Franck MolinaSophia Antipolis juillet 2008

FRE3009 CNRS / BIO-RAD

Modélisation et ingénierie des systèmes complexes biologiques pour le diagnostic

Biological processes modelling based on elementary actionsand synthetic biology

Complex System Modeling and Engineering for Diagnosis

Alliance betweenBIO-RAD and CNRS

Diagnosis, biotechnologies Discovery, Scientific research

SysDiag est dédiée à la recherche de diagnostic en santé humaine(Cancer, Alzheimer, cardiovasculaire, diabète, etc.)

Comprendre les bases de maladies multifactorielles (Alzheimer, Prion, Hépatite, Cancer, Cardiovasculaire, Diabète…)Et d’identifier les biomarqueurs associés à ces conditions pathologiques

SysDiag a pour objectif d’accélérer le transfert des découvertes de la recherche scientifique vers des applications mises sur le marché.

Modélisation et ingénierie des systèmes complexes biologiques pour le diagnostic

Crossing disciplines for innovation in Diagnosis and Life Sciences

Combining experimental biology and complex systems modelling approaches

70% experimentalists and 30% theoreticians

“Task-team” organization

The SysDiag model

Complex system modeling and engineering for diagnostic

SiliCell,

Immunology / BiotechnologiesAntibodies, proteomics, mass specSELDI, Prot-Prot Interactions, Bio-plex, Proteon, peptide and protein arrays, mRT-PCRHT antibody facilities.

Modeling and computing approachesMolecular modelingInformation systemsComplex systems modelingBiostatistics

Crossing disciplines for innovation in Diagnosis and Life Sciences

Complex pathology diagnosis : Causalities and biomarkers

New approaches for tomorrow’sdiagnosis

Clinicians

samplesExpertise

Path

olog

ies

of in

tere

st

PublicationsPatents

SoftwaresAlgorithms

TechnologiesMethodologies

BiomarkersDiagnostic probes

Diagnostics processesMolecular mecanisms

PlatformChemistryInteraction

Clinical Proteomic

PlatformBioinformaticsBiostatistics

PlatformAntibody Dev Pf

Bioinformati

csmolec

ularBiology

Immunology

Chemist

ry

biochem

istry

Mathemati

cs

SysDiag Platforms

SPR : ProteOn

CIP Peptides Chemistry-Interactions-ProteomicsHT Peptide synthesis , SPOT technique

Bioplex, Proteon SPR, protein arrays

2Dproteomics,MALDI, SELDI.

HT Antibody plateformHT mAb generation, screening and characterization

Mimétisme moléculaire:Localisation d’épitopes à partir d’approches phage display : MIMOP Prédiction de peptides antigéniques pour des immunisations ciblées : PEPOPBioinformatics 2006, BMC bioinformatics 2008

Amm8

Reactivity of "discontinuous" peptides designed by PEPOP from the 3D model of Amm8

Amm8

Lack of reactivity of the 15-mer overlapping peptides with anti-Amm8 antibody

Bioinformatics and biostatisticsMolecular modeling, epitope analysis, antigen prediction

Patient

followup

THERANOSTIC

Drug & Diag Discovery

DIA

GD

ISC

OVE

RY

DR

UG

DIS

CO

VER

YP

RA

CTI

CIA

NS

Questions Pathology characteristicsTherapeutic decision

Therapeuticproblem

Targetidentification

Activecompound

Clinicalstudies

Diagnosisproblem

Biomarkeridentification

Bindingprobes

Clinicalstudies

TEST DIAG

DRUG

treatment

MonitoringDiagnosis

AMA

AMA

2-4 years 1-2 years 1 year

3-6 years 6-8 years 1 year10 – 14 years

4 – 7 years

'Application for Market Authorization'

