Ion I. Moraru

Cell Shape and Negative Links in Regulatory Motifs Together Control Spatial Information Flow in Signaling Networks

The role of cell size and shape in controlling local intracellular signaling reactions, and how this spatial information originates and is propagated, is not well understood. We have used partial differential equations to model the flow of spatial information from the beta-adrenergic receptor to MAPK1,2 through the cAMP/PKA/B-Raf/MAPK1,2 network in neurons using real geometries. The numerical simulations indicated that cell shape controls the dynamics of local biochemical activity of signal-modulated negative regulators, such as phosphodiesterases and protein phosphatases within regulatory loops to determine the size of microdomains of activated signaling components. The model prediction that negative regulators control the flow of spatial information to downstream components was verified experimentally in rat hippocampal slices. These results suggest a mechanism by which cellular geometry, the presence of regulatory loops with negative regulators, and key reaction rates all together control spatial information transfer and microdomain characteristics within cells.

Topology of the Mitochondrial Inner Membrane: Dynamics and Bioenergetic Implications

Electron tomography indicates that the mitochondrial inner membrane is not normally comprised of baffle‐like folds as depicted in textbooks. In actuality, this membrane is pleomorphic, with narrow tubular regions connecting the internal compartments (cristae) to each other and to the membrane periphery. The membrane topologies observed in condensed (matrix contracted) and orthodox (matrix expanded) mitochondria cannot be interconverted by passive folding and unfolding. Instead, transitions between these morphological states likely involve membrane fusion and fission. Formation of tubular junctions in the inner membrane appears to be energetically favored, because they form spontaneously in yeast mitochondria following large‐amplitude swelling and recontraction. However, aberrant, unattached, vesicular cristae are also observed in these mitochondria, suggesting that formation of cristae junctions depends on factors (such as the distribution of key proteins and/or lipids) that are disrupted during extreme swelling. Computer modeling studies using the “Virtual Cell” program suggest that the shape of the inner membrane can influence mitochondrial function. Simulations indicate that narrow cristae junctions restrict diffusion between intracristal and external compartments, causing depletion of ADP and decreased ATP output inside the cristae.

Gene expression on acute myocardial stress. Induction by hypoxia, ischemia, reperfusion, hyperthermia and oxidative stress

It is apparent from the above discussion that acute stress, such as ischemia and reperfusion, hypoxia and reoxygenation, hyperthermia and oxidative stress, can rapidly potentiate the induction of genes for certain members of the HSP families and for antioxidants/antioxidant enzymes. Whether the stress response and induction of these genes have a direct role in myocardial protection is not known, but the induction of the expression of these genes are mostly associated with the preservation of myocardial cells from subsequent injury resulting from ischemia, hypoxia and reperfusion. The ubiquitous presence of some of these stress genes, such as for HSP 70 and catalase, in normal unstressed myocardium further suggests a role of these genes in many basic and essential biochemical and metabolic pathways. It is reasonable to speculate that the cells respond to the stress as a consequence of perturbations of one or more of the metabolic pathways by stimulating the induction of the stress genes of that particular pathway in which they participate. Thus, these genes are likely to be involved both in the protection and recovery/repair mechanisms. The precise mechanism by which myocardial cell recognizes and responds to a particular stress agent such as ischemia, hypoxia, hyperthermia or oxidative stress is not clear. While it is tempting to speculate that a generalized mechanism exists, applying to all different modes of stress response and gene induction, whether these agents induce the response via independent pathways or converge within a single point is entirely unclear. However, from the striking resemblance between the pattern of gene expression, especially with regard to HSP and antioxidant genes, it is reasonable to hypothesize the existence of a common and essential pathway of molecular signaling that leads to the expression of these stress genes (Fig. 2). The identification and characterization of the transcription factors that regulate the expression of the genes induced by these forms of stress should greatly facilitate our future understanding of the mechanism of stress response.

