CellGeo: a computational platform for cell edge analysis
by Denis Tsygankov (firstname.lastname@example.org)
This tool was created as part of collaborative research efforts between the laboratories of Tim Elston (Pharmacology, UNC), Klaus Hahn (Pharmacology, UNC) and Mark Peifer (Biology, UNC) to study complex cell edge dynamics. CellGeo provides a user-friendly graphical interface for automated tracking and analysis of both thin (filopodia) and broad (lamellipodia) protrusions. Importantly, the tool rigorously defines, measures, and tracks filopodia. It also provides an efficient method for separating filopodial dynamics from large-scale protrusive motion associated with lamelipodia, allowing simultaneous analysis of both cellular behaviors. Using the software does not require any computer programming skills.CellGeo website
We are also interested in developing computational tools for studying stochastic effects in signaling pathways and gene expression. With David Adalsteinsson (Applied Mathematics, UNC), we have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variable are simulated using an optimized implementation of the Gillespie algorithm. For the continous random variables, BioNetS constructs and numerically solves the appropriate chemical langevin equations. The software package has been designed to scale efficiently with network size, thereby allowing large systems to be studied.
Software for stochastic modeling of biochemical networks
With the increasing interest in formulating accurate models of large biochemical networks, there is a need for reliable software packages that correctly incorporate stochastic effects, yet are fast enough to simulate large interconnected sets of reacting species (as found, for example, in signaling cascades or genetic regulatory networks). We have developed the BIOchemical NETwork Stochastic Simulator, "BioNetS," to meet this need. BioNetS is capable of performing full discrete simulations using an efficient implementation of the Gillespie algorithm. It is also able to set up and solve the chemical Langevin equations, which are a good approximation to the discrete dynamics in the limit of large abundances. Finally, BioNetS can handle hybrid models in which chemical species that are present in low abundances are treated discretely, whereas those present at high abundances are handled continuously. Thus, the user can pick the simulation method that is best suited to their needs. All aspects of the software are highly optimized for efficiency.Adalsteinsson D, McMillen D, Elston TC. Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks. BMC Bioinformatics. 2004 Mar 8; 5:24
(Pubmed | Journal)
Official BioNetS website here.