>WormBase);
my @genes = $mod->fetch(-class=>Gene,-name=>unc-26);

Bio::GMOD is a unified API for accessing various Model Organism Databases. It is a part of the Generic Model Organism Database project, as well as distributed on CPAN.

MODs are highly curated resources of biological data. Although they typically incorporate sequence data housed at community repositories such as NCBI, they place this information within a framework of biological fuction gelaned from the published literature of experiments in model organisms.

Given the great proliferation of MODs, cross-site data mining strategies have been difficult to implement. Such strategies typically require a familiarity with both the underlying data model and the historical vocabulary of the model system.

Furthermore, the quickly-evolving nature of these projects have made installing a MOD locally and keeping it up-to-date a delicate and time-consuming experience.

Python Macromolecular Library (mmLib) is a software toolkit and library of routines for the analysis and manipulation of macromolecular structural models, implemented in the Python programming language.

Python Macromolecular Library is accessed via a layered, object-oriented application programming interface, and provides a range of useful software components for parsing mmCIF, and PDB files, a library of atomic elements and monomers, an object-oriented data structure describing biological macromolecules, and an OpenGL molecular viewer.

The mmLib data model is designed to provide easy access to the various levels of detail needed to implement high-level application programs for macromolecular crystallography, NMR, modeling, and visualization.

This includes specialized classes for proteins, DNA, amino acids, and nucleic acids. Also included is a extensive monomer library, element library, and specialized classes for performing unit cell calculations combined with a full space group library.

Americas Army is one of the five most popular action games played online. It provides players with the most authentic military experience available, from exploring the development of Soldiers in individual and collective training to their deployment in simulated missions in the War on Terror.
Americas Army: Special Forces is the follow-up to Americas Army: Operations, which was released on July 4, 2002.
In Americas Army: Special Forces, players attempt to earn Green Beret status by completing individual and collective training missions drawn from the Special Forces Assignment and Selection (SFAS) process.
Players who complete the SFAS process have the opportunity to take on elite Special Forces roles and are qualified to play in multiplayer missions with units ranging from the elite 82d Airborne Division to the 75th Ranger Regiment.
Includes the complete game Americas Army: Operations.
Main features:
- Authentic U.S. Army experience Realistic depiction of the values, units, equipment and career opportunities that make the Army the worlds premier land force continually updated to incorporate new occupations, units, technologies and adventures.
- Realistic roles Including Weapons Specialist (18B), Intelligence (18F), Engineer (18C), Communications (18E) and Combat Medic (18D).
- Challenging Green Beret training Complete training missions drawn from the SFAS process at Fort Bragg. Successfully complete SFAS and advance to Special Forces Qualification Course (Q-Course) missions to explore new Special Forces roles.
- Intense Special Forces action Intense Special Forces action Experience multiplayer missions in simulated combat environments. Take part in missions that span the capabilities of a Special Forces detachment, including unconventional warfare, direct action, surveillance and reconnaissance and Combat Search and Rescue.
- Detailed Special Forces equipment and military hardware Building on the equipment available in Americas Army: Operations, Americas Army: Special Forces adds the M4 Carbine featuring the Picatinny rail mod system for attaching laser-aiming devices and sighting systems; the MP5SD6 Remington 870 shotgun for forced entry; the AT4, a shoulder-fired anti-tank rocket and the BDM, a shoulder-fired bunker demolition munition.
- Accurate Soldier behavior Players are bound by the laws of land warfare, Army values (honor, duty and integrity) and realistic rules of engagement as they navigate challenges in teamwork-based multiplayer force vs. force operations. Mission accomplishment standings are evaluated based on team effort and adherence to a set of values and norms of conduct.

