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AFRL-IF-RS-TR-2006-
Final Technical Report
October 2006
SPATIAL MODELING TOOLS FOR CELL
BIOLOGY
CFD Research Corporation
Sponsored by Defense Advanced Research Projects Agency DARPA Order No. J058/U
APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED.
STINFO FINAL REPORT
The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the U.S. Government.
AIR FORCE RESEARCH LABORATORY
INFORMATION DIRECTORATE
ROME RESEARCH SITE
ROME, NEW YORK
NOTICE AND SIGNATURE PAGE
Using Government drawings, specifications, or other data included in this document for any purpose other than Government procurement does not in any way obligate the U.S. Government. The fact that the Government formulated or supplied the drawings, specifications, or other data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented invention that may relate to them.
This report was cleared for public release by the Air Force Research Laboratory Rome Research Site Public Affairs Office and is available to the general public, including foreign nationals. Copies may be obtained from the Defense Technical Information Center (DTIC) (http://www.dtic.mil).
AFRL-IF-RS-TR-2006-318 HAS BEEN REVIEWED AND IS APPROVED FOR
PUBLICATION IN ACCORDANCE WITH ASSIGNED DISTRIBUTION
STATEMENT.
FOR THE DIRECTOR:
/s/ /s/
CLARE THIEM JAMES A. COLLINS, Deputy Chief Work Unit Manager Advanced Computing Division Information Directorate
This report is published in the interest of scientific and technical information exchange, and its publication does not constitute the Government’s approval or disapproval of its ideas or findings.
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TABLE OF CONTENTS
Page No.
LIST OF FIGURES ....................................................................................................................... iii LIST OF TABLES........................................................................................................................ vii
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Figure 5.1: Computational results for a diffusion problem on planar square thin film............ 36 Figure 5.2: Membrane model for a thin spherical surface........................................................ 38 Figure 5.3A: Color contour map of specie A................................................................................ Figure 5.3B: Species A and B concentration distributions as a function of time. ...................... 39 Figure 5.4: Geometry of a cigar like bacterial membrane........................................................ 40 Figure 5.5: Transient distributions of species B concentration. ............................................... 40 Figure 5.6: Diagram of EGFR signal transduction pathway[Araujo 2005] ............................. 41 Figure 5.7: Time course for species PRLP A. Araujo et al.; B. Using J Designer; C. Using CoBi............................................................................................................. 42 Figure 5.8: Endocytic trafficking – multi cellular tissue model implemented in CoBi. .......... 43 Figure 5.9: Kinetic and rate equations for EGF signaling model............................................. 44 Figure 5.10: Microscopy image of the tissue, 3D unstructured mesh wit CoBi predicted ligand concentration in the intercellular space of the tissue. ................................. 44 Figure 5.11: Computational results for modeling drug delivery to 3D tissue structure with CoBi. .............................................................................................................. 45 Figure 5.12: Schematic of cellular calcium wave model ........................................................... 46 Figure 5.13: Cellular calcium wave simulation.......................................................................... 46 Figure 5.14: Tissue calcium wave model A. Schematic of pathway (Sneyd 1998) B. Model schematic (Sneyd 1998) and C. Calcium concentrations during CoBi simulation............................................................................................................... 47 Figure 5.15: Comparison of published and CoBi simulation results for calcium wave transmission between cells. A. Ca 2+^ concentrations (solid line) from Sneyd and B. CoBi............................................................................................................ 47 Figure 5.16: Actin filament network dynamics [Csikasz-Nagy 2005]....................................... 48 Figure 5.17: CoBi simulation results for yeast mutants showing F-actin concentrations .......... 48 Figure 5.18: Simple metabolic model response to a doubling of RMR from the rest state. ...... 50 Figure 5.19: Metabolic network diagram of the model (adapted from Salem et. al. 2002) ....... 51 Figure 5.20: Effect of GL, FA and LA uptake (bottom graph) to 30% ischemia ...................... 56 Figure 5.21: ATP and PC evolutions compared. Experimental data points are plotted with simulation curves for 30% and 50% ischemia....................................................... 57 Figure 5.22: Effects of 30% ischemia on AC production and redox state. ................................ 58 Figure 5.23: Effect of 50% ischemia on ATP hydrolysis rate, max ATP hydro. rate and available free energy (inset)................................................................................... 59 Figure 5.24: Effects of 40% hypoxia on the uptake of several metabolites and blood gasses... 60 Figure 5.25: 99.9% ischemia simulation. ................................................................................... 60 Figure 5.26: Snapshots of the cell model. (top left) The unstructured mesh grid used for simulations. (bottom left) oxygen distribution.(top and bottom right) carbon dioxide distributions. Capillary blood flow is shown circling both sides of the cell and entering from the bottom part of the figure. Species are transported in and out of the cell through the cell membrane and then into the mitochondria where energy is converted into ATP and CO 2. ...................................................... 61 Figure 5.27: A series-in-time showing the progression of metabolism in a cell. Top figures show CO2 concentration evolution throughout the cell for two different metabolic rates (MR = 9 and 90). The time stamps are indicated at the top of the figure. Bottom figures show the O2 distribution for the same simulation
