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Bio-SPICE

Biological Simulation Program for Intra- and Inter-Cellular Evaluation

Bio-SPICE, an open source framework and software toolset for Systems Biology, is intended to assist biological researchers in the modeling and simulation of spatio-temporal processes in living cells. In addition, our goal is to develop and serve a user community committed to using, extending, and exploiting these tools to further our knowledge of biological processes.

In collaboration with other Bio-SPICE Community members, we will develop, license, distribute, and maintain a comprehensive software environment that integrates a suite of analytical, simulation, and visualization tools and services to aid biological researchers engaged in building computable descriptions of cellular functions. From disparate data analysis and information mining to experimental validation of computational models of cell systems, our environment will offer a comprehensive substrate for efficient research, collaboration, and publication.

Mission

Bio-SPICE is intended for modeling and simulation of spatio-temporal processes in living cells. The goals of Bio-SPICE are to support discovery through:
  1. Developing computational and mathematical models of bio-molecular systems in cells capturing the nature of gene-protein interactions
  2. Developing tools that can rapidly incorporate relevant experimental data and knowledge known in the literature to build models of pathways, networks, and spatial processes
  3. Developing simulation tools for the dynamic analysis of bio-molecular systems
  4. Creating an extensible framework for easy insertion of models and their refinement, as well as customization to specific mechanisms
In addition, our goal is to develop and serve a user community committed to using, extending, and exploiting these tools to further our knowledge of biological processes. In collaboration with other Bio-SPICE Community members, we will develop, license, distribute, and maintain a comprehensive software environment that integrates a suite of analytical, simulation, and visualization tools and services to aid biological researchers engaged in building computable descriptions of cellular functions. From disparate data analysis and information mining to experimental validation of computational models of cell systems, our environment will offer a comprehensive substrate for efficient research, collaboration, and publication.

Background

The biological sciences have experienced dramatic growth over the past two decades, culminating in the recent completion of a working draft of the human genome. This stands as a seminal scientific achievement and is indicative of the growing repertoire of available molecular, biochemical, and physiological approaches by which investigators can elucidate the complex inner-workings of living cells. Research hypotheses that were untestable only a short time-period ago are fast becoming amenable to the scientific method.

Originally a DARPA-sponsored program, the goal of Bio-SPICE was to apply expertise from the computational and information sciences in order to develop in silico simulations that model how various pathogens act to disrupt normal cellular processes. The program was transitioned to an Open Source project at the conclusion of its DARPA sponsorship in December 2006.  The project is now hosted by SourceForge.net.  Originally developed under the DARPA BioCOMP license, Bio-SPICE is now released under the open-source BSD license.  (See license below.)

Original key contributors to Bio-SPICE included: Virginia Polytechnic University, University of Pennsylvania, University of Texas, Harvard University, University of California, Los Angeles and Berkeley, Lawrence Berkeley National Laboratory, California Institute of Technology, New York University, Massachusetts Institute of Technology, and the Molecular Sciences Institute. The lead integrator for the project was SRI International.

Bio-SPICE Core Tools

The core of the Bio-SPICE application is called the Dashboard. The Bio-SPICE Dashboard has an Update Center and allows for different tool functionality to be downloaded, data to be specified, and both to be integrated into tool chains and work flows for modeling, analysis, and simulation. The Dashboard is a Java-based toolkit, but the integrated tools can be written in any language. The Dashboard core libraries provide support for accessing non-Java tools.

Follow these links to begin setting up your Bio-SPICE work environment:
If you are developing tools for integration into Bio-SPICE, please take a look at the Developer's Manual.

Update Center

The update center server, formerly hosted by SRI, has been discontinued.  Please contact individual tool owners for their respective tools.

Bio-SPICE Products

Most Bio-SPICE tools are written to be plugin modules to the Bio-SPICE Dashboard.  The following is a partial list of tools written for Bio-SPICE, with links to the authoring institution, where available.  Tools are available from a variety of locations.  In addition to the links below, some tools are available when using the Bio-SPICE Dashboard's Update Center wizard.  (See the user manual for more information.)

