COSBI is a bioinformatics research center operating in the ... · annotation analysis...

2 February 2005

2 February 2015

COSBI is a bioinformatics research center operating in the fields of systems nutrition and systems pharmacology

COSBI headquarters located in a historical tobacco factory

The Microsoft Research - University of Trento Centre for Computational and Systems Biology • 3

Paolo ColliniUniversity of Trento

Corrado PriamiUniversity of TrentoPresident & CEO

Pierpaolo DeganoUniversity of Pisa

Assembly of Parties

Structure

Opening

Advisory Board

Board of Directors

Pier Paolo Di FioreIFOM

Leroy HoodInstitute forSystems Biology

Michael MüllerUniversity of East Anglia

Previous AB members:

Marvin Cassman, David Harel, Manuel Peitsch, Judith Armitage, Gianfranco Balbo, John Heath, John Tyson

AB role:scientific advice and evaluation

2 February 2005the signature

7 December 2005opening with a message from the President of Italian Republic Ciampi

2 April 2006scientific opening

Jim KarkaniasMicrosoft Corporation

Luca CardelliMicrosoft Research

Daron GreenMicrosoft Research

4 • COSBI - April 2015

COSBI works in the bioinformatics market by applying proprietary and public methods to systems pharmacology and systems nutrition to promote health with personalized medicine and nutrition in a collaborative effort with the international scientific community.

COSBI operates in the context of translational medicine and nutrition by integrating molecular and clinical, qualitative and quantitative, large scale and mechanistic data from different sources. Experimental data are continuously enriched and validated with scientific literature search.

Expertise

Metabolic disorders:obesity, diabetes, metabolic syndromeNeurodegenerative disorders:Alzheimer’s, dementia, autismData types:genomics, proteomics, transcriptomics, lipidomics,metabolomics, clinical markers, diet, lifestyle, physiologicalAnalyses:data aggregation and exploration, clustering, topological and functional network analysis, literature andtext mining, machine learning, stochastic and deterministic simulation, data visualization

Some indexes

2010 2011 2012 2013Avg IF 3.59 3.62 4.54 4.3Journals 19 21 26 24TOT PUB 40 46 55 36Invited talks 19 23 29 25Man Months 294 225 214 185

WHO

measurements

data productionand collection

WHAT

module identification

network analysis

mechanistic details

modeling

WHY WHERE

simulation

scenario generation

WHEN

pred

iction

and

cont

rol

analy

sis in

terp

retat

ionhy

poth

esis

gene

ration

prediction and control

functional annotation + low throughput lab work

Molecular understanding of (nutritional) diseases and health

MECHANISTIC DYNAMIC

CORRELATION STATIC

clustering stratification

multisource, omicsdata aggregation

and analysis

HOW

H2OH+

Glycine GlycineCO2NH3

Serine

NAD(P)H

NAD(P)+

1037 963342 902969353959 9664 31095963 87799 26101585310 3926 86678966 89567 2810158696 849508767 10973 91645 2598588562 589458824 10950 92684 269838858 8 5940 90078968 916768 2110138896 6 495390159 8956 89067 2310089126 759479038 5936 86046 2400791586 4990 9407 5932 85755 249948948 501019928 5915 87685 261006900 56010089341937 89557 301008902 6 4397394175 938 9147 829991909 7 40984940 4

1037 963966 973 950 968 956 936 932915937938

clinical and physiological data

omics and molecular data

qua

litat

ive,

larg

e sc

ale

dat

a

quantitative, sm

all scale data


COSBI applies network analysis to identify diagnostic and prognostic biomarkers. Phenotypes and omics data analysis select the biological network that drives the biological process. These networks are simulated to elucidate the molecular mechanisms and to choose potential drug/micronutrient targets.

Simulation of molecular models is also used to determine the parameters of clinical and physiological models.

Important visits

Strategic partnerships

18 June 2008Turing Award winnerRobin Milner

6 November 2008Nobel Prize winner Sydney Brenner

23 June 2009Turing Award winnerTony Hoare

DATA

ClinicalMarkers

GenomeProteome

Metabolome

Scientificliterature

MetabolicNetwork Protein

ProteinInteraction

Network

GeneRegulatory

Network

Target IdentificationDrug RepositioningModule Identification And Raking

Network Analysis (Topology+data Driven)Data MiningEnrichment Analysis

In-silico experimentsWhat-If analysisPrediction and controlPk/PdDose-regimen analysis

Stochastic, Deterministic, Hybrid SimulationODE, Language-based ModelingInference Procedures/fitting

OntologiesOmim

Drug-banks ODEChemical reactions

Languages Inference

Modules

FittingKinetics

Genomics

OU

TP

UT

INP

UT

ME

TH

OD

S

Multiomics(Integrated Data Analysis)

Signatures

DiagnosisPrognosisHealth Measures

Patient Stratification

Quan

titat

iveHe

tero

gene

ous

Quali

tativ

eEn

viron

men

t

Metabolomics

Multi

-om

icsMu

lti-s

ource

Multi

-sca

leInd

ividu

al

DATA INTO CONTEXT DYNAMIC MODELS

Proteomics

Lipidomics

Diet

Microbiome

LifeStyle

Functional

Analysis

ORGAN-LEVELPHENOTYPE REPRESENTATIONS

MOLECULAR PROCESSES

Simulation of molecular interactionsHigh-level variables Equations

D: administered glucoseJ: jejunumR: delay cmptL: ileumG: plasma glucoseI: plasma insulin

S

J R L

G

IVI

VG

D

kjsS

krjS klrR

kgjJ kglL

GPROD

kxgilG

-kxlI

kigmax

?