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and time points. Notice the increased O2 consumption and CO2 generation when energy demands on the cell are increased 10-fold. ...................................... 62 Figure A.1: Bacillus Subtilis geometry and computational mesh during germination cell cycle division. Image and mesh examples obtained with CFD-Micromesh......... 71 Figure A.2: CFD-Micromesh generated geometrical image and computational mesh for and immune cell with a complex plasma membrane and an example organelle (nucleus)................................................................................................................. 72 Figure A.3: Conceptual model of ligand-receptor binding at the cell membrane. Schematics of chemosensing experiment, and biochemical intracellular IP3 signaling model, Narang 2001........................................................................................................... 73 Figure A.4: Transient response to an increase of uniform chemoattractant concentration for two values of chemoattractant stimuli.............................................................. 74 Figure A.5: Membrane phosphoinositides concentration π, as a function of cell response time. Top left initial conditions, bottom right final equilibrium state. ................. 74 Figure A.6: CFDRC simulation results for time-variation of various chemical species in the yeast cell cycle model ...................................................................................... 76 Figure A.7: Image of bacterial bell early in the sporulation event and an oscillatory behavior of Min proteins and formation FtsZ ring, [Lutkenhaus 1997, Erickson 2001]. ...................................................................................................... 77 Figure A.8: E-Coli cell geometry and mesh.............................................................................. 78 Figure A.9: Predicted concentration oscillation history of MinD Protein and cell deformation. ........................................................................................................... 78 Figure A.10: Schematic and a microcopy image illustration of life cycle and division of E. coli................................................................................................................. 79 Figure A.11: Schematic and a microcopy image illustration of life cycle and division of E. coli. Shown are equations A.7 thru A.10, Huang 2003.................................... 81 Figure A.12: 1D and 3D simulation results for the bacterial oscillations of Min proteins during bacterial division. ....................................................................................... 82 Figure A.13: Bacterial cell division simulation with the membrane model of the Bio-SPICE cell biology framework................................................................................................. 82 Figure A.14: Transient distributions of membrane receptors during bacterial cell division................................................................................................................... 82 Figure A.15: Schematic of signaling pathways for E Coli chemotaxis....................................... 83 Figure A.16: Ligand-binding, phosphorylation, and methylation reactions of the Tar–CheA– CheW complex, and detail of the phosphotransfer reactions, Spiro 1997............. 85 Figure A.17: Bacterial chemotaxis signal transduction pathway including diffusive transport. ................................................................................................................ 87 Figure A.18: Time history of the concentration of CheYp at three sites along the E Coli
body........................................................................................................................ 88 Figure A.19: Comparison between ODE solution and diffusion-reaction solution. ................... 88 Figure A.20: Distribution of CheYp within thee coli cytoplasm during the transduction
process.................................................................................................................... 89 Figure A.21: Computational model of Motile Bacterium with rotating flagella using full Navier Stokes equations micro-hydrodynamic model and an overset mesh. .................... 91
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LIST OF TABLES
- EXECUTIVE SUMMARY ACKNOWLEDGMENTS viii
- 1.0 INTRODUCTION
- 1.1 Background
- 1.2 Project Objectives
- 2.0 COMPUTATIONAL CELLULAR BIOLOGY
- 2.1 Relevant Introduction to Cellular Biology......................................................................
- 2.2 Biological and Computational Challenges in Cellular Biology......................................
- 2.3 Assessment of Cell Biology Spatial Modeling Methods
- 3.0 SPATIOTEMPORAL MODEL OF CELLULAR BIOLOGY.........................................
- 3.1 Computational Cell Biology
- 3.2 Basic Transport Equations
- 3.3 Membrane Model..........................................................................................................