MODULE INSTITUTION
2DGrapher SRI
Add Column to Timeseries UTK
Biodata Viewer SRI
BioGrid UTK
BioMat Bridge SRI
BioNets UNC
BioPack VT
BioSens UCSB
BioSketchPad U Penn/BBN
BioSmokey UTK
BioSpreadsheet UTK
BioWarehouse SRI
BioWarehouse Query SRI
BioWarehouse2SBML Harvard
BioWave NYU
Cellx Indiana
Charon U Penn
Clone Updater TJU
CoBi CFDRC
Convert Data to Graph UTK
DBAgent SRI
ESS UTK
Fluxor Computational Analyzer Harvard
Fluxor Spreadsheet Harvard
FTF  
GCMConverter  
GCMMerger  
GeneCite WRAIR
GeneScreen UCLA
Geneways Columbia
Get Column from Timeseries UTK
Get Rows from Timeseries UTK
Graphviz LBL
Graph Viewer UTK
Homologue Finder LBL
Hybrid Automata Symbolic Reachability Tool Stanford
IcDNA UCLA
Jdesigner KGI
Jigcell VT
JournalMine ActionQuery Columbia
Karyote Cell Analyzer (KCA) Indiana
Karyote Genome Analyzer (KAGAN) Indiana
Merge Timeseries Columns UTK
Merge Timeseries Rows UTK
MetaCluster TJU
MetaTool KGI
MIAMESpice UCLA
Model Builder Analyzer  
Monod MSI
NCA Analyzer UCLA
NYUMAD NYU
NYUSIM NYU
Octave Bridge UTK
Oscill8 Tools  
PAINT TJU
PathwayBuilder LBL
PathwayScreen WRAIR
QIS QIS
PlotML Translator SRI
PtPlot SRI
Reactome  
Render Matlab Simulation LBL
Render SBML LBL
SAL SRI
Sample Timeseries UTK
SBML TO Graphviz Dot LBL
SBML to PathwayBuilder LBL
SBML to laid-out Graphviz DOT LBL
SBML:SCC to PathwayBuilder LBL
SBWMatlab KGI
SCCFinder LBL
Sensitivity Analyzer LBL
Simpathica NYU
SOS Tools Cal Tech
Symbolic Analysis Toolbox Stanford
Tab Dlimited Text Converter SRI
TableView SRI
Timeseries to Text UTK
TimeSeries To ZipFile Converter SRI
Vibrio U Penn



Example Bio-SPICE Projects

Experimental systems being studied using Bio-SPICE software include:

  • Virology
    • Lambda-phage
    • HIV - 1
    • Host-Pathogen Interactions
    • Lethal Shock: SEB Induced Apoptosis
  • Signal Transduction
    • Alliance for Cell Signaling
    • Quorum Sensing
  • Synthetic Circuits
    • Genetic Oscillator Design
    • Minimal Cell

Sporulation: Bacillus subtilis and Bacillus anthracis sporulation

Using comparative genomics software included as part of the Bio-SPICE software, researchers have characterized core elements and variable elements (environment specific) across more than 280 spore forming species; include Bacillus subtilis and Bacillus anthracis. This has provided a starting point for dynamic network analysis, which was performed to classify and analyze feedback loops, identify subnetworks (motifs) that could be potential control points, and analyze dynamic parameterized models. Analysis ranges from elementary modules to full system level, with robust identification of system level control points. Bio-SPICE software modules are used to identify relevant proteins and interactions and use literature searches to filter pathways of interest. Importantly, the computational model has been validated experimentally.

Bio-SPICE analysis of key switches in sporulation network
Overall sporulation network: B. subtilis - common across mutants
Source: Bio-SPICE projects at LBNL (PI: Adam Arkin)

Mtb Dormancy

Bio-SPICE has been used to generate an integrated model of metabolic and regulatory network for Myobacterium tuberculosis (Mtb). The stringent response is modeled by a stochastic hybrid system with integrated metabolic and regulatory pathways. The model was verified experimentally both in vivo and in vitro. Boundaries of the flux model kinetic parameters have been described. The Rel gene has been identified as a critical point, with the products regulated by this gene found to be diagnostic markers and therapeutic targets. The developed model has been expanded to simulate dormancy in other pathogens.