TISSUEFORMATION

CELL-CELLINTERACTION

HORMONESIGNALING

CerSph

Lysosome

ER

aSMase

SM

SM

CERSph aCDase

Cell Membrane

CerS

Cytoplasm

Plasma

Nucleus

CDase

Salvage pathway

Endocyticvesicle

SK

SPPaseS1Plyase

Ethanolamine phosphate+

hexadecenal

3kdhSphpalmitoyl-CoA

L-Serine

dhSph

Acyl-CoAdhCer

Des1

SPT

DSR

CerSde novo synthesis

CDase

S1P

Mitochondria

CerS

Sph Cer SM

SMS

SMasenCDase

PC DAG

palmitoyl-CoA

CoA

Acot2

palmitate

SK

S1P

SphCer

Cer

SM

SMSphS1P

PC DAGSPPase

CDase

CDase

nSMase

SMS2

nSMase

SMS2

CFTRABC

S1PlyaseEthanolamine phosphate

+hexadecenal

PC DAG

Sphingomyelinase pathway

SMLacCer

GM3

GluCer

GluCer

Cer

GolgiApparatus

LacCerS

GM3S

FAPP2CPase

Cer1P Cer SM

SMS1

SMaseCerkSapC

PC DAG

GDaseGtase

CPE GalCerGLASMSr

Connecting de novoand sphingomyelinase

CERT

Gi/o

Gq

G12-13

PKCCa2+

PLC

PI3k

RAS

ERK

PKB/Akt

RAC

Migration,vascular tone, endothelial barrier

function, neural cell communication

Survival

Proliferation

S1PR2S1PR3

S1PR4

S1PR5

AC

cAMP

S1P signaling

RhO

Cdc42

S1PR1

JNK

TNF

TNFR

NF-kB

Inflammation

InsulinAction

IRS1

PI3k

Akt/PKB

PP2A

PKC

IRInsulin

Cer

Cer

IKK

CAPK

RAF1

MEK1

ERK1/ERK2

Cer signaling

Cer PP1

SR proteinsCaspase-9

BCL-X

Cer signaling

GM3

GM3

GM3 signaling

CatepsinD

Cav1


Main journals

Nat communications, PloS ONE, Biol Letters, BMC Syst Biol, Bioinf, Drug Discovery Today, Nat Cell Biol Syst Biomed, Net Biology, J. Nutr Biochem, J Chem Phys, ACM Comp Surveys, Mol Nutr, Gene & Nutr, Clinical and Translational Gastroent, Food Res, J of Env Mgt, WIREs Syst Biol and Med

COSBI invented an innovative method to determine diagnostic and prognostic biomarkers validated in an international competition and industrial projects.

Gene Expression

Gene Ranking

A1

1

2 23

3

4 45

5

6 67

7

8

8

9 910

10

11 1112

12

13 1314

14

15

15

1616

17

17

1

1

2 23

3

4

4

5

5

6 67

7

8 89

9

10

11

11

12 1213

13

1415

15

16

17

17

1

1

2 23

3

4 45

5

667

7

8 89

9

10 1011

11

12

12

13 1314

14

15

15

16

16

17 17

B C A B C

Signature Extraction

A B C

SignatureComparison

d(A,C)2

410

1521013

162

4

11

16

15

816

17

914

3

59

17

A

B

C

2

410

15

21013

16

2

4

11

16

15

816

17

914

3

59

17

MapConstruction

A

B C

10

14

16

d(A,B) d(B,

C)AUTISM

MULTIPLE SCLEROSIS

Visual cortex

Posterior Cingulate Cortex

Superior Frontal Gyrus

Hippocampus

Medial temporal Gyrus

Enthorinal cortex

Microarrays from laser microdissected neurons

CONTROL

ACETAMINOPHEN (100mg/Kg)

6h

24h

3d

7d

ACETAMINOPHEN (1250mg/Kg)

High Dose 6 Hours

High Dose 24 Hours

High Dose 3 Days

Low Dose 6 Hours

Control

mRNA from liver in rats

GSE32891 (FDA, Je erson, AR) mRNA from L signature size: 50+50, top 20% edges

GSE37772 mRNA from lymphoblast cell lines derived from 386 individuals of 196 Simons Simplex Families Signature size: 25+25, top 20% edges

GSE37772 mRNA from lymphoblast cell lines derived from 386 individuals of 196 Simons Simplex Families Signature size: 25+25, top 20% edges

Main prizes

27 February 2009COSBI wins the competition Formal Methods for MolecularBiology with 22 participants

30 November 2010The President of the Italian Republic Napolitano appointsCOSBI with a medal for its results in the first 5 years of activity

2 October 2012COSBI wins second place over 52 participants in theinternational competition SBVImprover in Boston to determine biomarkers. COSBI is first in the sub-competition on multiple sclerosis

11-13 June 2014COSBI wins the first prize atSIBBM 2014 for its studies onneurodegenerative dementia


Main events and seminars

COSBI integrates multiple data types to correlateneurodegenerative diseases.

Similar methods have been used to identify Alzheimer’s biomarkers.

Information theory is used to determine the activity levelof biological networks.

Alzheimer relevant genes

Network analysis

mRNA expression

SNPs

Module genes

Modules in HPRD PPI network

GO terms(Biological Processes)

Functional annotation analysis

Identification of AD candidate genes and/or Biomarkers

Significantly enriched modules

Drug targets

OMIM genes

Inputs Processing Output

Identify modulesconnecting gene sets and receptors/transporters

Gene set

Interaction network

List of receptors/transporters

DEGs Fold change of DEGs

Pruning sub-network between selected receptors/transportersand gene set products

Topology

Node activity score and cellular functions+ p-value

Networkactivity score

Information theory-basedcomputation of node activity levels

Disease genes

Network reconstructionProtein-ProiteinInteraction network Shared node identification

GO terms Biological ProcessesAnalysus of specific GO term-associated genes

Network analysis FunctionalannotationanalysisSignificantly

enriched modules

OMIM genesHuntington, Prion, Frontotemporal dementia, Alzheimer’s, ALS, Friedreich ataxia, Lewy BD, Parkinson, SMA,Glioblastoma

follows>

Converging Sciences 2006

Biology without Borders 2007

BioComplex 2008

Merging Knowledge 2010

7 December 2005Leroy HoodSeminar

17 February 2011Larry WallSeminar


COSBI is setting the state of the art in modeling languages and simulation algorithms.

Languages

COSBI in Scientific International Boards

Scientific boards: Fondazione Veronesi, EU ISTAG - Future and Emerging Tecnologies Unit, CRUI - programmi europei, MT-LAB.Review panels: MIUR, EU-IST FET, Ireland Science Foundation, Genome Canada, BBSRC, The Royal Society UK, European Research Council, Medical Research Council UK, Council of Earth and Life Sciences NL, BMBF D, The Netherlands Organisation for Research, Czech Science Foundation, Research Council of Lithuania, Austrian Science Fund.

RSSA: a new faster, exact simulation algorithm

Case

Stud

ies

Implementation

Stochasticpi-calculus1995

Beta binders2004

BlenX2008

L2012

COSBI is devoting a great deal of effort in making simulation accessible through user-friendly graphical interface and minimal languages.