- 3.3.1 Basic Equations and Assumptions
- 3.3.2 Numerical Integration Method..............................................................................
- 3.3.3 Model of Membrane Geometry
- 3.4 Biochemistry Model......................................................................................................
- 3.5 Boundary, Initial, and Volume Conditions
- 3.6 Material Properties........................................................................................................
- 4.0 DESCRIPTION OF COBI CODE FRAMEWORK
- 4.1 Overall Software Framework........................................................................................
- 4.1.1 Preprocessing, Geometry, Meshing, and Model Setup.........................................
- 4.1.2 CoBi Software Programming................................................................................
- 4.1.3 Visualization with VisIt Tools
- 4.2 CoBi Solver Structure
- 4.3 Automated Geometry and Meshing of Evolving Biological Cells
- 4.4 Preprocessing and Geometry/Mesh Generation............................................................
- 4.5 Post-processing
- 4.6 Example Problems Setup in JCoBi and Execution in CoBi
- 5.0 RESULTS OF CELL/TISSUE BIOLOGY SIMULATIONS WITH CoBi
- 5.1 Diffusion on a Planar Membrane..................................................................................
- 5.2 Diffusion on a Spherical Cell Membrane
- 5.3 Diffusion-Reaction of a Flat Membrane
- 5.4 Diffusion-Reaction on a Bacterial Membrane
- 5.5 EGFR Signal Transduction Pathway
- 5.6 Multicellular Perfused Tissue EGF Signaling
- 5.7 Cellular Calcium Oscillations
- 5.8 Tissue Calcium Waves..................................................................................................
- 5.9 Morphogenesis of Yeast Cell........................................................................................
- 5.10 Cellular Oxygen and Energy Metabolism.....................................................................
- 5.11 Perfusion of a Cell in an Organ and Cell Metabolism ii
- CONCLUSIONS AND RECOMMENDATIONS
- 6.1 Summary and Conclusions
- 6.2 Future Developments and Recommendations
- REFERENCES
- APPENDIX A............................................................................................................................... APPENDICIES
- APPENDIX B
- expressions............................................................................................................. Table 3.1: Examples of enzyme catalyzed kinetics reactions and reaction rate
- Table 4.1: Volume conditions
- Table 4.2: Boundary conditions
- Table 5.1: Metabolite species and their abbreviations
- Table 5.2: Summarized stoichiometric reactions
- Table 5.3: Comparison of experiment to simulation during 1-hour ischemia
- Table A.1: Cell cycle model
- Table A.2: Equation system used in the Meinhardt de Boer (2001) Model
- Table A.3: Equation system for the Howard Model
- Table A.4: SPO model reaction mechanism
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ACKNOWLEDGMENTS
This project would not be possible without the vision, and determination of Dr. Sri Kumar, DARPA/IPTO Project Manager of the Bio-Computation (BioCOMP) Program and the advocate of the Bio-SPICE simulation framework. We would like to express our sincere appreciation to Dr. Kumar for the inspiration, scientific guidance, and constructive feedback during the duration of this project. Mr. Clare Thiem of the Air Force Research Laboratory, AFRL/IFTC Rome NY, was the Technical Manager for this CFD Research Corporation (CFDRC) project providing both inspiration and motivation. Mr. Thiem’s thrust and belief in the potential power of advanced mathematical modeling and patience during model development are greatly appreciated. We are also thankful to Dr. Thomas Renz of AFRL/IFTC for stimulating discussions in using Computational Biology (CoBi) software for exploring nano Bio-Computing problems.
We would also like to express our appreciation to the PIs of the DARPA BAA BioCOMP teams for their interest and technical suggestions (often critical) at the PI Meetings. This project is attracting significant attention from several individuals at DoD medical research laboratories. We are grateful for that interest and hope that the models developed in this project will be a springboard for several future projects.
We are also grateful to several colleagues at CFDRC who helped during this project, in particular to Dr. Ashok Singhal, President of CFDRC, Dr. Maciej Pindera, and Dr. Hui Ding. We also extend our thanks to Dr. Mahesh Athavale, Dr. Yu Jiang, and Dr. Michal Furmanczyk, former Computational Medicine and Biology (CMB) division team members. This report was skillfully assembled, proofread, and enhanced by Ms Silvia Harvey of CFDRC.
