SUMMARY: Low-affinity interactions among multivalent biomolecules may lead to the formation of molecular complexes that undergo phase transitions to become supply-limited large clusters. In stochastic simulations, such clusters display a wide range of sizes and compositions. We have developed a Python package, MolClustPy, which performs multiple stochastic simulation runs using NFsim (Network-Free stochastic simulator); MolClustPy characterizes and visualizes the distribution of cluster sizes, molecular composition, and bonds across molecular clusters. The statistical analysis offered by MolClustPy is readily applicable to other stochastic simulation software, such as SpringSaLaD and ReaDDy. AVAILABILITY AND IMPLEMENTATION: The software is implemented in Python. A detailed Jupyter notebook is provided to enable convenient running. Code, user guide, and examples are freely available at https://molclustpy.github.io/.

Cellular and intracellular processes are inherently complex due to the large number of components and interactions, which are often nonlinear and occur at different spatiotemporal scales. Because of this complexity, mathematical modeling is increasingly used to simulate such systems and perform experiments in silico, many orders of magnitude faster than real experiments and often at a higher spatiotemporal resolution. In this article, we will focus on the generic modeling process and illustrate it with an example model of membrane lipid turnover.

Motivation: Proteins, especially those involved in signaling pathways are composed of functional modules connected by linker domains with varying degrees of flexibility. To understand the structure-function relationships in these macromolecules, it is helpful to visualize the geometric arrangement of domains. Furthermore, accurate spatial representation of domain structure is necessary for coarse-grain models of the multi-molecular interactions that comprise signaling pathways. Results: We introduce a new tool, mol2sphere, that transforms the atomistic structure of a macromolecule into a series of linked spheres corresponding to domains. It does this with a k-means clustering algorithm. It may be used for visualization or for coarse grain modeling and simulation. Availability and implementation: PyMOL plugin, source, and documentation. https://nmrbox.org/registry/mol2sphere. SpringSaLaD executables and documentation: http://vcell.org/ssalad, SpringSaLaD v.2 source: https://github.com/jmasison/SpringSaLaD.

Kidney podocytes' function depends on fingerlike projections (foot processes) that interdigitate with those from neighboring cells to form the glomerular filtration barrier. The integrity of the barrier depends on spatial control of dynamics of actin cytoskeleton in the foot processes. We determined how imbalances in regulation of actin cytoskeletal dynamics could result in pathological morphology. We obtained 3-D electron microscopy images of podocytes and used quantitative features to build dynamical models to investigate how regulation of actin dynamics within foot processes controls local morphology. We find that imbalances in regulation of actin bundling lead to chaotic spatial patterns that could impair the foot process morphology. Simulation results are consistent with experimental observations for cytoskeletal reconfiguration through dysregulated RhoA or Rac1, and they predict compensatory mechanisms for biochemical stability. We conclude that podocyte morphology, optimized for filtration, is intrinsically fragile, whereby local transient biochemical imbalances may lead to permanent morphological changes associated with pathophysiology.

UNLABELLED: Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine. AVAILABILITY AND IMPLEMENTATION: Available as VCell (versions 6.0 and later) at the Virtual Cell web site (http://vcell.org/). The application installs and runs on all major platforms and does not require registration for use on the user's computer. Tutorials are available at the Virtual Cell website and Help is provided within the software. Source code is available at Sourceforge. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Pathway Commons is a resource permitting simultaneous queries of multiple pathway databases. However, there is no standard mechanism for using these data (stored in BioPAX format) to annotate and build quantitative mathematical models. Therefore, we developed a new module within the virtual cell modeling and simulation software. It provides pathway data retrieval and visualization and enables automatic creation of executable network models directly from qualitative connections between pathway nodes. AVAILABILITY AND IMPLEMENTATION: Available at Virtual Cell (http://vcell.org/). Application runs on all major platforms and does not require registration for use on the user’s computer. Tutorials and video are available at user guide page.

The Rho family of GTPases control actin organization during diverse cellular responses (migration, cytokinesis and endocytosis). Although the primary members of this family (RhoA, Rac and Cdc42) have different downstream effects on actin remodeling, the basic mechanism involves targeting to the plasma membrane and activation by GTP binding. Our hypothesis is that the details of GTPase cycling between membrane and cytosol are key to the differential upstream regulation of these biochemical switches. Accordingly, we developed a modeling framework to analyze experimental data for these systems. This analysis can reveal details of GDI-mediated cycling and help distinguish between GDI-dependent and -independent mechanisms, including vesicle trafficking and direct association-dissociation of GTPase with membrane molecules. Analysis of experimental data for Rac membrane cycling reveals that the lower apparent affinity of GDI for RacGTP compared to RacGDP can be fully explained by the faster dissociation of the latter from the membrane. Non-dimensional steady-state solutions for membrane fraction of GTPase are presented in multidimensional charts. This methodology is then used to analyze glucose stimulated Rac cycling in pancreatic β-cells. The charts are used to illustrate the effects of GEFs/GAPs and regulated affinities between GTPases and membrane and/or GDI on the amount of membrane bound GTPase. In a similar fashion, the charts can be used as a guide in assessing how targeted modifications may compensate for altered GTPase-GDI balance in disease scenarios.

MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/

Computer scientists, mathematicians, and physicists are collaborating with cell biologists to build a tool, "Virtual Cell", that can merge data of cellular biochemistry with knowledge of cell structure to help us understand how cells produce complex behaviors. A cell is a highly complex factory that organizes thousands of different molecules to produce a specialized function. Microscopists have catalogued a wealth of data about the detailed structures within different types of cells and biochemists have assembled details about the properties and reactions of many of a cell's individual molecular constituents. The experimental information gathered from the test tube and from the microscope is married within "Virtual Cell" to construct a computer model which can produce simulations of how cells carry out a given function (e.g. secretion of insulin, contraction of heart muscle). What is produced are digital movies showing how each molecular species moves and reacts inside the cell during the cell biological process. Perhaps the most exciting application of this tool is that it allows scientists to ask questions about what happens when one step in a complex chain of events misfires or how a drug that is known to affect just a single enzyme might change the way a cell carries oat a specific function. But in addition to its else as a research tool, "Virtual Cell" can be a valuable teaching aid and may serve as a focus for organizing the tremendous knowledge base that comprises modern cell biology and biochemistry.

Clinical ultrasound image quality typically suffers from reduced resolution due to the effects of limited effective aperture size (convolutional blurring), out-of-focus blurring and noise. Speckle noise in ultrasound images can be especially troublesome and significantly deteriorates image quality. This paper discusses the use of a novel approach developed for optical microscope images and applied to ultrasound. In earlier research, 3D microscope images were successfully deconvolved using an adaptive, least-mean-square solution to a statistical Wiener filter. The filter can be applied to two and three-dimensional images as a finite impulse response (FIR) filter solved adaptively using an inverse model of the point-spread-function (PSF). For ultrasound, the filter can be solved using the response to a phantom where the desired result is known a priori. The filter is solved adaptively to minimize the mean-square-error. The resulting filter can then be applied to any image acquired with the same transducer array and instrument parameters. The application of this type of filter to ultrasound is in the preliminary stages with some partial success obtained so far. Preliminary results are shown on phantom and clinical ultrasound data (courtesy of Diasonics, Inc.) processed by this method. The results are promising for improving resolution and minimizing noise.