SHMT

H2O

H+

NADP+

NADP+

NADPH

NADPH

NADP+

NADPH

DHFR

TYMS

THF

10f-THF

Formate

5,10 CH=THF

5,10 CH2-THFDHF

5m-THF

Met

SAM

SAH

Hcy

Methylation

Sumoylation

MTHFD1(FTHFS)

MTHFR

MTR

ADP+Pi

ATP

dUMP

Glycine

Serine

Purinesynthesis

CYTOSOLNUCLEUS

MTHFD1(MTHFC)

MTHFD1(MTHFD)

dTMP

sumoSHMT

NADP+

NADPH sumoDHFR

5,10 CH2-THF

THF

DHF

dUMP

Glycine

Serine

dTMP

Thymidylate Biosynthesis

sumoTYMS

[steps = 5000, delta = 0.2]let CYCBT: bproc = #(x,CYCBT)[ nil ];when(CYCBT:: d_dtCYCBT_1) new(1); when(CYCBT:: d_dtCYCBT_2) delete(1);when(CYCBT:: d_dtCYCBT_3) delete(1); when(CYCBT:: d_dtCYCBT_4) delete(1);let CDH1: bproc = #(y,CDH1)[ nil ]; let CDH1_IN : bproc = #(y_in,CDH1_IN) [ nil ];when(CDH1_IN :: d_dtCDH1_1 ) split(Nil, CDH1); when(CDH1_IN :: d_dtCDH1_2 ) split(Nil, CDH1);when(CDH1 :: d_dtCDH1_3 ) split(Nil, CDH1_IN); when(CDH1 :: d_dtCDH1_4 ) split(Nil, CDH1_IN);let CDC20_IN : bproc = #(a,CDC20_IN)[ nil ]; let CDC20_A : bproc = #(a,CDC20_A)[ nil ];when(CDC20_IN :: d_dtCDC20_IN_1 ) new(1); when(CDC20_IN :: d_dtCDC20_IN_2 ) new(1);when(CDC20_IN :: d_dtCDC20_IN_5 ) delete(1); when(CDC20_IN :: d_dtCDC20_IN_4 ) split(Nil, CDC20_A);when(CDC20_A :: d_dtCDC20_A_2) split(Nil,CDC20_IN); when(CDC20_A :: d_dtCDC20_A_3) delete(1);

A+B C

C+D

E G

G+H-E

L

-F

E

O43680

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Syndrome of “a new symbol”+

Better = New functionalities

COSBI Style

4 November 2014Pier Giuseppe PelicciSeminar

ECEM 2011

SAC 2012

< Main events and seminars


Institutionalvisits

13 November 2006Minister Fabio Mussi

2 April 2008United States Ambassador in Italy Ronald Spogli

19 May 2009President of PAT Lorenzo Dellai

COSBI develops software prototypes to apply the analytical methods that were designed and implemented for ad hoc biological problems.

COSBI has also developed COSBI LAB, a professional environment to model and simulate molecular biological processes.

Software prototypes

SCUDO is a tool for clustering gene expression profiles for diagnostic purposes using a new type of rank-based signatures

SCUDOL

An imperative, domain specific language to stochastically simulate biological systems

NASFinder

The Network Activity Score Finder is a web service for topological and functional analysis of sub-networks connecting an omics-determined module

SICOMPRE

Simulation-based, qualitatitve and quantitative prediction of protein complexes

BioNetMotion

BioNetMotion provides dynamic and network-based visualization of time course omic data

GENER

Gener is a tool for performing reductions on DNA-strands based on a strand-displacement algebra

LIME

Language Interface for individual-based modeling of ecosystem dynamics

WALDO

Waldo Reaction-based tool for easily modeling and simulating biological systems

BETAWB

BlenX-based tools to represent and simulate biological entities and their interactions

KINFER

Estimates both structural and nuisance model parameters from time-series data of reagents abundance

REDI

Simulates non-homogeneous and anisotropic stochastic di�usion of molecules

RSSA

Rejection-based Stochastic Simulation

COSBI LAB MODEL

estSestS

COMPONENT SITE

EST estSi

ERa

COSBI LAB SIMULATION

COSBI LAB PLOT

COSBI LAB GRAPH

COSBI LAB PLOT MATRIX

MOdEL SIMuLatIOn PLOt GraPh PLOt MatrIx


Corporate visits

Main customers

People

technology transfer

7 December 2005 and5 June 2009Rick Rashid, SVP Microsoft Research

Nestlé Institute of Health SciencesPurinaUniversità di VeronaGlaxoSmithKlineSanofiUniversità di ParmaIEOAutifonySomaLogic

Avg age: 32Countries: 10Disciplines:Biology,ComputerScience,Bioinformatics,Mathematics,Ecology,Statistics,Engineering,Bioengineering

13 September 2007and 15 March 2013Tony Hey, VP Microsoft Research Connections

5 October 2012Ed Baetge, CEO Nestlé Institute of Health Sciences

COSBI refocused its activities from 2011 to be selfsustained by offering added-value scientific services in the fields of data analysis, modeling and simulation of biological process both to food and pharma industries and academic institutes and groups. This reorganization allowed COSBI to cover about 70% of its costs with commercial services and to change the structure of itsincome considerably.

0%

17,5%

35%

52,5%

70%

2010 2011 2012 2013 2014 2015

Public funds Academic projectsCommercial income Other income

the network of people grown at COSBI

Virginia Tech, University of Exeter, INRIA Paris, Universitad EAN, University of Lille, INRIA Rennes, RIKEN institute, University of Aalborg, Ecole Centrale Paris, Roche Diagnostics, Centro National de Biotecnologia Madrid, Navionics, University of Bolzano, Università di Trento, Fondazione Edmund Mach, University College London, Bax Energy, IMT Lucca, Accenture, ETH Zurich, University of Cincinnati, Università di Milano Bicocca, Trento RISE, Skype, SMC.


Contract research and servicesSystems pharmacology - Systems nutrition

1 biomarkers

these methods identify biological signatures that characterize a phenotype to classify samples. Biomarkers are diagnostic or prognostic, determine optimal groups in clinical trials, and drive the next steps in the pipeline

2 network selection

phenotypes or diseases select the gene, protein, metabolic, drug, microbiome, mixed network for the analysis

3 network analysis

omics and clinical data are used with topological indexes to identify modules of the network most significantly associated with the phenotype

4 functional analysis

omics, clinical data, knowledge from literature and DBs is used to identify the processes of themodules from step 3

5 simulation

modules are mapped into executable representations of the dynamics of the system to run virtual experiments

Each step of the pipeline produces significative results and it can be performed in isolation. The pipeline can start from each step and can continue until the desired results are obtained.

Each step of the pipeline applies a combination of proprietary methods and public methods to maximize the results. COSBI methods are designed ad hoc for the customer’s problems and are always equipped with software prototypes to run them and orchestrate the integration with public software. All results of each pipeline step are biologically interpreted at COSBI.

Continuous interaction with customers in each step of the analysis ensures timely and valuable results.

COSBI has developed modeling languages and simulation algorithms that currently set the state of the art worldwide.