Some of the illustrations presented in this report have been obtained from National Institute of Health (NIH) and National Library of Medicine (NLM) public domain web sites. Current electronic links for these pictures are provided in the figure caption. The authors are grateful to NIH and NLM for permission to reproduce some of the images in this report.
period CFDRC developed Co mputational Bi ology ( CoBi ,) software tools to simulate complex cell and organ biology problems. The code was written in modern C++ programming language using state of the art numerical techniques for solving large systems of partial differential equations (PDEs) and CFDRC multiphysics modeling expertise. A graphical user interface (GUI) for CoBi, JCoBi, was written in Java and interactive 3D graphics. CoBi has been designed to interact with the other Bio-SPICE software tools using Systems Biology Markup Language (SBML, http://sbml.org/index.psp ) as a standard interface.
This report describes the mathematical formulation of cellular biophysics and biochemistry models used in the code development, numerical methods for solving large-scale systems of PDE equations, and CoBi software structure. The code has been successfully applied to a number of cell biology problems. This report describes the models in detail and presents simulation results for several cell biology problems including: bacterial chemosensing and chemotaxis, bacterial sporulation, epidermal growth factor receptor (EGFR) signal transduction, cellular and tissue calcium oscillations, cellular and tissue oxygen and energy metabolism, morphogenesis of the yeast cell, and perfusion of a cell in an organ. The CoBi code has been developed, validated, and demonstrated on several cell biology and organ physiology problems. The code has been used as the starting point for several DARPA and DoD projects related to military medicine, DNA based bio-computing, personnel protection, and biodefense.
This project has established excellent foundations for future computational medicine and biology projects (CMB) at CFDRC for military and civilian medical applications. After two years of this project, CoBi has matured to the point that it can be used for solving problems not only in cell biology but also organ biology and whole body (organism) physiology. CFDRC has invested significant IR&D resources in CoBi code development, integration, and testing. The code is being used as a starting point for several projects with DoD focused on biotechnology, nanotechnology, and military medicine.
1.0 INTRODUCTION
1.1 Background
CFD Research Corporation (CFDRC) has been involved in the DARPA/IPTO BAA 01- BioCOMP Program between January 2002 and April 2006. The objective of the DARPA BioCOMP Program, conceptualized and organized by Dr. Sri Kumar of DARPA/IPTO was to develop a computational framework that enables the construction of sophisticated models of intracellular processes that can be used to predict and control the behavior of living cells. One of the program tracks was to develop Bio-SPICE modeling framework, which stands for the Simulation Program for Intra-Cellular Processes. DARPA has initiated the project to develop computational models and open source software for modeling cell biology. Among several projects, typically lead by US academic teams, CFDRC’s project task was first to formulate and explore a spatiotemporal (multidimensional) modeling approaches and then to implement and demonstrate it on cell and organ biology problems.
The project described in this report was conducted as a direct contract funded by DARPA and coordinated and technically directed by the Air Force Research Laboratory (AFRL/IFTC Rome NY). Mr. Clare Thiem of AFRL/IFTC was the DoD Technical Project Manager for the CFDRC BioCOMP Program. This project was conducted during June 2004 to May 2006. The overall objective of the CFDRC BioCOMP Program was to develop a self-contained software framework solving partial differential equations governing flow, diffusion, and biochemistry applicable for spatiotemporal modeling of cell and organ biology problems. The specific objective of this project was to develop Co mputational Bi ology ( CoBi ) software tools and a graphical user interface (GUI) using state-of the art numerical techniques and novel programming tools (C++ and Java) to simulate complex cell and organ biology problems. CoBi has been designed to interact with the other Bio-SPICE software tools using Systems Biology Markup Language (SBML, http://sbml.org/index.psp ) as a standard interface. The prototype CoBi software developed in this project was delivered in source to DARPA (SRI Bio-SPICE site) in February 2005 and the final code along with the code documentation has been delivered to AFRL/IFTC at the end of the project.
1.2 Project Objectives
The overall objective of the CFDRC BioCOMP Program was to develop a software toolkit and modeling framework for multidimensional, spatiotemporal comprehensive modeling of cell biology.
