Mobile Process Algebras in Systems Biology
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Transcript of Mobile Process Algebras in Systems Biology
Mobile ProcessMobile ProcessAlgebras inAlgebras in
Systems BiologySystems Biology
New Challenges and Opportunities New Challenges and Opportunities
Corrado PriamiUniversity of Trento
1. 1. WhatWhat we can do we can do2. 2. WhyWhy we want to do it we want to do it 3. 3. WhereWhere we are we are4. 4. HowHow we can do it we can do it5. The stochastic pi5. The stochastic pi6. Its biochemical version6. Its biochemical version7. The BioSPI tool7. The BioSPI tool8. A success story8. A success story9. Concluding remarks9. Concluding remarks
AGENDA
WhatWhat we can do we can do“In Silico” Virtual Distributed Lab for Systems Biology
• Modeling dynamic evolution of bio-systemsNot only structures (genome), but functions
• Analysis of their propertiesCausality, Locality, Concurrency, feedback loops
• Comparison for similar/equivalent behaviorBisimulation based equivalences/Modular Cell BiologyApplication of knowledge to similar classes of diseases
• Simulation of time/space evolutionStochastic run-time of languages/Parameter fitness and exploration
• Predicting behaviorLooking at the computational space of models
• Data bases of (behavior) functionalitiesPrograms as data + a run time engine
• Connection with high-throughput toolsSpecifications inferred from actual data
A possible architectureA possible architecture
We need biologists to use our tools and this implies
1. We must hide as much formal details as possible from the user,2. We must include in the framework all the tools they usually work with
A man on the moon visionA man on the moon vision
Programming the cellNew computational paradigms,
new primitives for programming,new software development tools,
new (living) hardware.New drugs development,
new genetic therapies,new cell repairing tools,
predictive, preventive, personalized medicine
First step: complete understanding of living matter functions
WhyWhy we want to do it we want to do itHigh impact on health and quality of life environmental protection (reduction of in vivo and in vitro experiments) software development (new primitives and paradigms) social and economical models of evolution
Living devices: machine are already there (bacteria, eukaryotic cells, etc.).
Once we completely understand their physical layer,we only need a hierarchy of software on top of them
BUILDING A CELL COMPUTERBUILDING A CELL COMPUTER is BUILDING A SOFTWARE INTERPRETATION BUILDING A SOFTWARE INTERPRETATION
Result Interpretation of the new behavior/ new states
Execution Perturbation of normal behavior
E.coli: smaller thanPentium gate,~ 1M molecules, ~ 1M ROM,~ 1M aminoacids PS
“Shapiro,Cardelli”
DOE visionDOE vision
goal 1goal 1Identify and characterize the molecular machines of life
goal 2goal 2Characterize gene regulatory network
goal 3goal 3Characterize the functional repertoire of complex microbial communities in their natural environments at the molecular level
goal 4goal 4Develop the computational capabilities to advance understanding of complex biological systems and predict their behavior
Systems BiologyGain a Gain a comprehensive and comprehensive and predictive predictive understanding of the understanding of the dynamic, dynamic, interconnected interconnected processes underlying processes underlying living systemsliving systems
LONG-TERM IMPACT: predictive and preventive medicine, rationale drug discovery and design, cell models and simulationcell models and simulation, cell programming and repair, biocomputing and biocomputerscell programming and repair, biocomputing and biocomputers
WhereWhere we are we are
On the starting blocks, but …
• we developed the first tool (BioSPI and Stochastic pi)• we applied it to a real case study (inflammatory processes
in brain vessels)
HowHow we can do it we can do it
What is Systems BiologyWhat is Systems BiologyLeroy Hood (invented systems biology)
Building models of biological systems and thentuning/validating them via (high-throughput) experiments that provide feedback.
Reductionism is replaced by hypothesis driven investigation.
Robin Milner (invented mobile process algebras)
Computer science as an experimental science.
Computer systems are first modeled (generation of hypothesis),
then implemented and tested (experiments)to refine/validate the model (feedback loop).
Abstracting from experiments, Systems Biology is Computer Science in the applicative domain of life science
From structures toFrom structures tofunctions in Biologyfunctions in Biology
New vision of biological systems• Bio-components as information and computational devices• Millions of simultaneous computational threads active (e.g., metabolic networks, gene regulatory networks, signaling pathways).
• Components interaction changes the future behavior• Interactions occur only if components are correctly located
(e.g., they are close enough or they are not
divided by membranes).
Interpreting Bio-components as Processes, Concurrent, Distributed, Mobile Systems have the above characteristics.