COSBI is not for-profit, but it is completely self-funded. Income from services covers salaries, IT infrastructure and minimal overhead.

Complete confidentiality and data security is ensured. State of the art security IT infrastructure and protocolsare adopted and only COSBI researchers that run the analyses can access thecustomer’s data.

COSBI integrates multiomics,multilevel and clinical data setswith diet, lifestyle and scientificliterature.


2 network selection

1 biomarkers

INPUT

phenotype, omics andclinical data, the biomarkers from step 1 for driving the selection of the backgroundbiological network

INPUT

transcriptomics, proteomics orany data set that can be ordered or measured and the phenotype of interest

COSBI defines the backgroundbiological network from the phenotype description andthe raw data provided by thecustomer.

COSBI performs the analysisstarting from raw data andphenotype description. Theresult is the set of patients’signature, the similaritynetwork and the consensussignature per luster ofpatients.

METHOD

the phenotype and the data available determine the components of the biological process of interest (genes,proteins, metabolites, drugs, nutrients, lipids, drugs), their localization, the reference tissues and organs. An ad hocnetwork is built by integrating the data provided by the customer and public knowledge from DBs and literature

METHOD

data are ordered for each patient and a signature is made up to the upper and lower elements of the ordered list (e.g. most and least expressed genes). A distance is definedbetween each pair of signatures and it is visualized on a network of patients with the length of the arcs proportional to the distance of the signatures. The closer the patients, the more similar, andthe visualization produces clusters of individuals (e.g.,health vs disease, responder vs non responder). Classical methods are applied for comparison

OUTCOME

a biological network withhighlighted biomarkers (genes, proteins, etc.) and the experimental data (e.g., mostand least expressed genes). A report with the literature references, DBs and methodology adopted is always provided

OUTCOME

a network of patientsclustered according to thephenotype. New patients can be mapped onto the networkaccording to their signatureto determine the cluster theyenter in. The correspondingstratification of patients can beused for diagnosis or prognosis, optimal selectionof cohorts for clinical trials,toxicology studies, etc.

-4 -2 0 2 4

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glucoseIntolerant

normalnormaldiabetic

diabetic

normal

diabetic

glucoseIntolerant

normal

diabetic

diabeticnormal

normal

glucoseIntolerant

diabetic

normal

diabetic

diabeticdiabetic

glucoseIntolerant


diabetic

diabetic

normal

diabeticdiabetic

diabetic

diabetic

diabetic

glucoseIntolerant

normalglucoseIntolerant

diabetic

diabetic

diabetic

glucoseIntolerant

diabeticnormal

diabeticdiabetic

diabetic

diabetic

normal

diabetic

normalnormal

normalglucoseIntolerantnormal glucoseIntolerant

normal


diabetic

normal

normal

normalnormalglucoseIntolerant

normal

normal

diabetic

normal

normalnormal

normal

glucoseIntolerant

normal

glucoseIntolerant

diabetic

normal

diabetic

normal

glucoseIntolerant

glucoseIntolerant

normal

normal

normal

glucoseIntolerant

diabetic

glucoseIntolerant

normalnormal

(A) (B)


3 network analysis

INPUT

background biological network from step 2 and available experimental data

COSBI defines the mostsignificant network modules for the phenotype and produces a report of the biological interpretation with new hypotheses for new experiments

METHOD

the biological network is studied according to topological indexes and the mapping of the experimentaldata to determine the importance of the nodes.Enrichment analysis with respect to the data available is used to define the modules. nformation theory is applied toinvestigate the transmission capacity of cascades andscore them accordingly to select the dominant pathways

OUTCOME

a set of modules (usually different from classicalpathways as they aredetermined by experimentaldata) and measures of statistical significance, activity level, sensitivity, robustness. The most topologically relevant nodes for each module are identified with respect to the phenotype of interest

4 functional analysis

INPUT

network modules from step 3 and phenotype of interest

COSBI produces a biologicalinterpretation of the modules and suggests possible targetsor modulators of the biologicalprocesses and new experiments to elucidate molecular mechanisms

METHOD

modules from step 3 are tuned to the phenotype via enrichment analysis with respect to the functions of their components. Literature mining is applied to compare our analysis results with the biological knowledge in the literature and DBs. The primarymetabolic and signaling cascades are identified andassociated with biological functions

OUTCOME

a report with metrics of the module for the statistical significance, coverage of known pathways and biological interpretation of the enrichment analysis. The modules are the parts of the network where to look fortargets and modulators or where to investigate to better understand molecular mechanisms

NOTCH1 Intracellular Domain Regulates TranscriptionSignaling by NOTCH1 PEST Domain Mutants in Cancer

Signaling by NOTCH1 HD+PEST Domain Mutants in CancerSignaling by NOTCH1 in Cancer

Signaling by NOTCH1 HD Domain Mutants in CancerFBXW7 Mutants and NOTCH1 in Cancer

Signaling by NOTCH1Signaling by NOTCH1 t(79)(NOTCH1:M1580_K2555) Translocation Mutant

Constitutive Signaling by NOTCH1 PEST Domain MutantsConstitutive Signaling by NOTCH1 HD+PEST Domain MutantsTranscriptional Regulation of White Adipocyte Di erentiation

Fatty acid, triacylglycerol, and ketone body metabolismRegulation of Lipid Metabolism by Peroxisome proliferator-activated receptor alpha

PPARA Activates Gene ExpressionRORA Activates Circadian Expression

Circadian Repression of Expression by REV-ERBACircadian Clock

BMAL1:CLOCK/NPAS2 Activates Circadian ExpressionYAP1- and WWTR1 (TAZ)-stimulated gene expression

Regulation of Cholesterol Biosynthesis by SREBP (SREBF)Activation of Gene Expression by SREBP (SREBF)

Metabolism of lipids and lipoproteinsDevelopmental Biology

Generic Transcription PathwayNuclear Receptor transcription pathway

Bile acid and bile salt metabolismalpha-linolenic acid (ALA) metabolism

alpha-linolenic (omega3) and linoleic (omega6) acid metabolismCD28 dependent PI3K/Akt signaling

Release of eIF4ES6K1-mediated signalling

mTORC1-mediated signallingRecycling of eIF2:GDP

LDL-mediated lipid transportScavenging by Class B Receptors

HDL-mediated lipid transportRetinoid metabolism and transport

Chylomicron-mediated lipid transportLipid digestion, mobilization, and transport

Lipoprotein metabolismTriglyceride Biosynthesis

Synthesis of very long-chain fatty acyl-CoAsFatty Acyl-CoA Biosynthesis

Cell Cycle, MitoticCell Cycle

G2/M TransitionMitotic G2-G2/M phases

Regulation of PLK1 Activity at G2/M TransitionRecruitment of mitotic centrosome proteins and complexes

Centrosome maturationLoss of Nlp from mitotic centrosomes

AMPK inhibits chREBP transcriptional activation activityImport of palmitoyl-CoA into the mitochondrial matrix

Signaling by Insulin receptorPI3K Cascade

IRS-related events triggered by IGF1RIGF1R signaling cascade

Signaling by Type 1 Insulin-like Growth Factor 1 Receptor (IGF1R)Insulin receptor signalling cascade

IRS-mediated signallingIRS-related events

Regulation of Rheb GTPase activity by AMPKPKB-mediated events

mTOR signallingRegulation of AMPK activity via LKB1

Energy dependent regulation of mTOR by LKB1-AMPK

! "! #! $!