Virtual cell modelling and simulation software environment

The Virtual Cell (VCell; http://vcell.org/) is a problem solving environment, built on a central database, for analysis, modelling and simulation of cell biological processes. VCell integrates a growing range of molecular mechanisms, including reaction kinetics, diffusion, flow, membrane transport, lateral membrane diffusion and electrophysiology, and can associate these with geometries derived from experimental microscope images. It has been developed and deployed as a web-based, distributed, client-server system, with more than a thousand world-wide users. VCell provides a separation of layers (core technologies and abstractions) representing biological models, physical mechanisms, geometry, mathematical models and numerical methods. This separation clarifies the impact of modelling decisions, assumptions and approximations. The result is a physically consistent, mathematically rigorous, spatial modelling and simulation framework. Users create biological models and VCell will automatically (i) generate the appropriate mathematical encoding for running a simulation and (ii) generate and compile the appropriate computer code. Both deterministic and stochastic algorithms are supported for describing and running non-spatial simulations; a full partial differential equation solver using the finite volume numerical algorithm is available for reaction-diffusion-advection simulations in complex cell geometries including 3D geometries derived from microscope images. Using the VCell database, models and model components can be reused and updated, as well as privately shared among collaborating groups, or published. Exchange of models with other tools is possible via import/export of SBML, CellML and MatLab formats. Furthermore, curation of models is facilitated by external database binding mechanisms for unique identification of components and by standardised annotations compliant with the MIRIAM standard. VCell is now open source, with its native model encoding language (VCML) being a public specification, which stands as the basis for a new generation of more customised, experiment-centric modelling tools using a new plug-in based platform.

Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language

BackgroundThe increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools.ResultsIn this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions.ConclusionsWith SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined.

Minimum Information About a Simulation Experiment (MIASE).

Reproducibility of experiments is a basic requirement for science. Minimum Information (MI) guidelines have proved a helpful means of enabling reuse of existing work in modern biology. The Minimum Information Required in the Annotation of Models (MIRIAM) guidelines promote the exchange and reuse of biochemical computational models. However, information about a model alone is not sufficient to enable its efficient reuse in a computational setting. Advanced numerical algorithms and complex modeling workflows used in modern computational biology make reproduction of simulations difficult. It is therefore essential to define the core information necessary to perform simulations of those models. The Minimum Information About a Simulation Experiment (MIASE, Glossary in Box 1) describes the minimal set of information that must be provided to make the description of a simulation experiment available to others. It includes the list of models to use and their modifications, all the simulation procedures to apply and in which order, the processing of the raw numerical results, and the description of the final output. MIASE allows for the reproduction of any simulation experiment. The provision of this information, along with a set of required models, guarantees that the simulation experiment represents the intention of the original authors. Following MIASE guidelines will thus improve the quality of scientific reporting, and will also allow collaborative, more distributed efforts in computational modeling and simulation of biological processes.

Phospholipase D signaling in ischemic heart

Phospholipase D (PLD) activity was found to be present in the membrane fraction of rat myocardial cells by in vitro assays (36.7 +/- 4.1 nmol/mg protein per h against 1-palmitoyl-2-arachidonoyl- phosphatidylcholine) and demonstrated in intact cells by the specific transphosphatidylation reaction (in the presence of 0.02% ethanol) quantitated using n-[1-14C]butanol (201.16 +/- 7.1 pmol/min per g dry weight in the whole heart). Both methods showed a significant increase in PLD activity (by 62 and 44%, respectively) in hearts subjected to reversible (30 min) global normothermic ischemia followed by reperfusion (30 min). In hearts prelabeled with [1-14C]arachidonic acid, ischemia/reperfusion induced a significant increase in the amount of radiolabel incorporated into phosphatidic acid (PtdOH) (by 49.6%) and diacylglycerol (DG) (by 259%). DG kinase inhibition by 100 microM dioctanoylethylene glycol did not affect the ischemia/reperfusion DG and PtdOH levels while PtdOH phosphohydrolase inhibition with 40 microM propranolol produced a further increase in PtdOH (to 2.36-fold the baseline level) and a reduction in DG (to only 145% over the baseline levels). Put together, all these results suggest an activation of PLD during myocardial ischemia/reperfusion generating intracellular PtdOH, part of which is converted by PtdOH phosphohydrolase to DG. We further investigated the possible pathophysiological significance of the observed PLD activation. Stimulation of PLD with sodium oleate (20 microM) induced a significant improvement of functional recovery of ischemic hearts during reperfusion (as monitored by coronary flow and left intraventricular pressure measurements) and an attenuation of cellular injury as expressed by lactate dehydrogenase and creatine kinase release in the coronary effluent during reperfusion. These results suggest a PLD-mediated signaling in the ischemic heart which may benefit functional recovery during reperfusion.