Clinical samples

Biomarkeridentification

Experimentalanalyses

Biomarkeridentification

Signal analysesBiostatistics

MolecularMechanism

understanding

Validation

SetOf

Biomarkers

Diagnostic Assay design

Specific probes

Research for early diagnosis and follow up of complex diseases

Qualityassessment

Systems BiologyComplex system modeling

HT mAb platfomand

Multiplexed assaysProfiling and omics

Biostatistics

Integrative biology for biomarker discovery

The NEPHRODIA Project

Collaboration LIA, CBS Sfax, Tunisia

Natural history and Diagnosis

Normal albuminuria Microalbuminuria Macroalbuminuria ESRD

Diabetesdiagnostic

diabetes

DN diagnostic

5-10 yrs 5-7 yrs5-10 yrs

Diabetic Nephropathy

Aims of the project• a better understanding of the role of Nephrin

and related proteins in the glomerular filtration process

• the identification of early DN biomarkers by using proteomic approaches

HypothesisProteins that are excreted in the urine precede albumin

MethodsCohort of DT1 patients and DT1 patients at-risk of DNComparative 2D GEL of urine and further identification of biomarker proteins

• ResultsA standardized protocol for urinary proteinspreparation has been set-upA study of the variability of the normal urinaryproteome has been conductedA database of proteins of the normal urinaryproteome has been constructed

DN biomarker discovery

Structural and functional analyses of key molecules in ND

Structure-functionanalyses of proteinnetworks involved in podocyte interactions and filtration

Molecular modeling of Nephrin proteinfamily and associated proteins

Combination of molecular results with kidneysimulations

Nephrodia

Step A : Heterogeneous biological

data recruitment, production, formalization

Step B: Heterogeneous graph

representation and analysis

Step C Identification of key

elements for early onset of disease

Step D : Multi-scale modelling of key elements influence on diseases associated

mechanisms

Step E validation

Biomarkers

Dynamic modelssimulations

Clinicalsamples

HeterogeneousGraphs

Clinicaldata

Proteomics

Genomics

Semi-Automatic Literature mining

Biomarker discovery loop

Combination of bio-experiments, literature mining and modeling approaches

Biomarker discovery loop

ClinicalSamples

Biomarkeridentification

High-throughput Complementary

Antibody pair design

Multiplexed assay

development

Genomics

Proteomics2D vs LC-MS

Knowledge base

Biophysic

010001011101010110010011101

Biological network Modelling, Simulation

Systems biology and discovery

Expert

UnderstandingObservation Engineering

DataInformation

Modelling

Control

Design

Synthetic BiologyRational vs Systematic ways

Biochemistry

protein-gene-RNA interactions

protein-protein interactions

PROTEOME

GENOME

TRANSCRIPTOME

Citrate Cycle

METABOLISM

Bio-chemical reactions

Multi-scale modelling

What do we know from biology

Biological data are partialNot always « true »

Not always understood

Biological networks are complexinter-dependent

dynamicredondent/robust

Scale-free ?

Molecular functions are complexregulated (+/-)

pléiotropicstochasticdynamic

Keyword : « it depends »

Keyword : « who knows ? »

Keyword : « nobody knows...»

Modeling objects or actions ?

The Time problem

The state problem

The Location problem

Modeling objects or actions ?

Bairoch A (1993). The ENZYME data bank. Nucleic Acids Res., 21, 3155-3156

Enzyme Classification E.C.

http://www.chem.qmul.ac.uk/iubmb/enzyme/

Nomenclature effort (~3800 E.C.)Linked to 3D structures

Problems :Close to biochemical point of view, far from cellular Pt of viewReduced to enzymatic processesBio-Object orientedDoes not address multifunction protein

Gene ontology

Multi-scale

Problems :Bio-object orientedDescription of THE function of the molecule.Fixed number of possible descriptions (sensitive to knowledge changes)No easy link to 3D structure

http://www.geneontology.org/

Ambiguité sur la fonction

Récepteur du FGF (facteur de croissance)

Quelle fonction pour cette protéine ?

KinaseDomain

FGF

GO:0003673 : Gene_Ontology (103367)GO:0008150 : biological_process (68451)

GO:0009987 : cellular process (26824)GO:0007154 : cell communication (7527)

GO:0007165 : signal transduction (5890)GO:0007166 : cell surface receptor linked signal transduction (2414)

GO:0007167 : enzyme linked receptor protein signaling pathway (701)GO:0007169 : transmembrane receptor protein tyrosine kinase signaling p

GO:0008543 : FGF receptor signaling pathway (54)GO:0005575 : cellular_component (54946)GO:0003674 : molecular_function (75116)EC 2.7.1.37

BioΨ biological processes description scheme based on elementary actions

A new approach :

Biological processes can be described independently from biologicalObject description.