Source: Bio-SPICE projects at Univ. of Penn. (PI: Harvey Rubin)

Bacterial Metabolism

This project is developing tools for metabolic analysis and employing integrated Bio-SPICE to study how mutant organisms optimize their growth rate or minimize their metabolic adjustment relative to the wild-type organism. What are the gaps in our knowledge of the metabolism of this organism? And how can we re-optimize it for new (biotech) goals?

The modeling questions include: Given a genome how do we reconstruct metabolism? How do we generate optimal models? How do we compute the metabolic capabilities of an in-silico representation of an organism? How do we choose a metric for minimizing a mutant organism's metabolic adjustment?

How do we integrate predictions with experimental design and data?

Metabolic models from this pipeline can be used for any annotated microbial genome, e.g., pathogenic Escherichia, Pseudomonas, Yersinia, Bacillus and others of concern to the DoD. In addition, the models could be used to engineer the production and delivery of biomolecules.

Bio-SPICE Minimization of metabolic adjustment - MOMA
Source: Bio-SPICE projects at Harvard (PI: George Church)

HIV

In the fight against the HIV virus, the elucidation of retroviruses has been key in fighting the disease. The lack of current computational models also makes HIV a difficult and logical target for malicious engineering. Cell models have been developed to design therapeutic retroviruses and predict parameters critical to the design of these retroviruses. In HIV, the TAT gene has been heavily implicated in whether HIV is latent or active. A higher TAT level results in an increased level of mRNA for the viral genome. The influence of TAT is though to be even more relevant, with measurements obscured by noise.

Source: Bio-SPICE projects at LBNL (PI : Adam Arkin)

Source: Bio-SPICE projects at LBNL (PI : Adam Arkin)

Cell Cycle Models

A key Bio-SPICE discovery found that cell cycle control could be stopped after passing what was conventionally assumed to be the point of no return. A critical checkpoint was identified that allows cell cycle to be arrested and restarted through control of the cyclin pathway. The discovery was made in yeast cells, but a homologous pathway was identified in frog cells, cells which are more complex with a higher level of differentiation. The homologous pathways consisted of similar interactions, but utilized different genes and proteins. The computational model developed contained three modules: an oscillatory module, based on a delayed negative feedback loop; a switching module, based on cooperative removal; and a proteolysis module, based on irreversible activation. Cell cycle arrest was predicted and experimentally verified. Discoveries such as these open new vistas of research, including bacterial growth control, wound-healing, and nerve regeneration.

Bio-SPICE assisting rapid validation of CDK control across organisms
Cell cycle control: generic model of CDK control developed
Source: Bio-SPICE projects at VaTech (PI: John Tyson)

Planar Cell Polarity in Drosophila Wings Epithelium

This project is developing models and tools for predicting the spatial pattern on hair formation in drosophila wings and cell differentiation in xenopus. Novel stochastic hybrid system models (combining continuous and discrete dynamics) of protein concentrations in a network of cells are developed to characterize hair pattern formation and the dependence on cellular network connectivity and initial conditions. Models use robust parameter identification techniques, symbolic computation and reachability analysis, and ordinary and partial differential equations to represent the system. The role of delta-notch protein concentration dynamics in xenopus differentiation, and the role of Fz, Dsh, and Pk proteins in drosophila wing hair pattern formation are being studied.

Bio-SPICE Spatial Models of networks of cells
Source: Bio-SPICE projects at Stanford Univ. (PI: Claire Tomlin) and at UC Berkeley (PI: Shankar Sastry)

Neutrophil Chemotaxis

Neutrophil chemotaxis has been elucidated through three dimensional modeling and simulation, allowing reconstruction of the cell shape during location through measurements of polarity and chemical localization. Bio-SPICE offers the unique ability to perform three dimensional simulation of multi-physical systems with one, two, and three dimensional diffusion and changing boundary morphology. The simulation model takes into account both surface and volume measurements, using partial differential equations with stochastic events to model the spatial chemical activity.

Spatial modeling and simulation essential in many systems
Source: Bio-SPICE projects at LBNL (PI: Adam Arkin
)


In Memoriam


The Bio-SPICE project  is dedicated to the memory of our friend and colleague, Charles "John" Pedersen.  John enthusiastically ran the Bio-SPICE program at SRI during its five years as a DARPA project.  We miss you John! John Pedersen