Loss of proteins required for interphase microtubule organization

number of pathway genes in module

25

50

75

100

% pathwaycoverage


5 simulation

INPUT

relevant modules for the phenotype from step 4 and hypothesis to be verified

COSBI produces the executable model and runsthe simulation corresponding to the virtual experiment

METHOD

modules are represented in a graphical language suitable to interact with biologists and to simulate (either deterministically or stochastically) the dynamics of the biological process. After parameter inference and model calibration, perturbation experiments are performed in silico to verify the hypotheses

DATA TYPES

OMICS (all platforms)genetics, SNPs, genomics, proteomics, transcriptomics, metabolomics, lipidomics, microbiome

CLINICALblood markers, urine and saliva markers, tissue markers, diet, physical activity

BIOCHEMICALkinetic parameters and reaction rates, affinity, active domains and binding sites

LITERATUREmining and search to acquire the knowledge needed to optimize the pipeline and drive the analysis especially in the step of network selection and to assess the results

DATA BASEScomparison, integration and validation of the analyses through public data collections

METHODS

DATA ANALYSISstandard and multivariate statistics, data exploration, aggregation and visualization, clustering, functional and topological network analysis, dimensionality reduction, annotation, data and literature mining, machine learning, proprietary methods defined ad hoc for specific biological problems, sensitivity and robustness analysis

MODEL REPRESENTATIONpublic and proprietary graphical languages, domain-specific and general purpose programming languages, reaction-based modeling, agent-based modeling, differential equations, boolean networks, Petri nets, rewriting systems

SIMULATION ALGORITHMSdeterministic, stochastic and hybrid algorithms, non parametric simulation, incomplete model simulation, proprietary algorithms to identify dominant pathways

OUTCOME

executable model of thebiological processes ofinterest and biological interpretation of the virtual experiments; preliminary validation through literature search.Elucidation of the mechanisms of action, design of new experiments


Main studies

Cofactor network. Integration of data from multiple sources in order to build a comprehensive network linking cofactors, cofactor-requiring enzymes, human diseases and biological processes. Study of the influence ofbetween-population genetic differences on polymorphic distribution in cofactor-requiring genes

Assessment of CVD risk. Identification of healthy individuals at low (controls) and high-CV (cases) risk based on fasting proteomic signature of data and of the genetic factors involved in ischemic stroke predisposition

Modeling the response of small intestine to dietary fat intake. Identification of genes exhibiting significant linear or nonlinear response to dietary fat doses. Identification of dietary fat responsive metabolic and transport processes that are commonly enriched from the proximal to distal sections; commonly affected intestinal segments; Predominant transcriptomic response patterns

Compound-affected differentiation. Cell differentiation and comparative differences among different dosages of compounds over multiple time points from metabolomics andproteomics

Methods

data and literature mining,network analysis (moduleidentification, centrality, hubidentification, dominator tree),permutation test, functionalenrichment analysis andbiological interpretation, FSTindex, data visualization

functional enrichment analysis and biological interpretation, clustering, rank-based signatures, network analysis, genetic algorithms, PCA, casecontrol association analyses (chi-square and logistic regression) using genomewide SNP, data visualization

nonlinear regression of transcriptomic data, weighted co-expression network analysis, hypergeometric test of functional enrichmentanalysis, biologicalinterpretation

integrative multiomic analysis,statistical indices, normalization and variance stabilization, differential analyses, co-expression analysis, data mining, network analysis, dominant pathway identification, functional enrichment analysis and biological interpretation, data visualization

man/ months*

6-10

10-14

10-14

10-14


Main studies

Response to metabolic challenges. Identification of proteins and pathways that are differentially expressed after a metabolic challenge. Identification of genetic and proteomic markers predictive of metabolic challenge response and development of a systems view of these responses

Metabolic flexibility and metabolic phenotypes. Different measures of fat and carbohydrate oxidation, and individual levels of severalinflammatory markers in lipid and glucose oral tolerance tests (OLTT and OGTT). Investigation on gene expression patterns in OLTT and OGTTand inflammatory patterns. Investigation of the effect of diet to predict of the levels of someinflammatory markers

SNPs predicting diabetes phenotype. Investigation on groups of SNPs to see if they have a predictive role on several diabetes related phenotypes on a wide (~800) cohort of diabetic subjects

Extreme phenotypes of diabetic patients. Gene expression microarray data analysis in extreme phenotypes of 148 diabetic subjects (10 most insulin resistant and 10 most insulin sensitive)

Systemic response to food intake of T1 diabetic patients. Development of a mathematical model of mixed meal in type 1 diabetes

Methods

integrative multiomic analysiscovariate analysis, robustlinear regression, multiplecorrection testing, functionalenrichment analysis andbiological interpretation, dataand literature mining, GWAS,pQTL, robust sparse k-meansclustering, rank-basedsignatures, genetic algorithms,data visualization

integrative multiomic analysis ,GWAS, pathway identificationand biological interpretation,data visualization, multipleregression models, canonicalcorrelation analysis, mixomics,PMA, proprietary algorithms,gene enrichment analysis,network analysis

normalization, random forest,data and literature mining,biological interpretation, dataexploration and visualization

differential analysis, probemapping, gene enrichmentanalysis and interpretation,rank based signature, geneticalgorithms, data and literaturemining, data visualization

data and literature mining,ODE, simulation algorithms,virtual experiments, datavisualization and biologicalinterpretation

man/ months*

8-12

10-14

3-6

3-6

8-12


Main studies

Role of leptin in food intake and energy metabolism. Development of a mathematical model of leptin dynamics with parameters derived from published experimental data

Sphingolipid metabolism. Network selection, module identification and development of a mathematical model of sphingolipid dynamics from literature knowledge and experimental data

EGFR internalization. Development of a mathematical model of the molecular mechanisms driving internalization of the EGF receptor in HeLa cells from literature and experimental data