Multi-scale biological process description could be compatible withA multi-scale component structure description

Does exist a limited set of elementary processes which combinedcould describe the biological function diversity ?

Protein are: multi-domainmulti-action

Kinase activities

FGF binding

Heparin binding

We need multi-scale views on biological functions

Molecular functions are dynamic and depend on their environment (biological context)

Biological Activities

Biological functionalities

Basic Elements of action(BEA)

Biological Roles cellular

molecular

Sub-molecular

Biochemistry whichleads to action

Angles of view on biological processesLevels of abstraction

-BEA refer to the elementary actions at a chemical level involved in biological processes.

-Biological Activities represent the use of a combination of BEA byfunctional domains to exert their activity at a sub-molecular level.

-Biological Functionalities represent the integration of the Biological Activitiesof molecular entities.

-Biological Roles represent the combination of the Biological Functionalitiesof different molecular entities within functional modules in the cell.

BioΨ elementary bricks:

ABond Modifiers

a split/linka acting on C-Cb acting on C-Oc acting on C-Nd acting on C-Se others B

Transfertsa Transferors

a acting on C-Cb acting on C-Oc acting on C-Nd acting on C-Se others

b Oxidoreductorsa acting on C-Cb acting on C-Oc acting on C-Nd acting on C-Se acting on Sf acting on N-Og acting on S-Oh others

CIntramolecular modifications

a Isomerorsa chiralityb cis/transc bond movesd others

DNon covalent interactions

a Binding:a Protein-Proteinb Protein-Nucleic Acidc Protein-Otherd Nucleic Acids-Nucleic Acidse Nucleic Acid-Others

b Transport:a Tunnelb Cargo

BEA : Classification of 97 basic elements of action (for all known processes)

Insulin

C3G

SHCIRS1SHPS1

CrkII

Grb2

SOS

ERK1

GSK3

IR

MEK1 MNK1

mTOR

PDK1

PKB

PKCζ

RSK

LAR

PP1CPP2A

PTENPTP1B

SHIP

SHP2

p85αp110α

PI3K

Glycogenesis

Rap1

Raf1

Rap1

Glucose uptake

Transcriptionregulation

NckIRS1

SOS

Ras

14-3-3

Raf1

cytoplasm

extracellular

Protein bindingNucl Ac binding kinase

phophatase

Biological Activities bricks

picosec

nanosec

micosec

second

minutes

hours

t scale

A

nm

micron

mm

cm

Dist scale

Molecular description Processes descriptionElementary bricks of biological processes

J. Mol. Biol. 2004, J. BioSc. 2007

Scales Inter-dependenciesP(l): Biological ProcessA(l): biological actorl = level of abtractionP(l)

Composed of i P(l-1) performed by j A(l-1)

Conditionalities on the i P(l-1)SequentialityLocalizationbiochemical stateconformational state

Kinetics of P(l-1) performs by A(l-1) in a context of SequentialityLocalizationbiochemical stateconformational state

l>2

l>2

[always/never][before/after/while]boolean

BEA classes comparison

0

10

20

30

40

50

60

70

80

90

classA classBa classBb classC

E coliBacillusPombeHeamophimycobactArabidopS. CerevisC elegFruit flyMouseratBovhuman

bond

mod

ifica

tion

Grou

p tra

nsfe

rt

chem

ical r

eorg

anisa

tion

prot

on/el

ectro

ntra

nsfe

rt

BEA relative distribution among species

0

5

10

15

20

25

30

35

Aa:

CC

Aa:

CO

Aa:

CN

Aa:

CS

Aa:

mis

c

Ba:

CC

Ba:

CO

Ba:

CN

Ba:

CS

Ba:

NO

Ba:

PO

Ba:

SO

Ba:

