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

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are needed to see this picture.

SimulationSimulation

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are needed to see this picture.

ResultsResults

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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