Software
Cell ID
Cell-ID is an open-source cell-finding, tracking, and analysis package. It is set up to identify relatively regularly shaped cells that produce dark boundaries in brightfield images. In its current implementation, it is fine-tuned to find yeast cells and is able to separate growing buds as new daughter cells while maintaining the ability to distinguish cells with large mating projections as single cells.
The code takes the pixels of each found cell and copies them over to corresponding fluorescence images, allowing for slight cell shifting of the individual cells between the bright field and fluorescence images, as well as some overall differences in image registration. It then calculates a number of single cell variables using both the fluorescence image(s) and the brightfield image (such as area, volume, fluorescence in various cellular annuli, etc). Each of the algorithms can be easily modified and other algorithms and statistics added. For time course studies, the individual labeled cells are tracked through the time course.
The output of the program is an image annotated with cell boundaries and numbers, as well as a series of tables in text format containing all the calculated data. We have been analyzing the data from such files using the open source analysis package PAW, and we have included a number of PAW scripts to aid in using PAW. Since the program output is in text format, other analysis packages (such as, R or ROOT can be easily used.
Cell ID is made available to the public through the GNU Lesser General Public License. Please see the README file for instructions on how to install and run Cell ID.
References:
Gordon A, Colman-Lerner A, Chin TE, Benjamin KR, Yu, R.C., Brent R: (2007) Single-cell quantification of molecules and rates using open-source microscope-basd cytometry. Nat Methods, [PDFs: article, supplement 1, supplement 2, supplement 3, supplement 5].
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Monod
Monod is an extensible knowledge management and collaboration
web application. Monod stands for MOdeler's NOtebook
and Datastore in honor of Jacob Monod, the pioneering molecular
biologist who, with Francois Jacob, abstracted the relevant
details from their experiments that enabled them to devise
the Lac operon model. Monod was designed to aid researchers
in extracting, annotating, and organizing relevant data about
biological systems and to model system behavior. The user
can input data about molecular species and the types of reactions
they undergo. The user can specify post-translational modification
states and the reactions that depend on those states. Each
of the species can be described using keywords to facilitate
searching, and species and reactions can be annotated with
user comments and linked to relevant citations. The citations
can also be annotated with the user's comments. The
Monod web application is built with open source components
including the underlying PostgresSQL 7.3 database. The user
can set access parameters to allow others access to the database.
When deployed as a shared resource Monod is a functional
data dissemination and collaboration environment. Monod was
originally designed to model an intracellular signaling system,
however it can be easily modified to include other data types
to facilitate modeling of other biological processes. Monods
extensible design also facilitates its integration into a
network of software, such as simulators and data warehouses.
Monod is available to the public through the Lesser Gnu
Public License.
References:
Soergel, D., George, B., Morgan-Linial, R., and Brent, R.
(2004) Monod, a tool to support collaborative modeling
of biological processes. [ PDF ]
Moleculizer 1.0
Moleculizer
1.0 is a non-spatial stochastic simulator for
cellular reaction networks. It was designed to enable a researcher
to simulate the behavior of intracellular signaling systems
over time and in response to defined perturbations. The outcomes
of the simulations can then be tested experimentally. A
unique feature of Moleculizer is that it generates species
and reactions, as they are needed. This 'on- the-fly' mode
saves time by increasing computational speed and reducing manual
input by the researcher. Moleculizer uses Monte Carlo methods
to simulate the reactions. Moleculizer models can be exported
as SBML files that can be subsequently imported into other
modeling and simulation software such as ECell.
Moleculizer 2.0 will be released in late 2006/early 2007.
Significant new features of version 2.0 include a spatial stochastic
simulator (Compartmentalizer) and replacement of loadable reaction
modules with a simplified suite of reaction generators. Moleculizer
2.0 has the capacity to generate reaction networks without
simulation. These reaction networks can then be loaded into
other simulation software.
Moleculizer is available to the public under the Lesser GNU
Public License.
References:
Lok, L and Brent, R. Automatic generation of cellular
reaction networks with Moleculizer 1.0. Nature Biotechnology,
1:131-136 (2005).
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