SS

Ba:

mis

c

Ba:

lab

% u

tiliz

atio

n

0

510

1520

25

3035

Bb:

CC

Bb:

CO

Bb:

CN

Bb:

N

Bb:

S

Bb:

NO

Bb:

SO

Bb:

ion

Bb:

mis

c

Bb:

etra

ns

Ca:

chir

Ca:

ct

Ca:

btra

ns

Ca:

mis

c

BEA codes

% u

tiliz

atio

n

Escherichia coli Bacillus subtilisHaemophilus influenzae Mycobacterium tuberculosisSchizosaccharomyces pombe Saccharomyces cerevisaeCaenorhabditis elegans Arabidopsis thalianaDrosophila melanogaster Mus musculusRattus norvegicus Bos taurusHomo sapiens

Ca

A: Bond Modifications B: Transferts C: Chemichal reorganisation D: No Chemichal Modification

BEA relative distribution among species

BioΨ what for ?

Formalized functionnal description common to all species

Biological function knowledge base.

Component

Process(Conditions)

Localization(Constraints)

Orthologues

Protéines AutresComposantsAcides NucleiquesProcessus Localisation > Espèce

SiliBase Ver. β1.0

Mapping structure-function relationships(support for 3D structure or function predictions, Design of new functions ?)

BioΨ what for ?

limited # folds in PDB

Different Biological FunctionalitiesSimilar biological activities

Orthogonal bundle fold

Functional comparison between organisms

BioΨ allows processes comparison without sequence comparison.

BioΨ overcomes the problem of :Emergent processesDomain shufflingEtc.

BioΨ what for ?

Modeling and simulation of biological processes

Elementary bricks of process at each level can be consideredas primitives for formal language construction.

Genericity of process description is an advantage for nonDeterministic modeling

Easy to use with cellular automaton and multi-agents approaches

On going works : MitochondriaB. Subtilis (EU BaSysBio)Synthetic Biology

BioΨ what for ?

15 Biological Functionalities11 Biological Activities16 Basic Elements of Action

Generic TCA Cycle : a case study

Pathway comparison without sequence analysismulti-agent simulations

BA_thioacyl_thiol_transferase BA_thioacyl_thiol_transferase using Input1 and Input2 to obtain Output1 and Output2(Ba:CS.2 with C-S-R==Input1 andwith C-S-R==C(=O)-S-Rwhile Ba:lab.2 with R-H==Input2 andwith R-H==R-SH andwith R-H==R-CH(R')-SH )and always after ( Ba:lab.2 back with R-H==Output1while Ba:CS.2 back with C-S-R==Output2)

BA_thio_oxidase BA_thio_oxidase using Input1 and Input2 to obtain Output1 and Output2Bb:S.1 with R-SH==Input1 and

with R'-SH==Input1 andwith R-S-S-R'==Output1

and always after Bb:CN.1 back with R-N=C(R')-R''==Input2 and always after Ca:btrans.2 with R-C(=N-R')-R''==Output2

BA_FADH2_NAD_reductase BA_FADH2_NAD_reductase using Input1 and Input2 to obtain Output1 and Output2Ca:btrans.2 back with R-C(NH-R')=R''==Input1and always after Bb:CN.1 with R-N=C(R')-R''==Output1and always after Bb:CC.1 back with R-C(R')=C(R'')-R'''==Input2 and

with R==C(R)-C(=O)NH2and always after Ca:btrans.5 with R-C(=R')-N(R'')-R'''==Output2

BA_thioacyl_keton_transferase BA_thioacyl_keton_transferase using Input1 and Input2 and Input3 to obtain Output1 and Output2(Ba:CS.2 with C-S-R'==Input1 while Ba:lab.1 with H-OH==Input2)and always after (Ba:lab.2 back with R-H==Output2 and

with R-H==R-S-Hwhile (Ba:CO.1 back with C°==Interm1while Aa:CC.2 back with R-C(OH)(R')-R''==Output1 and

with R-CO-R'==Input3 andwith R'-H==Interm1 and with R'-H==R'-O-H ))

BioΨ formal description

Central Carbon metabolism (Gluc/Mal shift)

BioΨ formalization,Modules identification, Context variation and spacio-temporal simulations

Formalization of structural and functional knowledgeFrom molecular details to network

ExperimentalParameters

Multi-scaleconstraints

Multi-scalestructural organization

Model Repository

SBML

BioΨ language

Multi-agents systemDynamic and spacial

simulation

Flux analysesElementary modes

Multi-scale modelling

Model checkingRobustnessDynamic modellingODEEtc.

