Date post: | 27-Dec-2015 |
Category: |
Documents |
Upload: | morgan-francis |
View: | 215 times |
Download: | 1 times |
Systems Biology•Computational Systems Biology Group (Peter Spirtes) in Pittsburgh, Pennsylvania •Biochemical Networks Modeling Group (Pedro Mendes) at the Virginia Bioinformatics Institute Computational Systems Biology Group (Reinhard Laubenbacher) at the Virginia Bioinformatics Institute •Evolution of Molecular Networks group (Andreas Wagner) at the University of New Mexico •Systems biology group (Trey Idekeker) at the Whitehead Institute for Biomedical Research, Cambridge (USA) •Computational Cell Biology (Dennis Bray) at the University of Cambridge (UK) STRC Biocomputation Group (Hamid Bolouri) at the University of Hertfordshire •Computational Molecular Biology (Ron Shamir) at the University of Tel Aviv •Complex Systems Division (Carsten Peterson) at the University of Lund •Design Principles of Protein Networks (Uri Alon) at the Weizmann Institute •Design Principles of Protein Networks (Naama Barkai) at the Weizmann Institute •Probabilistic Graphical Models (Daphne Koller) at the University of Stanford •Molecular Biology and Probabilistic Models (Nir Friedman) at the Hewbrew University of Jerusalem •Systems Optimization Group (Eckart Zitzler) at the ETH Zürich •Protein Interaction Group (Benno Schwikowski) at the Systems Biology Institute, Seattle •Systems Biology Center at TU Delft •Integrative Systems Biology at TU Denmark •U Ghent •Institute for Advanced Study, Center for Systems Biology •Ron Weiss group, Princeton University •BII Systems Biology Group (Singapore) •UC San Francisco BioSystems Group •Kitano Systems Biology Group •Davidson Lab at Caltech •Bioinformatics & Systems Biology Group at the Burnham Institute (La Jolla) •Virtual Cell Project, U Connecticut •UC Santa Barbara IGERT Program on Systems Biology •UC San Diego Bioinformatics & Systems Biology Groups •UC San Diego Systems Biodynamics Group •Integrated Systems Biology Group at Rensselaer Polytechnic Institute
Groups World-Wide
Systems Biology
•BioSPI Project at Weizmann •BioSPICE •BioMaps Institute at Rutgers: •Institute for Systems Biology, Seattle •Bauer Center for Genomics Research (CGR) at Harvard University •Systems Biology Department at Harvard Medical School •Computational and Systems Biology Initiative at MIT •Bio-X at Stanford University •Center for Studies in Physics and Biology at The Rockefeller University •GENSCEND Initiative of the Wellcome Trust •"Genomes to Life program" (a funding initiative of the DOE) •"Cell Systems Initiative" (an initiative of the University of Washington) •"Systems of Life - System Biology" (a funding initiative of the German Ministry of Education and Research, BMBF) •SFB 618 (funded by the German Research Council DFG) •STAGSIM - Systems Biology (An Expression of Interest (EoI) submitted to the EU Framework Program VI) •Systems Biology in Sweden •Institute for Computational Biomedicine at the Weill Medical College of Cornell University. •Pathways/Systems Biology Working Group at I3C.
Institutes and Larger Initiatives
Though coined 40 years ago,1 a lot of people still ask, "What's that?" when the term systems biology comes up. "It is used in so many different contexts, nobody is really clear what you mean by it," says John Yates III, a professor at the Scripps Research Institute in La Jolla, Calif. He's not the only one stumped by the term's meaning. David Placek, president of Sausalito, Calif.-based Lexicon Branding, a company that cooks up names for pharmaceutical products such as Velcade and Meridia, says he's not so hot on the moniker. "Systems biology is just so general that it could apply to many things. When you're naming a category, the underlying principle is that if you make a statement like, 'I'm doing systems biology,' do people know what you're talking about?'“……
Systems Biology Has its Backers and Attackers
Revolution or buzzword du jour, pundits ponder a pervasive term | By Mignon Fogarty
Volume 17 | Issue 19 | 27 Oct. 6, 2003, The Scientist
Understanding the principles of how physiological/phenotypic characteristics emerge from the properties of the components.
Predicting how these characteristics will change in response to alterations in the environment or system components.
What is Systems Biology?
Successful Models
Barbara Bakker, Westerhoff and Cornish-Bowden
Trypanosoma Brucei
Bas Teusink
Yeast Glycolysis
Frances Brightman et al
EGF Signaling Pathway
Red Blood Cell
Mulquiney, Joshi, Heinrich, …
Poolman and Fell
Calvin Cycle Yeast Cell Cycle
John Tyson et al
Chemotaxis, ecoli
Many Contributors
Level of Complexity
Molecule# Molecules per cell # of Types
Protein 2,360,000 1000-2000RNA 270,000 5Small Molecules millions 500Ions millions 20-30
http://biosci191.bsd.uchicago.edu/L02/ecoli.htmhttp://opbs.okstate.edu/5753/Composition%20table.html
E. coli composition
Man-made Complex Devices
• The Intel Itanium 2• 410 million transistors• Number of gates > 100 Million
Man-made Complex Devices
• The Intel Itanium 2• 410 million transistors• Number of gates > 100 Million
By 2007 both Intel and AMD are predicting dies with 1 billion transistors
Man-made Complex Devices
• The Intel Itanium 2• 410 million transistors• Number of gates > 100 Million
By 2007 both Intel and AMD are predicting dies with 1 billion transistors
Many of the new graphics chips have over 60 million transistors
AMD are working towards 45-nanometer transistors by 2007. The sizes of proteins vary from 2nm to 20 nm.