Inference of cancer gene essentiality from genomic data. Characterization of cancer cell lines with biomarkers for tailored treatments and patients with higher treatment efficacy

Neurodegenerative dementia. Investigation of the molecular connections between complexdiseases with the shared clinical symptoms of dementia

Methods

data and literature mining,ODE, simulation algorithms,virtual experiments, datavisualization and biologicalinterpretation

data and literature mining,interaction network selection,differential analysis,enrichment analysis,functional analysis, moduleidentification, ODE and ad hocprogramming languages,simulation algorithms, virtualexperiments, datavisualization and biologicalinterpretation

data and literature mining,ODE and stochastic models,proprietary language,simulation algorithms, virtualexperiments, fitting, modelcalibration

integrative multiomic analysis,support vector regression (e-SVR), clustering, data andliterature mining, differentialanalysis, network analysis,biological interpretation, datavisualization

multi source data integration,literature and data mining,machine learning, networkanalysis, rank-basedsignatures, genetic algorithms,biological interpretation, datavisualization

man/ months*

8-12

10-14

10-14

4-6

6-8


Main studies

Novel dementia drug targets. A multi-relational association mining approach to predict targets for innovative therapeutic treatment ofdementia

Alzheimer’s and diabetes crossdiseaseanalysis. Integration of transcriptomic data from Alzheimer’s and diabetes post mortem brains toassess commonalities between two highly co-morbid diseases

Role of APOE4 in Alzheimer’s.Analysis of the role of one of the genetic predisposing genetic factors (APOE4) of Alzheimer’s

A multi-factor network analysis onAlzheimer’s. Integration of the genomic aspect of AD with the gene expression and drug candidate targets for the understanding of disease pathophysiology

Neurological diagnostic biomarkers.Early diagnosis of neurological disorders (autism, Parkinson’s) using gene expression profiles

Toxicology. Diagnosis of hepatotoxicity using gene expression profiles

Methods

multi relational mining, dataand literature mining, dataintegration and exploration,statistical indices, networkanalysis, biologicalinterpretation, datavisualization

differential analysis, data andliterature mining, networkanalysis, rank-based signature,enrichment analysis,functional analysis, moduleidentification, datavisualization and biologicalinterpretation

integrative multiomic analysis,data and literature mining,network analysis, rank-basedsignature, enrichment analysis,functional analysis, datavisualization and biologicalinterpretation

multi source data integration,data and literature mining,network analysis, enrichmentanalysis and biologicalinterpretation, datavisualization, machine learning

data normalization andfiltering, rank-based signature,genetic algorithms, data andliterature mining, pathwayanalysis and interpretation

data normalization andfiltering, rank-based signature,genetic algorithms, pathwayanalysis and interpretation

man/ months*

6-8

8-12

8-10

4-6

4-6

4-6


the Delta Obesity Vitamin Study. Genes & Nutrition, 9(3):403, 2014.15. J. Kaput, B. Van Ommen, B. Kremer, C. Priami, J. Pontes Monteiro, M.J. Morine,

et al. Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life. Genes & Nutrition, 9:1-9, 2014.

16. M.J. Morine, J. Pontes Monteiro, C. Wise, C. Teitel, L. Pence, A. Williams, B. Ning, B. McCabe-Sellers, C. Champagne, J. Turner, B. Shelby, M. Bogle, R.D. Beger, C. Priami, J. Kaput. Genetic associations with micronutrient levels identified in immune and gastrointestinal networks. Genes & Nutrition, 9(4):408, 2013.

17. R. De Cegli, S. Iacobacci, G. Flore, G. Gambardella, L. Mao, L. Cutillo, M. Lauria, J. Klose, E. Illingworth, S. Banfi, D. di Bernardo. Reverse engineering a mouse embryonic stem cell-specific transcriptional network reveals a new modulator of neuronal differentiation. Nucleic Acids Res, 41:711-26, 2013.

18. M. Scotti, L. Stella, E. Shearer, P. Stover. Modeling cellular compartmentation in one- carbon metabolism. WIREs Systems Biology and Medicine, 5:343-365, 2013.

19. 13. J. Dodgson, A. Chessel, M. Yamamoto, F. Vaggi, S. Cox, E. Rosten, D. Albrecht, M. Geymonat, A. Csikasz-Nagy, M. Sato, R. E. Carazo-Salas. Spatial segregation of polarity factors into distinct cortical clusters is required for cell polarity control. Nature Communications, 2013.

20. L. Caberlotto, M. Lauria, P. Nguyen, M. Scotti. The central role of AMP-kinase and energy homeostasis impairment in Alzheimer’s disease: a multifactor network analysis. PloS ONE, 8:e78919, 2013.

21. A. Tarca, M. Lauria, M. Unger, E. Bilal, S. Boue, K. Dey, J. Hoeng, H. Koeppl, F. Martin, P. Meyer, P. Nandy, R. Norel, M. Peitsch, J. Rice, R. Romero, G. Stolovitzky, M. Talikka, Y. Xiang, C. Zechne. Strengths and limitations of microarray-based phenotype prediction: Lessons learned from the IMPROVER Diagnostic Signature Challenge. Bioinformatics, 29:2892-9, 2013.

22. M. Lauria. Rank-based transcriptional signatures: A novel approach to diagnostic biomarker definition and analysis. Systems Biomedicine, 1:0-10, 2013.

23. O. Kahramanogullari, J. Lynch. Stochastic Flux Analysis of Chemical Reaction Networks. BMC Systems Biology, 7:133, 2013.

24. K. Martin, M.J. Morine, J. Hager, B. Sonderegger, J. Kaput. Perspective: a systems approach to diabetes research. Front Genet, 4:205, 2013.

25. C. O’Grada, M.J. Morine, C. Morris, M. Ryan, E. Dillon, M. Walsh, E. Gibney, L. Brennan, M. Gibney, H. Roche. PBMCs reflect the immune component of the WAT transcriptome - Implications as biomarkers of metabolic health in the postprandial state. Molecular nutrition & food research, 2013.

26. J. Kaput, M.J. Morine. Discovery-Based Nutritional Systems Biology: Developing N-of-1 Nutrigenomic Research. Int J Vitam Nutr Res, 82(5):333-41, 2012.

1. M. Lauria, P. Moyseos, C. Priami. SCUDO: a tool for signature-based clustering of expression profiles. Nucleic Acid Research, 2015.

2. H. Vo Thanh, R. Zunino, C. Priami. On the Rejection-based Algorithm for Simulation and Analysis of Large-Scale Reaction Networks. Journal of Chemical Physics, 142:244106, 2015.

3. H. Vo Thanh, C. Priami. Simulation of Biochemical Reactions with Time- Dependent Rates by the Rejection-based Algorithm. Journal of Chemical Physics, 143, 2015.