Xholon runtime framework executes applications that are event-driven or that have highly dynamic structure or behavior. Specify your models using XML and Java, or using third-party UML2 tools and MDA transformations.
To get started, read or actively work through the basic HelloWorld tutorial. Its a very simple application, but it demonstrates many of the main concepts.
For more detail on the concepts behind Xholon, you might want to read one of the papers thats been published. These describe how to model cells and other complex biological entities using tools designed for developing real-time and embedded systems.
This earlier work used Rational Rose RealTime and C++, rather than the current Java. Xholon is intended to be a runtime framework that can execute the same types of systems described in those papers, plus many more traditional non-biological event-driven systems.
The goal of the Cellontro sister project is to develop complex biological simulations using the Xholon framework. Most of the features described in the published papers have been re-implemented as Cellontro applications using Xholon.
Also have a look at the sample applications that are included with the Xholon software. These give an idea of the range of applications that can be supported by the Xholon runtime framework.
These have been employed as use cases to determine what functionality is most important in Xholon. The digital watch simulation is a good example of a Xholon application with a hierarchical state machine, developed using a UML modeling tool.
A Xholon is essentially a holon. A holon is an entity that lives within a hierarchical structure, and is both a whole and a part at the same time.
In mainstream computer science terms, a Xholon is a node in a tree. The node has a single parent, possibly one or more children, and possibly one or more siblings. A Xholon may also be an active agent able to interact in real-time with other Xholons in the tree.
In UML2 terminology, a Xholon is a structured classifier that may exist as a part within other structured classifiers, and that may in turn contain other structured classifiers as parts of itself. The result is a hierarchical containment structure, nested to an arbitrary number of levels.
As a part, a Xholon plays a specific role within another structured classifier. Xholons are UML classes that are subsequently refined using UML2 composite structure diagrams. Structured classifiers interact with each other through ports, by passing messages or by making function calls.
Using the more philosophical terminology used to describe holons, a Xholon is something that is simultaneously both a whole and a part. Since everything in the universe is a holon, then everything running within a computer application should be a Xholon. The term holon was invented by Arthur Koestler in 1967.
The Xholon Project is inspired by biological concepts. A major incentive behind the project is to build a run-time environment that is equally adapted to running simulations of biological systems, and to running more traditional real-time, embedded and other event-driven reactive systems.
Xholon applications may contain structures that are highly mutable. A Xholon is an active agent that can modify the tree structure in which it lives. It can navigate the tree to interact with any other node, it can add, delete or modify other nodes, it can exchange messages with other nodes, and it can move itself to another position within the tree.
The Xholon Project incorporates many concepts of the Real-time Object-Oriented Modeling (ROOM) methodology, much of which has been incorporated into UML2. At the same time, Xholon removes some of the limitations of ROOM to allow for greater flexibility, mutability and mobility of active objects.
The Xholon run-time can serve as a target for a Model Driven Architecture (MDA) transformation pipeline. MDA stresses the importance of models, and the ability to transform those models, through a series of steps, into an executing target system.
You can create your model using a UML tool such as Gentlewares Poseidon or NoMagics MagicDraw, save the model as an XMI file, transform it using XSLT (or by some other MDA means) into a Xholon model and application, and then execute the model.
Enhancements:
- UML state machine simulation capabilities have been extended, including animation, and fork, join, junction.
- Additional Agent-Based Modeling functionality is available.
- The NetLogo-like syntax has been enhanced.
- The architecture is more flexible and is ready to more fully support integration of multiple domains.
- Histograms and probability distributions are now available. Line charts update in real-time.
- Numerous other modeling, simulation, transformation, and execution features have been added.

Bio::GMOD::Admin::Monitor::blat is a Perl module that can monitor a BLAT server.

SYNOPSIS

Check the installed version of a MOD
use Bio::GMOD::Util::CheckVersions.pm
my $gmod = Bio::GMOD::Util::CheckVersions->new(-mod=>WormBase);
my $version = $gmod->live_version;
Update a MOD installation
use Bio::GMOD::Update;
my $gmod = Bio::GMOD::Update->new(-mod=>WormBase);
$gmod->update();
Build archives of MOD releases (coming soon...)
Do some common datamining tasks (coming soon...)

Bio::GMOD is a unified API for accessing various Model Organism Databases. It is a part of the Generic Model Organism Database project, as well as distributed on CPAN.

MODs are highly curated resources of biological knowledge. MODs typically incorporate the typical information found at common community sites such as NCBI. However, they greatly extend this information, placing it within a framework of experimental and published observations of biological function gleaned from experiments in model organisms.

Given the great proliferation of MODs, cross-site data mining strategies have been difficult to implement. Furthermore, the quickly-evolving nature of these projects have made installing a MOD locally and keeping it up-to-date a delicate and time-consuming experience.

Bio::GMOD aims to solve these problems by:

1. Making MODs easy to install
2. Making MODs easy to upgrade
3. Enabling cross-MOD data mining through a unified API
4. Insulating programmatic end users from model changes

NOTES FOR DEVELOPERS

Bio::GMOD.pm uses a generically subclass-able architecture that lets MOD developers support various features as needed or desired. For example, a developer may wish to override the default methods for Update.pm by building a Bio::GMOD::Update::FlyBase package that provides an update() method, as well as various supporting methods.

Currently, the only participating MOD is WormBase. The authors hope that this will change in the future!

This program places the required number of sodium ions around a system of electric charges, e.g., the atoms of a biological macromolecule (protein, DNA, protein/DNA complex).

The ions are placed in the nodes of a cubic grid, in which the electrostatic energy achieves the smallest values. The energy is re-computed after placement of each ion. A simple Coulombic formula is used for the energy:

Energy(R) = Sum(i_atoms,ions) Q_i / |R-R_i|

All the constants are dropped out from this formula, resulting in some weird energy units; that doesnt matter for the purpose of energy comparison. To speed the program up, the atoms of the macromolecule are re-located to the grid nodes, closest to their original locations.

The resulting error is believed to be minor, compared to that resulting from the one-by-one ions placement, or from using the simplified energy function. The coordinates of the placed ions are printed out in the PDB format for further usage.

It is recommended that the placed ions are equilibrated in a separate Monte Carlo or Molecular Dynamics simulation. Trivial modifications to the program should allow the placement of any combination of multivalent ions of different charges.