Mobile process algebrasMobile process algebras
CompletenessCompleteness CompositionaliCompositionalityty
ConcurrencyConcurrency CostCost
TMTM
-calculus-calculus
Petri NetsPetri Nets
CCS/CSPCCS/CSP
Mobile Mobile processprocess
algebrasalgebras
“Meredith”
Formal models of Bio-SystemsFormal models of Bio-Systems
Process Algebras for Mobility• Compositionality• Simple Abstractions• Well-developed theory for analysis and verification• Tools already developed and available
CompositionalityCompositionality
1. Assign meaning to the basic graphical notations2. Interpret them as process calculi primitives3. Compose the processes to formally specify the whole system
The pi-calculusThe pi-calculus
MoleculeMolecule ProcessProcess
Interaction Interaction capabilitycapability ChannelChannel
InteractionInteraction CommunicationCommunication
ModificationModification State and/or State and/or channel changechannel change
Modeling paradigm of bio-Modeling paradigm of bio-componentscomponents
With the same principles specify chemistry, organic chemistry, enzymatic reactions, metabolic pathways, signal-transduction pathways…and ultimately the entire cell.
Molecule --- ProcessesMolecule --- ProcessesCompartments --- Private names and Compartments --- Private names and
scopescope
SYSTEM ::= … | ERK1 | ERK1 | … | MEK1 | MEK1 | …
ERK1 ::= (new internal_channels) (Nt_LOBE |CATALYTIC_CORE |Ct_LOBE)
ERK1
Domains, molecules, systems ~ Processes
Compartments, membranes ~ Restriction
“Shapiro”
Interaction capability --- Global channelsInteraction capability --- Global channelsChange of future interactions --- mobilityChange of future interactions --- mobility “Shapiro”
Molecular interaction and modification ~ Communication and change of channel names
p-tyr replaces
tyr
KINASE_ACTIVE_SITE | T_LOOP {p-tyr / tyr}
Actions consumed alternatives discarded
tyr ! [p-tyr] . KINASE_ACTIVE_SITE + … | … + tyr ? [tyr] . T_LOOPY
ERK1MEK1Ready to
send p-tyr on tyr !
Ready to receive on
tyr ?
pY
The stochastic pi-calculus The stochastic pi-calculus
Biology is driven by quantities (e.g., energy, time,
affinity, distance, amount of components).
Stochastic variant of process algebras must be considered
Simulation techniques come into play
Syntax and semanticsSyntax and semanticsWe associate the single parameter r in (0, ∞] of an exponential distribution to each prefix ; it describes the stochastic behavior of the activity
.P is replaced by (, r).P
The delay of the activity (x, r) is a random variable with an exponential distribution.
Exponential distribution guarantees the memoryless property: the time at which a changeof state occurs is independent of the time at which the last change of state occurred.
Bang “!” is replaced by constant definition and the structural congruence accordingly extended with
A(y) congruent to P{y/x}
if A(x) = P is the unique defining equation of constant A withx = fn(P)
Race condition is defined in a probabilistic competitive context: all the activities that are enabled in a state compete and the fastest one succeeds.
Stochastic TS and CTMCStochastic TS and CTMC
A transition system is an oriented graph that connectsthe states through which a process can pass with arcscalled transitions and possibly labeled with informationon the activities that causes the state change.
TS resembles stochastic (Markov) processesexcept that TS can have pair of statesconnected by more than one transition.
(A, r)
(A, r)
TS(A, 2r)
CTMCSimpleGraph
Manipulation
Biochemical stochastic pi-Biochemical stochastic pi-calculuscalculus
Gillespie (1977): Accurate stochastic simulation of chemical reactions
Modification of the race condition and actual rate calculation according to biochemical principles
“Shapiro”
The actual rate of a reaction between twoproteins is determined according to a basal rateand the concentrations or quantities of the reactants
Biochemical stochastic pi-Biochemical stochastic pi-calculuscalculus
Reduction Semantics
Biochemical stochastic pi-Biochemical stochastic pi-calculuscalculus
Computing rates according to bio intuition
Inductively counts the number of receive operationsEnabled on the channel x.
The BioPSI systemThe BioPSI system
Compiles (full) pi calculus to FCP/Logix
Incorporates Gillespie’s algorithm in the runtime engine
BioSPI
Transcriptionalregulationby positivefeedback
Interphase
G1: growth phase, synthesis of organelles
S: synthesis of DNA (replication)
G2: growth; synthesis of proteins essential to cell division
Cycle duration in human liver cells
G1 G1 9 h9 h
SS 10 h10 h
G2G2 2 h2 h
MM 50 min50 min
Eukaryotic cell cycleEukaryotic cell cycleMitosis
prophase methaphase anaphase telophase
Nasmyth’s model Nasmyth’s model (1996)(1996)
At STARTSTART a cells confirms that internal and external conditions are favorable for a new round of DNA synthesis and division and commits itself to the process.