Spatial modeling of cell signaling networks.

The shape of a cell, the sizes of subcellular compartments, and the spatial distribution of molecules within the cytoplasm can all control how molecules interact to produce a cellular behavior. This chapter describes how these spatial features can be included in mechanistic mathematical models of cell signaling. The Virtual Cell computational modeling and simulation software is used to illustrate the considerations required to build a spatial model. An explanation of how to appropriately choose between physical formulations that implicitly or explicitly account for cell geometry and between deterministic versus stochastic formulations for molecular dynamics is provided, along with a discussion of their respective strengths and weaknesses. As a first step toward constructing a spatial model, the geometry needs to be specified and associated with the molecules, reactions, and membrane flux processes of the network. Initial conditions, diffusion coefficients, velocities, and boundary conditions complete the specifications required to define the mathematics of the model. The numerical methods used to solve reaction-diffusion problems both deterministically and stochastically are then described and some guidance is provided in how to set up and run simulations. A study of cAMP signaling in neurons ends the chapter, providing an example of the insights that can be gained in interpreting experimental results through the application of spatial modeling.

Integrating BioPAX pathway knowledge with SBML models

Online databases store thousands of molecular interactions and pathways, and numerous modelling software tools provide users with an interface to create and simulate mathematical models of such interactions. However, the two most widespread used standards for storing pathway data (biological pathway exchange; BioPAX) and for exchanging mathematical models of pathways (systems biology markup language; SBML) are structurally and semantically different. Conversion between formats (making data present in one format available in another format) based on simple one-to-one mappings may lead to loss or distortion of data, is difficult to automate, and often impractical and/or erroneous. This seriously limits the integration of knowledge data and models. In this paper we introduce an approach for such integration based on a bridging format that we named systems biology pathway exchange (SBPAX) alluding to SBML and BioPAX. It facilitates conversion between data in different formats by a combination of one-to-one mappings to and from SBPAX and operations within the SBPAX data. The concept of SBPAX is to provide a flexible description expanding around essential pathway data - basically the common subset of all formats describing processes, the substances participating in these processes and their locations. SBPAX can act as a repository for molecular interaction data from a variety of sources in different formats, and the information about their relative relationships, thus providing a platform for converting between formats and documenting assumptions used during conversion, gluing (identifying related elements across different formats) and merging (creating a coherent set of data from multiple sources) data.

Kinetic Modeling Using BioPAX Ontology

Thousands of biochemical interactions are available for download from curated databases such as Reactome, Pathway Interaction Database and other sources in the Biological Pathways Exchange (BioPAX) format. However, the BioPAX ontology does not encode the necessary information for kinetic modeling and simulation. The current standard for kinetic modeling is the System Biology Markup Language (SBML), but only a small number of models are available in SBML format in public repositories. Additionally, reusing and merging SBML models presents a significant challenge, because often each element has a value only in the context of the given model, and information encoding biological meaning is absent. We describe a software system that enables a variety of operations facilitating the use of BioPAX data to create kinetic models that can be visualized, edited, and simulated using the Virtual Cell (VCell), including improved conversion to SBML (for use with other simulation tools that support this format).