Complex System Modeling and Engineering for Diagnosis

CompuBioticA synthetic biology approach for new Diagnostic devices : CR Cancer

Complex System Modeling and Engineering for Diagnosis

Synthetic biologyA) the design and construction of new biological parts, devices, and systemsB) the re-design of existing, natural biological systems for useful purposes.

Zauner , 2006.

Design network of biomolecule able to realize elementary tasks which, whencombined perform « programmed » processes.

Our goalDesign and build a synthetic Vesicule « programmed » to perform

in vitro or in vivo diagnostic assays

Colo-rectal cancer diagnosis and follow-up

« Close to the patient » simple assay

Multi-parametric assay

Sophisticated signal integration(qualitative, quantitative, temporal, spacial etc.)

Result return in a simple way (local dying)

- The test can detect abnormalities only in the lower part of the rectum. - Additional procedures are necessary if the test indicates an abnormality.

- Often part of a routine physical examination. -No preparation of the colon is necessary. - The test is usually quick and painless.

Digital Rectal Exam (DRE)

- The test may not detect some small polyps and cancers. - Thorough preparation of the colon is necessary before the test.- False positive results are possible. - The doctor cannot perform a biopsy or remove polyps during the test. - Additional procedures are necessary if the test indicates an abnormality.

- This test usually allows the doctor to view the rectum and the entire colon. - Complications are rare. - No sedation is necessary.

Double Contrast BariumEnema(DCBE)

- The test may not detect all small polyps and cancers, but it is the most sensitive test currently available. - Thorough preparation of the colon is necessary before the test.- Sedation is usually needed. - Although uncommon, complications such as bleeding and/or tears in the lining of the colon can occur.

- This test allows the doctor to view the rectum and the entire colon. - The doctor can perform a biopsy and remove polyps during the test, if necessary.

Colonoscopy

- This test allows the doctor to view only the rectum and the lower part of the colon. Any polyps in the upper part of the colon will be missed.- There is a very small risk of bleeding or tears in the lining of the colon. - Additional procedures, such as colonoscopy, may be necessary if the test indicates an abnormality.

- The test is usually quick, with few complications. - Discomfort is minimal. - In some cases, the doctor may be able to perform a biopsy(the removal of tissue for examination under a microscope by a pathologist) and remove polyps during the test, if necessary. -Less extensive preparation of the colon is necessary with this test than for a colonoscopy.

Sigmoidoscopy

- This test fails to detect most polyps and some cancers.- False positive results are possible. ("False positive" means the test suggests an abnormality when none is present.) - Dietary and other limitations, such as increasing fiber intake and avoiding meat, certain vegetables, vitamin C, iron, and aspirin, are often recommended for several days before the test. - Additional procedures, such as colonoscopy, may be necessary if the test indicates an abnormality.

- No preparation of the colon is necessary. - Samples can be collected at home. - Cost is low compared to other colorectal cancer screening tests. - FOBT does not cause bleeding or tears in the lining of the colon.

Fecal Occult Blood Test (FOBT)

DisadvantagesAdvantagesTest

Table: Advantages and Disadvantages of Colorectal Cancer Screening Tests

Need something new !Blood test CA19-9, CEA etc. poor specificity

Test Hemocult® simple mais, peu sensible, peu spécifique

Coloscopie lourde à mettre en œuvre et peu sensible

Pas de test periphérique spécifique

Scientific contex

Il existe pourtant des biomarqueurs de surface ou sécrétés

Colo-Rectal CancerResponse of the patients to a treatment

First set of biomarkers to predict patient response to Campto®

Responders Non-respondersResponders Non-responders

RespondersNon-responders

RespondersNon-responders

RespondersNon-responders

(Coll. CRLC,Aventis/Pfizer)