S(anaphase)
G1
G2
M
M
(metaphase)
START
FINISH APC APC
CDK
cyclin
CDKCDK
+
+ +
+cell division
S(anaphase)
G1
G2
M
M
(metaphase)
START
FINISH APC APC
CDK
cyclin
CDK
cyclin
CDKCDK
+
+ +
+cell division
Cycle with two states (G1 and S-G2-M) separated by two irreversible transitions STARTSTART and FINISHFINISH.
When DNA replication is complete and all the chromosomes are aligned, the second transition of the cycle (FINISHFINISH) drives the cell in anaphase.
CDK = Cyclin-Dependent Kinase; APC = Anaphase-Promoting Complex
STARTSTART is triggered by the activity of a protein kinase (CDK) associated with a cyclin subunit.
FINISHFINISH is accomplished by proteolytic machinery (APC) thatinhibits the activity of cyclin/CDK dimer.
The molecular mechanismThe molecular mechanism
APC destroys CDK activity degrading cyclin and
cyclin/CDK dimers inactivate APC by phosphorilating some of its subunits.
CDK = Cyclin Dependent KinaseAPC = Anaphase Promoting ComplexCKI = Cyclin-dependent Kinase Inhibitor
degraded cyclin
degraded CKI
START FINISH
CDK activity drives cell through S phase, G2 phase and up to the metaphase
Moreover, cyclin/CDK dimers can be put out of commission also by the stoichiometric binding with an inhibitor (CKI)
CDK and APC are antagonistic proteins:
Fundamental antagonismFundamental antagonism
The APC extinguishes CDK activity by destroying its cyclin partners, whereas cyclin/CDK dimers inhibit APC activity by phosphorilating CDH1. Two alternative stable steady states of the
cell cycle:
G1 state with high CDH1/APC activity and low cyclin/CDK activity S-G2-M state with high cyclin/CDK activity and low CDH1/APC activity.
CDC14cyclin/CDK CDC20/APC
ON
CDH1
APCAPC
OFF
CDH1P
CDC14CDC14cyclin/CDK CDC20/APCCDC20/APC
ON
CDH1
APC
ON
CDH1
APCAPC
OFF
CDH1P
APC
OFF
CDH1P CDH1P
12 polypeptides+ 2 auxiliary proteins CDH1 and CDC20
APC
CDC20
CDK – APC antagonism specification
BioSPI specification specification
SYSTEM = CYCLIN | CDK | CDH1 | CDC14 | CKI | CLOCK
BioSPI Simulations
Time (min)
N.
of m
olec
ules
CYCLIN_BOUND
Fictious values for the initial number of molecules!
16 molecular species16 molecular species
24 domains; 15 sub-24 domains; 15 sub-domainsdomains
Four cellular Four cellular compartmentscompartments
Binding, dimerization, Binding, dimerization, phosphorylation, phosphorylation, de-phosphorylation, de-phosphorylation, conformational changes, conformational changes, translocationtranslocation
~100 literature articles~100 literature articles
250 lines of code250 lines of code
ERK1RAF
GRB2
RTK
RTK
SHC
SOS
RAS
GAP
PP2A
MKK1
GF GF
MP1
MKP1
IEG
IEP
IEP
J F
The RTK-MAPK pathwayThe RTK-MAPK pathway
A success storyA success storyA simulation of extra-vasation in multiple sclerosis has highlighted anew behavior of leukocytes proved in lab experiments a posteriori
Selectins/Mucins
PSGL -1/E & P-SelectinIntegrins
a4 b1 / VCAM-1LFA-1/ICAM-1
lymphocyte1. Tethering
and rolling
2. Firm arrest
3. Diapedesis
Activationof G protein
Activationof integrins
Hematic flow
Endothelium
ImplementationImplementation
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
SimulationSimulation
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
ResultsResults
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Prediction of rolling cells percentage as a function ofvessel diameters
Recent evolutionsRecent evolutionsFirst attempt: lambda-calculus
Buss, Fontana -- no concurrency
Second attempt: (stochastic) pi-calculusPriami, Regev, Shapiro, Silvermann
Then:
BioAmbients, Brane Calculi -- Cardelli et al.
Core Formal Biology, CCS-R -- Danos et al.
Beta binders -- Priami, Quaglia
ConclusionsConclusions
Unique opportunity to change future life science,but also future computer science
We have a lot to do, butwe are in the position to win the challenge, if
we establish a P2P collaboration between BIO and IT
we find a common language and common expectations
we set up interdisciplinary curricula and carry out interdisciplinary research projects
Acknowledgements:Acknowledgements:Bioinformatics group at the University of Trento:
Corrado Priami, Paola Quaglia
Daniel Errampalli, Katerina Pokozy
Federica Ciocchetta, Claudio Eccher,Paola Lecca, Radu Mardare, Davide Prandi, Debora Schuch da Rosa, Alex Vagin
Alessandro Romanel
www.dit.unitn.it/~bioinfo