MOTIVATION: Many advances in biomedical research are driven by structural analysis, which investigates interconnections between elements in biological systems (e.g. structural analysis of proteins to infer their function). Herein, we consider subnet discovery in chemical reaction networks (CRNs)-discovering a subset of a target CRN, i.e. structurally identical to a reference CRN. Structural analysis techniques such as motif finding and graph mining look for small, arbitrary, and commonly occurring substructures (e.g. three gene feedforward loops). In contrast, subnet discovery looks for larger, specific, and infrequently occurring substructures (e.g. 10 reactions mitogen-activated protein kinase (MAPK) pathway). RESULTS: We introduce pySubnetSB, an open source Python package for discovering subnets in CRNs that are represented in the Systems Biology Markup Language (SBML) community standard. We show that pySubnetSB achieves large reductions in computational complexity for subnet discovery. For example, in studies of randomly selected target networks with 100 reactions each with a random reference network with 20 reactions, computations are reduced from an infeasible 1078 evaluations to a more practical 108 evaluations. We develop a methodology for assessing the statistical significance of subnet discovery. Last, we study subnets in BioModels for approximately 200 000 pairs of reference and target models. We show that for a reference MAPK pathway, subnet discovery correctly indicates the presence of MAPK function in several target models. The studies also suggest two interesting hypotheses: (a) the potential presence of hidden oscillators in several models in BioModels, and (b) the possibility of a conserved mechanism for intracellular immune response. AVAILABILITY AND IMPLENETATION: pySubnetSB is installed using pip install pySubnetSB, and is hosted at https://github.com/ModelEngineering/pySubnetSB/.

MOTIVATION: Smoldyn is a particle-based biochemical simulator that is frequently used for systems biology and biophysics research. Previously, users could only define models using text-based input or a C/C++ application programming interface (API), which were convenient, but limited extensibility. RESULTS: We added a Python API to Smoldyn to improve integration with other software tools, such as Jupyter notebooks, other Python code libraries and other simulators. It includes low-level functions that closely mimic the existing C/C++ API and higher-level functions that are more convenient to use. These latter functions follow modern object-oriented Python conventions. AVAILABILITY AND IMPLEMENTATION: Smoldyn is open source and free, available at http://www.smoldyn.org and can be installed with the Python package manager pip. It runs on Mac, Windows and Linux. Documentation is available at http://www.smoldyn.org/SmoldynManual.pdf and https://smoldyn.readthedocs.io/en/latest/python/api.html.

Motivation: Smoldyn is a spatial and stochastic biochemical simulator. It treats each molecule of interest as an individual particle in continuous space, simulating molecular diffusion, molecule-membrane interactions and chemical reactions, all with good accuracy. This article presents several new features. Results: Smoldyn now supports two types of rule-based modeling. These are a wildcard method, which is very convenient, and the BioNetGen package with extensions for spatial simulation, which is better for complicated models. Smoldyn also includes new algorithms for simulating the diffusion of surface-bound molecules and molecules with excluded volume. Both are exact in the limit of short time steps and reasonably good with longer steps. In addition, Smoldyn supports single-molecule tracking simulations. Finally, the Smoldyn source code can be accessed through a C/C ++ language library interface. Availability and Implementation: Smoldyn software, documentation, code, and examples are at http://www.smoldyn.org . Contact: [email protected].

Abstract Summary: Smoldyn is a software package for stochastic modelling of spatial biochemical networks and intracellular systems. It was originally developed with an accurate off-lattice particle-based model at its core. This has recently been enhanced with the addition of a computationally efficient on-lattice model, which can be run stand-alone or coupled together for multiscale simulations using both models in regions where they are most required, increasing the applicability of Smoldyn to larger molecule numbers and spatial domains. Simulations can switch between models with only small additions to their configuration file, enabling users with existing Smoldyn configuration files to run the new on-lattice model with any reaction, species or surface descriptions they might already have. Availability and Implementation: Source code and binaries freely available for download at www.smoldyn.org, implemented in C/C++ and supported on Linux, Mac OSX and MS Windows. Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online and include additional details on model specification and modelling of surfaces, as well as the Smoldyn configuration file used to generate Figure 1.

Most cellular processes depend on intracellular locations and random collisions of individual protein molecules. To model these processes, we developed algorithms to simulate the diffusion, membrane interactions, and reactions of individual molecules, and implemented these in the Smoldyn program. Compared to the popular MCell and ChemCell simulators, we found that Smoldyn was in many cases more accurate, more computationally efficient, and easier to use. Using Smoldyn, we modeled pheromone response system signaling among yeast cells of opposite mating type. This model showed that secreted Bar1 protease might help a cell identify the fittest mating partner by sharpening the pheromone concentration gradient. This model involved about 200,000 protein molecules, about 7000 cubic microns of volume, and about 75 minutes of simulated time; it took about 10 hours to run. Over the next several years, as faster computers become available, Smoldyn will allow researchers to model and explore systems the size of entire bacterial and smaller eukaryotic cells.