4. T.-P. Nguyen, C. Priami, L. Caberlotto. Novel Drug Target Identification for the Treatment of Dementia Using Multi-Relational Association Mining. Nat. Sci. Rep., 5:11104, 2015.

5. F. Capuani, A. Conte, E. Argenzio, L. Marchetti, C. Priami, S. Polo, P.P. Di Fiore, S. Sigismund, A. Ciliberto. Quantitative analysis reveals how EGFR activation and downregulation are coupled in normal but not in cancer cells. Nat. Comm., 2015.

6. S. Rizzetto, C. Priami, A. Csikász-Nagy. Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations. PLOS Comp. Biol., 2015.

7. O. Finucane, C. Lyons, A. Murphy, C. Reynolds, R. Klinger, N. Healy, A. Cooke, R. Coll, L. McAllan, K. Nilaweera, M. O’Reilly, A. Tierney, M.J. Morine, J. Alcala-Diaz, J. Lopez-Miranda, D. O’Connor, L. O’ Neill, F. McGillicuddy, H. Roche. Monounsaturated fatty acid enriched high fat-diets impede adipose NLRP3 inflammasome mediated IL-1β secretion and insulin resistance despite obesity. Diabetes, 2015.

8. P. Nguyen, L. Caberlotto, M.J. Morine, C. Priami. Network Analysis of Neurodegenerative Disease Highlights a Role of Toll-Like Receptor Signaling. BioMed Research International, 2014:1-16, 2014.

9. L. Caberlotto, P. Nguyen. A Systems Biology investigation of Neurodegenerative Dementia reveals a pivotal role of Autophagy, BMC Systems Biology, 8:65, 2014.

10. L. Caberlotto, M. Lauria. Systems biology meets -omic technologies: novel approaches to biomarker discovery and companion diagnostic development. Expert Review of Molecular Diagnostics, November:1-11, 2014.

11. M. Lauria. Rank-Based miRNA Signatures for Early Cancer Detection. BioMed Research International, Vol. 2014:Article ID 192646, 2014.

12. R. Gostner, B. Baldacci, M.J. Morine, C. Priami. Graphical Modeling Tools for Systems Biology. ACM Computing Surveys, 47(2), 2014.

13. T. Vo, C. Priami, R. Zunino. Efficient Rejection-based Simulation of Biochemical Reactions with Stochastic Noise and Delays. J. Chem. Phys., 141, 2014.

14. J. Pontes Monteiro, C. Wise, M.J. Morine, C. Teitel, L. Pence, A. Williams, B. McCabe-Sellers, C. Champagne, J. Turner, B. Shelby, B. Ning, J. Oguntimein, L.Taylor, T. Toennessen, C. Priami, R.D. Beger, M. Bogle, J. Kaput. Methylation potential associated with diet, genotype, protein, and metabolite levels in

The main methods developed at COSBI in ten years of activities and tested both on industrial projects and classroom at the Uni-versity of Trento are introduced in the book “analysis of biologicalsystems,” Imperial College Press, March 2015.

Imperial College PressImperial College PressP1004 hc

www.icpress.co.uk

ISBN 978-1-78326-687-6

corrado pr iami mel issa j . morine

analys is of biological systems

Modeling is fast becoming fundamental to understanding the processes that define biological systems. High-throughput technologies are producing increasing quantities of data that require an ever-expanding toolset for their e�ective analysis and interpretation. Analysis of high-throughput data in the context of a molecular interaction network is particularly informative as it has the potential to reveal the most relevant network modules with respect to a phenotype or biological process of interest.

Analysis of Biological Systems collects classical material on analysis, modeling and simulation, thereby acting as a unique point of reference. The joint application of statistical techniques to extract knowledge from big data and map it into mechanistic models is a current challenge of the field, and the reader will learn how to build and use models even if they have no computing or math background. An in-depth analysis of the currently available technologies, and a comparison between them, is also included. Unlike other reference books, this in-depth analysis is extended even to the field of language-based modeling. The overall result is an indispensable, self-contained and systematic approach to a rapidly expanding field of science.

analysis of biological systems

priamimorine

analys is of b iological systems

Main recent publications

COSBI Methods Book


27. F. Niola, X. Zhao, D. Singh, A. Castano, R. Sullivan, M. Lauria, H. Nam, Y. Zhuang, R. Benezra, D. di Bernardo, A. Iavarone, A. Lasorella. Id proteins synchronize stemness and anchorage to the niche of neural stem cells. Nature Cell Biology, 14:477-8, 2012.

28. P. Lecca, D. Morpurgo, G. Fantaccini, A. Casagrande, C. Priami. Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics, BMC Systems Biology, 6:51, 2012.

29. O. Kahramanogullari, G. Fantaccini, P. Lecca, D. Morpurgo, C. Priami. Algorithmic Modeling Quantifies the Complementary Contribution of Metabolic Inhibitions to gemcitabine Efficacy. PLoS ONE, 7:12, 2012.

30. P. Nguyen, T. Ho. Detecting Disease Genes Based on Semi-Supervised Learning and Protein-Protein Interaction Networks. Artificial Intelligence in Medicine, 54. 1:63-71, 2012.

31. A. Romanel, L. Jensen, L. Cardelli, A. Csikasz-Nagy. Transcriptional Regulation Is a Major Controller of Cell Cycle Transition Dynamics. PLoS ONE, 7:e29716, 2012.

32. S. Lai, W. Liu, F. Jordan. On the centrality and uniqueness of species from the network perspective. Biology Letters, 8:570-573, 2012.

33. F. Jordan, P. Nguyen, W. Liu. Studying protein-protein interaction networks: a systems view on disease. Briefings in Functional Genomics, 2012.

34. F. Ferrezuelo, N. Colomina, A. Palmisano, E. Garí, C. Gallego, A. Csikasz-Nagy, M. Aldea. The critical size is set at a single-cell level by growth rate to attain homeostasis and adaptation. Nature Communications, 3:1012, 2012.

35. L. Cardelli, A. Csikasz-Nagy. The Cell Cycle switch Computes Approximate Majority. Scientific Reports, 2:656, 2012.

36. P. Lecca, C. Priami. Biological network inference for drug discovery. Drug Discovery Today, 2012.

37. E. Brennan, M.J. Morine, D. Walsh, S. Roxburgh, M. Lindenmeyer, D. Brazil, P. Gaora, H. Roche, D. Sadlier, C. Cohen, C. Godson, F. Martin. Next-generation sequencing identifies TGF-ß1- associated gene expression profiles in renal epithelial cells reiterated in human diabetic nephropathy. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 2012.

