140 CATALYZING INQUIRY
There are many different tools for simulating and analyzing models of cellular systems (Table 5.1).
More general tools, such as Mathematica and MATLAB or other systems that can be used for solving
systems of differential or stochastic-differential equations, can be used to develop simulations, and
because these tools are commonly used by many researchers, their use facilitates the transfer of models
among different researchers. Another approach is to link data gathering and biological information
systems to software that can integrate and predict behavior of interacting components (currently, re-
searchers are far from this goal, but see Box 5.5 and Box 5.6). Finally, several platform-independent
model specification languages are under development that will facilitate greater sharing and
interoperability. For example, SBML,^42 Gepasi,^43 and CellML^44 are specialized systems for biological
and biochemical modeling. Madonna^45 is a general-purpose system for solving a variety of equations
(differential equations, integral equations, and so on).
Rice and Stolovitzky describe the task of inferring signaling, metabolic, or gene regulatory path-
ways from experimental data as one of reverse engineering.^46 They note that automated, high-through-
put methods that collect species- and tissue-specific datasets in large volume can help to deal with the
risks in generalizing signaling pathways from one organism to another. At the same time, fully detailed
kinetic models of intracellular processes are not generally feasible. Thus, one step is to consider models
that describe network topology (i.e., that identify the interactions between nodes in the system—genes,
proteins, metabolites, and so on). A model with more detail would describe network topology that is
causally directional (i.e., that specifies which entities serve as input to others). Box 5.7 provides more
detail.
TABLE 5.1 Sample Simulation Programs
Name Descriptorsa Web Site
Gepasi/Copasi fkFW http://gepasi.dbs.aber.ac.uk/softw/gepasi.html
BioSim qWMU http://www.molgen.mpg.de/~biosim/BioSim/BioSimHome.html
Jarnac krfbFWS http://members.tripod.co.uk/sauro/Jarnac.htm
MCELL rsU http://www.mcell.cnl.salk.edu/
Virtual Cell ksDFWMU http://www.nrcam.uchc.edu/
E-Cell kWUS http://www.e-cell.org/
Neuron ksFWMUS http://neuron.duke.edu/
Genesis ksUS http://www.bbb.caltech.edu/GENESIS/genesis.html
Plas kfbFW http://correio.cc.fc.ul.pt/~aenf/plas.html
Ingeneue qkFMWUS http://www.ingeneue.org/
DynaFit kfW http://www.biokin.com/dynafit/
Stochsim rS http://www.zoo.cam.ac.uk/comp-cell/StochSim.html
T7 Simulator kUS http://virus.molsci.org/t7/
Molecularizer/Stochastirator krUS http://opnsrcbio.molsci.org/alpha/comps/sim.html
NOTE: All packages have facilities for chemical kinetic simulation of one sort or another. Some are better designed for metabolic
systems, others for electrochemical systems, and still others for genetic systems.
aThe descriptors are as follows: b, bifurcation analyses and steady-state calculation; f, flux balance or metabolic control and
related analyses; k, deterministic kinetic simulation; q, qualitative simulation; r, stochastic process models; s, spatial processes; D,
database connectivity; F, fitting, sensitivity, and optimization code; M, runs on Macintosh; S, source code available; U, runs on
Linux or Unix; W, runs on windows.
(^42) See http://www.cds.caltech.edu/erato/sbml/.
(^43) See http://www.gepasi.org/.
(^44) See http://www.cellml.org/.
(^45) See http://www.berkeleymadonna.com/.
(^46) J.J. Rice and G. Stolovitzky, “Making the Most of It: Pathway Reconstruction and Integrative Simulation Using the Data at
Hand,” Biosilico 2:70-77, 2004.