International patent CNRS/PfizerJ.Clin.Onco. 2007, Cancer Research 2008, Mol. Cancer 2008

Main strategies in synthetic biology

Bromley et al ACS Chem biol.2008

Ron Weiss, MIT, Harvard,Design of a lentivirus able to target breast cancer intra-cellular biomarkers RNAi networks

Steem cell reprograming by bact. for tissus reconstructionPL Luizi, Roma autocatalytic vesicues constructions, minimal cells.J. Stelling ETHZ, Zurich Electronic-like circuit design with compasable parts (Bact.)V. Dos santos, Helmotz Inst. Germany reprogrammed bact. to target cancer cell

Pseudomonas putida.Luca Cardelli , Microsoft Research, Cambridge, Uk.Cell automaton, semantic of collective behaviourJim Haseloff, Univ Cambridge UK , Plant reprogramming

Alfonzo Jaramillo, E. Polytechnique, reseau RNAi (théorique), proteine design.Antoine Danchin, Inst Pasteur. (Bact)

Design – Simulation - Experimentalvalidation

BioΨFormalization

SimulationStochastic Cellular automaton, Multi-Agent

HsimFunctional bircks

Modelling:Control Th.flux analyses

ODEElementary modes

Design synthetic network

E1

E3

S1

P1

P3

E4

E5

S2

P2

P4

P5

P4P3

Biotechnology

Auto-organisation/Robustness

Validation in vitro

Identification and characterization of molecular compounds

in vitro Proof of concept

Notre Stratégie

Standardized catalog of proteic biological compounds:Processes are formalized (ready for modelling)Biological behavior characterized experimentaly (ready to use in a synthetic system

« compound » propertiesrobustnessstabilityfunctional diversity relative to context modifications

Design

System design using our compound catalogSBGN and Celldesigner

BioΨ modellingFormal description

Simulation : Stochastic Cell automatonand multi-agent

Simulation

Construire le réseau biologique synthétique

Simuler son comportement à l’échelle.

50 nm

Experimental validation

Stable Vesicles construction (liposomes) ~100nm

Introduction of chosen functional compounds (GOD-POD)

Opérational assays of full synthetic system (in vitro)

SP

S

P

S

Experiments• Enzymatic system : GOD-POD (kit Sigma GAGO20)

• Glucose oxidase – peroxidase• Reactions :D-glucose + O2 + H2O D-gluconic acid +H2O2H2O2 + reduced o-dianisidine (colorless) -> oxidized o-dianisidine (brown)

D-glucoseGlucose oxidase

O2H2O

D-gluconic acidH2O2

reduced o-dianisidine(colorless)

oxidized o-dianisidine(brown)

Peroxidase

Simulation

Vesicles Construction

2 Liposomes formulation: Lipid film hydration:

1 Injection Lipides/ethanol in H20 Solution

Sabine PérèsStephanie RialleFranck MolinaLiza FelicoriAlain ThierryDavid JeanChams KifagiCecile FleuryNicolas SalvetatEve DupatViolaine MoreauKarine Kaminski

A Jaramillo, E. polytechnique, Palaiseau, Fr P Noirot, INRA Jouy-en-Josas, FrJ Stelling, ETHZ, Zurich, CHJ Banga, CSIC, Vigo, SpainB Schickowski, Inst. Pasteur, Paris, FrP Amar , LRI orsay, FrF. Képes, Genopole Evry, FrE. Klipp, MaxPlanck Inst.,Berlin DF. Fages, INRIA

Collaborators

Montpellier, France

Groupe National de Biologie Synthetique, Genopole Evry®A. Jaramillo, F. Molina, F. Fages

EU, BaSysBio

18 academic staff (8 permanents)9 CNRS, 2 INSERM, 2 PostDoc, 5 phD

23 Bio-RadBiorad 1 RdS, 4RdP, 9CdP, 4AcP, 4T, CDD 1CdP

5 undergraduates70% wet lab experimentalists 30% theoreticians