Man-made Complex Devices
Probably by 2010, man-madedevices will have comparable complexity to bacterial cells if not greater.
Cellular Models
Building computational models of cells seems more and more like a viable project.
Such a project would bring a much clearer understanding of how cellular systems are controlled and ultimately it should bring unprecedented predictive power.
Are Biologists Ready?
Xo and X1 fixed,
all reactions reversible, assume stable steady state.
Xo S1 S2 X1S3 S4 S5 S6v
Are Biologists Ready?
What happens to the steady state?
Xo S1 S2 X1S3 S4 S5 S6v
Xo and X1 fixed,
all reactions reversible, assume stable steady state.
50 %
Are Biologists Ready?
Xo S1 S2 X1S3 S4 S5 S6
Students reply:
1. Nothing happens.
2. Nothing happens unless it is the rate-limiting step.
3. The rate v goes down, but that’s all.
4. S3 goes up.
5. S4 goes down.
6. Species downstream of v go up.
7. Steady State flow changes but species levels don’t.
8. Xo and X1 change
v
50 %
Are Biologists Ready?
Xo S1 S2 X1S3 S4 S5 S6
If we can’t understand this system how can we hope to understand:v
50 %
Functional Motif Identification
http://bms-mudshark.brookes.ac.uk/frances/fabweb5.htm
29 species
Computer simulation of EGF signal transduction PC12 cells.
Frances Brightman, Simon Thomas and David Fell
Functional Motif Identification
Computer simulation of EGF signal transduction PC12 cells.
Frances Brightman, Simon Thomas and David Fell
http://bms-mudshark.brookes.ac.uk/frances/fabweb5.htm
Functional Motif Identification
Pow
er A
mpl
ifier
Pre
-Am
plifi
er
Am
plifi
erFeedback
Fee
dbac
k
Filt
er
Carrier Filter
Audio FilterRectifier
Amplifier
Demodulator
How Intel Engineers Cope
Complex man-made devices are modeled and designed on multiple levels, each level may use different modelingtechniques:
Transistor Characteristics
Basic Logic Gates
Small Gate Modules
Hierarchy of functional modules
Top Level Module
How Intel Engineers Cope
Complex man-made devices are modeled and designed on multiple levels, each level may use different modelingtechniques:
Transistor Characteristics
Basic Logic Gates
Small Gate Modules
Hierarchy of functional modules
Top Level Module
Fundamental Protein Chemistry
Basic Enzyme Rate Characteristics
Small Enzyme Motifs
Hierarchy of functional modules
Top Level Module
Functional Motif Identification
Negative Feedback in the MAPK Pathway
yi
yo
A k
At high amplifier gain (A k > 1):
Functional Motif Identification
Negative Feedback in the MAPK Pathway
At high amplifier gain (A k > 1):
Linearization of the amplifier response.
Without Feedback With Feedback
Software
Tools and Resources:
Software Infrastructure
Interchange Formats
Analysis Algorithms
Model Editors
Visualization
Model Databases
Theoretical Foundation
Databases for Systems Biology
The oldest known metabolic pathway is Yeast Glycolysis
http://www.utc.edu/Faculty/Becky-Bell/210-outline05.html
http://www.utoronto.ca/greenblattlab/yeast.htm
Databases for Systems Biology
Hexokinase 2.7.1.1
Glucose + ATP = G6P + ADP
Km None available
Specific Activity: 512 M/min/mg
Databases for Systems Biology
Phosphofructokinase 2.7.1.11
ATP + F6P = ADP + FBP
Km None available
Specific Activity: 180 M/min/mg148 M/min/mg114 M/min/mg
Databases for Systems Biology
Pyruvate Kinase 2.7.1.40
PEP + ADP = Pyruvate + ATP
Km ADP : 0.16 mM (+ FBP)
Specific Activity: None available
Databases for Systems Biology
1. Kinetic equations
2. Values for kinetic constants plus standard errors
3. Conditions under which enzyme was characterized
Networks
Network information is mainlyInaccessible in convenientformats, much work has to bedone by the user to extract the desired information.without much work.
The need for a model or network exchange format.
Networks
There is also the need for a network visualization standard.
Mirit I. Aladjem and Kurt Kohn
DCL: Gene Network Sciences
Model Databases
SBW
Desktop
Client
Web Services
=> translatorMatlab, XPP, FORTRANBerkeley Madonna, SBML, CellML, C, Java, Mathematica, etc…..