The objective of the present follow up project was to develop a self-contained software framework and computational cell and organ biology for applications in military medicine, bio- computation, biotechnology, and in civilian medical and biological sciences. The experience gained in CFDRC’s earlier involvement in the BioCOMP Program, described in Appendix A, in modeling 3D cell biology problems and the established biomembrane model was a starting point for this project. CFDRC proposed to adapt the existing 3D model of cell biomembrane for complete simulation of cell biology including intracellular, extracellular and membrane bound
2.0 COMPUTATIONAL CELLULAR BIOLOGY
2.1 Relevant Introduction to Cellular Biology
Living systems, from simple organisms to the human body, contain organs. Organs are composed of tissues, tissues consist of cells, and cells are formed of organelles, and molecules. Figure 2.1 – the biological cell is the basic unit of all living organisms (for detailed discussions of cell biology see e.g. Lodish 2000). There are more than 300 trillion cells in the human body, and every second of every day, more than 10 million die and are replaced. The unity of an organism is coordinated by many levels of material, chemical, and electric compunctions. Specific organ tissues and individual cells are surrounded by molecular membranes produced by cells. The biological universe consists of two types of cells, shown schematically in Figure 2. prokaryotic cells , and eukaryotic cells.
Prokaryotes , such Escherichia Coli bacteria, are single cell organisms, which have simple internal organization lack a nucleus, and the genetic material (DNA) is directly suspended inside the cell cytoplasm. Eukaryotes , comprising plant and animal kingdoms, have complex internal structure with various organelles and membrane limited nucleus that is kept separate from the cytoplasm by a double membrane structure. The cytoplasm contains the rest of the organelles and the area of the cytoplasm outside of the individual organelles is called the cytosol. Each organelle, surrounded by a membrane, contains a collection of specific enzymes and plays a unique role in the growth and metabolism of the cell. The most important organelles are: nucleus – containing DNA and other replication molecules, mitochondria – in which most of he cell energy metabolism takes place, endoplasmic reticula – build of complex membranes within which glycoproteins and lipids are synthesized, Golgi vesicles – which direct membrane constituents to appropriate places in cells, peroxisomes – in which fatty acids and aminoacids are degraded, and lysosomes – which also degrade internal and foreign molecules.
Figure 2.1: Examples of mammalian (eukaryotic) and bacterial (prokaryotic) cells (with NIH permission), http://www.ncbi.nlm.nih.gov/About/primer/genetics_cell.html.
Biological cells are surrounded by a membrane, phospholipid bilayer, designed to keep the cell interior together and protect the cell from the surrounding environment. The biochemical processes of cellular life takes place in the cytosol and cells communicate with the surrounding
and neighbor cells via reactions on the membrane and via the trans- membrane molecule exchange. The cytosol is the largest structure in the cell. It composes 54% of the cells total volume. The cytosol contains thousands of enzymes that are responsible for the catalyzation of glycolysis and gluconeogenesis and for the biosynthesis of sugars, fatty acids, and amino acids. The cytosol takes molecules and breaks them down, so that the individual organelles can use them.
The cytosol contains a skeletal structure, called the cytoskeleton. Cytoskeleton gives the cell its shape and allows it to organize many of the chemical reactions that occur in the cytoplasm. Additionally, the cytoskeleton can aid in the movement (motility) of the cell. It consists of protein filaments: actin filaments and the microtubules. The actin is responsible for contraction (like in muscles) and the microtubules are for structural strength of the cell. Cytoskeletal fibers also control movement of structures within the cell.
To survive cells have to carry out numerous functions ranging from replication and energy conversion to molecule transport and the various complicated cascades of biochemical reactions, signaling , used in cellular communication. Some of the most important functions are: Molecular transport, DNA replication, Reproduction, Protein synthesis, and Metabolism and Signaling.
One of the primary goals of all living organisms is to survive and reproduce – typically by cell division. Cell division occurs rapidly in living organisms. For example, in an adult human, millions of cells divide each second to maintain homeostasis (the proper balance in cells). Cells can reproduce in two ways, mitosis, and meiosis. In mitosis, the resulting daughter cell is an identical clone of the original cell. Mitosis is mostly used by somatic cells (cells of the body). Meiosis, however, is a form of sexual reproduction and only occurs in reproductive cells.
Mitotic cell division is an ordered set of events, cell cycle , culminating in cell growth and division into two daughter cells. It begins with interphase , when the cell replicates all of its genomic and cytoplasmic material and prepares for division. After preparation is complete, the cell enters the 4-phased mitosis. In mitosis, the cell sequentially goes through prophase , metaphase , anaphase , and telophase. Immediately after the completion of telophase, cytokenesis is initiated to end the cell division by literally separating the cell in two. Figure 2.2(a) schematically illustrates the cell division and cell cycle processes.
The cell cycle stages, Fig 2.2(b), are G1-S-G2-M. The G1 stage stands for "GAP 1". The S stage stands for "Synthesis". This is the stage in which DNA replication occurs. The G2 stage stands for "GAP 2". The M stage stands for "mitosis", and is when nuclear (chromosomes separate) and cytoplasmic (cytokinesis) divisions occur.
Figure 2.2(a) (^) Figure 2.2(b) Figure 2.2: Schematics of events during cell division and cell cycle (with NIH permission, http://www.ncbi.nlm.nih.gov/About/primer/genetics_cell.html.
proteins, ion channels, or cytoskeletal proteins). Signaling molecules, also called ligands (protein, small peptide, amino acid, nucleotide, steroid, hormone) are secreted from one cell in response to a specific stimulus travel to a target cell, where they bind to specific receptors on their membrane and elicit an intracellular response.
Within the cell the main method of signal transduction occurs through structural changes of pathway components. A given protein will affect the conformation of one or several other proteins, activating or inhibiting those proteins and thus propagating the signal down the pathway. The trigger for signal propagation often occurs with the binding of the signaling molecule to the receptor, which causes a conformational change in the receptor. Subsequently, cytosolic regions of the receptor are activated, making them active targets for intracellular and membrane associated proteins. The biochemical signal transduction in the cell is a complex and combinatorial process involving switches, integrating centers, feedback loops and crosstalk between pathways. Figure 2.4 presents example schematics of cell signaling pathways analyzed at CFDRC for modeling cellular neurotoxicity problems. Obtaining a detailed understanding of the dynamics of a biochemical reaction is a formidable challenge.
Figure 2.4: Schematic of organophosphate neuron toxicity cellular signaling pathways, from CDFRC dose-dependent, analysis of signaling and activation, neurotransmitter release, and metabolic response in neurotypical PC12 cells, Jenkins J. and Hood J. 2006.
2.2 Biological and Computational Challenges in Cellular Biology
Rapid progress in experimental biology and medicine, particularly at molecular and cellular levels, is generating an enormous amount of “bio-informatics” data. The raw data in digital or visual imaging form is analyzed and interpreted by highly skilled experts who formulate “models” of cellular events. These “models” are typically expressed in a long narrative form and described in biomedical journals. In molecular and cellular biology compact elegant mathematics based models are rare.
In the last few years it has been recognized that further understanding of biology will be possible only with a more comprehensive multidisciplinary approach involving: mathematics, informatics, computational biophysics, in addition to more traditional disciplines such as biochemistry, biology, physiology, and pathology, etc. Joel E. Cohen of the Rockefeller & Columbia Universities elegantly projected progress in biology as:
“ Mathematics Is Biology’s Next Microscope, Only Better; Biology Is Mathematics’ Next Physics, Only Better”
A Computational and bioinformatics approach to biology and medicine has enormous potential that has not yet been completely revealed. In the last two decades, computational technology (hardware and software) has been revolutionized and classical engineering disciplines such as fluid dynamics or structures mechanics evolved into Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) based Computational Structures Dynamics (CSD). New industries have emerged and CFDRC is one of the best examples of US based commercial ingenuity and business successes. It is believed that similar scientific and business revolutions are approaching in Computational Medicine and Biology (CMB). In 2002 CFDRC formed a new CMB team determined to spearhead that revolution.
Scientific potentials and military relevance of computational biology and bioinformatics have also attracted the interest of DARPA. The DARPA IPTO ambitious and visionary BioCOMP Program to develop an open source computational framework and modeling tools for cell biology started in 2001. This framework is called Bio-SPICE which stands for the Simulation Program for Intra-Cellular Processes. The BioCOMP Program involves experts from a broad range of disciplines, including biology, biochemistry, biophysics, computer science, mathematics, and bioengineering. Several teams were tasked to develop novel modeling methods and simulation software for broad but complementary aspects of cell biology such as: metabolic pathways, cellular signaling, cell cycle and division, chemosensing, chemotaxis, cellular colonies, and microorganisms. New standards of model description languages (System Biology Modeling Language, SBML) were being pursued to enable model exchange, reuse and archiving.
In the current BioCOMP Program almost all Bio-SPICE development activities concentrate on the development of biochemistry (cell cycle, signaling, metabolism, genomics, etc) or bioinformatics (e.g. microarray data processing) use Ordinary Differential Equations ( ODEs ) and as a consequence describe cells as a “well stirred reactor”. Spatial multidimensional cell modeling using Partial Differential Equations ( PDEs ) and accounting for cell morphology are