Database
=> SBML/SQL translator
Client
Other Systems eg BioSPICE
Peer Reviewed
Version ControllerScratchpad
Model Databases
• BIOSSIM (1968)
• ESSYN (1976)
• SCAMP (1983)
• SCOP (1986)
• METAMOD (1986)
• SIMFIT (1990)
• METAMODEL (1991)
• METASIM (1992)
• KINSIM (1993)
• GEPASI (1994)
• METALGEN (1994 ?)
• MIST (1995)
• METABOLIKA (1997 ?)
• METAFLUX (1997)
• SIMFLUX (1997)
• MNA (1998)
• CELLMOD (1998)
• FLUXMAP (1999)
• METATOOL (1999)
• VCELL (1999)
Modelling Tools
65-69 70-74 75-79 80-84 85-89 90-94 95-99
1
3
5
7
9
Period
Klaus Mauch, University of Stuttgart
SBML – Systems Biology Markup Language
The Systems Biology Markup Language (SBML) is a computer-readable format for representing models of biochemical reaction networks. SBML is applicable
to metabolic networks, cell-signaling pathways, genomic regulatory networks, and many other areas
in systems biology.
The Systems Biology Markup Language (SBML) is a computer-readable format for representing models of biochemical reaction networks. SBML is applicable
to metabolic networks, cell-signaling pathways, genomic regulatory networks, and many other areas
in systems biology.
The Systems Biology Markup Language (SBML) is a computer-readable format for representing models of biochemical reaction networks. SBML is applicable
to metabolic networks, cell-signaling pathways, genomic regulatory networks, and many other areas
in systems biology.
The Systems Biology Markup Language (SBML) is a computer-readable format for representing models of biochemical reaction networks. SBML is applicable
to metabolic networks, cell-signaling pathways, genomic regulatory networks, and many other areas
in systems biology.
The Systems Biology Markup Language (SBML) is a computer-readable format for representing models of biochemical reaction networks. SBML is applicable to metabolic networks, cell-signaling pathways, genomic regulatory networks, and many other areas in systems biology.
Originally developed Hamid Bolouri, Andrew Finney, Mike Huck and Herbert Sauro
Tool 1
Tool 2
Tool 2
SBML – Systems Biology Markup Language
XML based Standard
• Simple Compartments (well stirred reactor)
• Internal/External Species
• Reaction Schemes
• Global Parameters
• Arbitrary Rate Laws
• DAEs (ODE + Algebraic functions, Constraints)
• Physical Units/Model Notes
• Annotation – extension capability
SBML – Systems Biology Markup Language
What is XML?
<?xml version="1.0" ?>
<note> <to> Hobbit </to> <from> Orc </from> <heading> Note to Frodo </heading> <body> I want to eat you </body> </note>
SBML – Systems Biology Markup Language
XML has a hierarchical structure
<root> <child> <subchild>.....</subchild> </child></root>
Each node can also have optional attributes, eg <child name = “john”>
SBML – Example
<?xml version="1.0" encoding="UTF-8"?><!-- Created by XMLPrettyPrinter on 11/14/2002 --><sbml level = "1" version = "1" xmlns = "http://www.sbml.org/sbml/level1"> <!-- --> <!-- Model Starts Here --> <!-- --> <model name = "untitled"> <listOfCompartments> <compartment name = "uVol" volume = "1"/> </listOfCompartments> <listOfSpecies> <specie boundaryCondition = "false" compartment = "uVol" initialAmount = "0" name ="Node0"/> <specie boundaryCondition = "false" compartment = "uVol" initialAmount = "0" name = "Node1"/> <specie boundaryCondition = "false" compartment = "uVol" initialAmount = "0" name = "Node2"/> </listOfSpecies>
<listOfReactions>
<reaction name = "J0" reversible = "false"> <listOfReactants> <specieReference specie = "Node0" stoichiometry = "1"/> </listOfReactants> <listOfProducts> <specieReference specie = "Node1" stoichiometry = "1"/> </listOfProducts> <kineticLaw formula = "v"> </kineticLaw> </reaction>
<reaction name = "J1" reversible = "false"> <listOfReactants> <specieReference specie = "Node1" stoichiometry = "1"/> </listOfReactants> <listOfProducts> <specieReference specie = "Node2" stoichiometry = "1"/> </listOfProducts> <kineticLaw formula = "v"> </kineticLaw> </reaction> </listOfReactions> </model>
</sbml>
Other Related Efforts - CellML
CellML is a more comprehensive attempt at developing an
exchange standard, also defined in terms of XML.
However, it is much more complex and the designers of
CellML have not provided software support in the form of tools
and software libraries.
Data Formats
One other area which is even more difficult to resolve is
experimental data formats, microarray, proteomic, metabolmic,
basically all the omics.
Two projects are attempting to put some order in the data
format area, bioSPICE and particularly the DOE GTL project.
The Future
There is obviously a long long way to go.
Kinetic data must be more carefully curated.
Standards for exchanging data, models, including visualization
notations need to be developed further.
What are we dealing with?
Reaction systems working on multiple time scales:
1. Discrete deterministic events
2. Fast reactions
3. Continuous variables (modeled by ODES)
4. Continuous variables with additive and multiplicative noise
5. Stochastic discrete systems (Gillespie type)