38. E. Allott, M.J. Morine, J. Lysaght, S. McGarrigle, C. Donohoe, J. Reynolds, H. Roche, G. Pidgeon. Elevated tumour expression of PAI-1 and SNAI2 in obeseoesophageal adenocarcinoma patients and impact on prognosis. Clinical and Translational Gastroenterology, 2012.

39. C. Reynolds, S. Toomey, R. McBride, J. McMonagle, M.J. Morine, O. Belton, A. Moloney, H. Roche. Divergent effects of a CLA-enriched beef diet on metabolic health in ApoE(-/-) and ob/ob mice. Journal of Nutritional Biochemistry, 2012.

40. M.J. Morine, S. Toomey, F. McGillicuddy, C. Reynolds, K. Power, J. Browne, C. Loscher, K. Mills, H. Roche. Network analysis of adipose tissue gene expression highlights altered metabolic and regulatory transcriptomic activity in high-fat-diet-fed IL-1RI knockout mice. Journal of Nutritional Biochemistry, 2012.

41. N. Gjata , M. Scotti, F. Jordan. The strength of simulated indirect interaction modules in a real food web. Ecological Complexity, 11:160-164, 2012.

42. M. Scotti, N. Gjata , C. Livi, F. Jordan. Dynamical effects of weak trophic interactions in a stochastic food web simulation. Community Ecology, 13:230-237, 2012.

43. F. Jordan, N. Gjata , M. Shu, C. Yule. Simulating food web dynamics along a gradient: quantifying human influence. PLoS ONE, 7:e40280, 2012.

2014.14. Visual Modeling of Biological Systems. Fraunhofer Institute for Algorithms

and Scientific Computing, Sankt Augustin, Germany, Jun 2014.15. A novel approach to systems pharmacology. Drug Discovery Summit,

Geneva, Jun 2014.16. Algorithms for ecological network analysis. 6th SIDEER Symposium, Sede

Boqer Campus of Ben Gurion University, Israel, Mar 201417. Systems biology: a molecular nutrition perspective. Molecular-Med Tri-Con,

San Francisco, Feb 2014.18. Key species and key interactions in food web simulations. University of

Potsdam, Potsdam, Germany, Jan 2014.

19. Key players in ecological networks. Food Webs 2013 Symposium, Giessen, Germany, Nov 2013.

20. Network dynamics: from data to behavior. Merck, New York, Oct 2013.21. Network science as the key for understanding complex problems at different

spatial scales. Academia Sinica, Taipei, Taiwan, Sep 2013.22. Dynamic simulation of Biological Systems. Sanofi, Frankfurt, Feb 2013.23. Bioinformatics at COSBI, Microsoft Italy, Feb 2013.24. Network identification, analysis and simulation, Amgen, Los Angeles, Feb

2013.25. Systems biology: a molecular nutrition perspective. Molecular-Med Tri-Con,

San Francisco, Feb 2013.26. Service-oriented data aggregation, analytics and interactive visualization.

Google, Mountain-View, Jan 2013.

1. Key players in the microbial ecosystems of the human body, Discovery on Target, Boston, Sep 2015.

2. Sphingolipid metabolism and data-driven module detection, Sanofi, Frankfurt, Jul 2015.

3. Computational systems biology applied to pharmacology and nutrition, ISC, Frankfurt, Jul 2015.

4. Systems level understanding of biological processes in nutrition, Nestlé Institute of Health Science, Lausanne, Jul 2015.

5. Ranking omics data for discovering biomarkers, Mol-Med Tri-Conference, San Francisco 1, Feb 2015.

6. The COSBI case. Big Data Leaders Forum, Berlin, Dec 2014.7. Quantitative analysis of biological systems. Energy Biosciences Institute,

University of Berkeley, Sep 2014.8. bSTYLE - a minimal graphical language to model biological systems.

Microsoft, Redmond, Sep 2014.9. Quantitative network analysis of biological systems. SomaLogic, Boulder, Sep

2014.10. Programming languages and biology. Microsoft Research Cambridge, Sep

2014.11. Quantitative pipelines for systems pharmacology. GSK, Stevenage, Sep 2014.12. Identification of cofactor-requiring enzymes with high genetic differentiation

between 1000 Genomes populations. European NuGO week, Castellammare di Stabia, Sep 2014.

13. Are Alzheimer’s disease and neurodegenerative dementia primarily metabolic diseases? A systems biology study. University of Bologna, Jun

Main recent invited talks


Main competitive grants

Where we are

1. MIUR-FIRB, Computational tools for systems biology, 2005-2009.2. PAT, Language-based systems biology, 2006-2009.3. CARITRO, Molecular modelling of polyphenols biosynthetic pathways and its application in gene discovery and gene modulation in living cells for pharmacogenomics purposes, 2007-2009.4. CARIPLO NOBEL, Molecular modeling of gene regulation, transcription and translation, 2007-2011.5. HFSP, Quantitative study of polarised cell growth in vivo and in silico, 2009-2011.6. PAT, Personalized molecular nutrition, 2010-2014.7. EU (Action: Integrated Infrastructure Initiative (I3)) VENUS-C: Virtual multidisciplinary EnviroNments USing Cloud infrastructures, 2010-2012.8. EU (JPI - Infrastructures), ENPADASI - A healthy diet for healthy life. 2014-2016.

Dissemination is fundamental for COSBI to keep its strategic partnerships alive and toincrease its visibility in the scientific and industrial communities.

27. Algorithmic Systems Biology: from omics data set to mechanistic models. HITS, Germany, Jan 2013.

28. Computational and Systems Biology at COSBI. UCB, Brussels, Jan 2013.

29. Algorithmic Systems Biology: from omics data set to mechanistic models, FOSBE 2012, Tokyo, Japan, Oct 2012.

30. Mastering complexity of biological systems through network modularization. BIO-IT World Europe, Vienna, Oct 2012.

31. Computing as Enabling Technology for Systems Biology. SEFM, Greece, Sep 2012.

32. High-throughput analysis of nutritional heath, Atlantic Food and Horticultural Research Centre. Kentville, Canada, Jul 2012.

33. Food web dynamical simulations. FiBL, Frick, Switzerland, Mar 2012.34. Algorithmic systems biology: mastering the complexity of biosystems

without math and computing background. Applications of Systems Biology in Drug Discovery and Development Mini Symposium, Basel and International Conference and Exhibition on Biometrics & Biostatistics, Omaha, USA Mar 2012; Molecular Med Tri-Con, San Francisco, and Pharmaceutica, San Francisco Feb 2012.

>450COSBI sc

ientific papers

1 paper every 6 days

>5500COSBI citations

>1 citation per day 22COSBI innovative S W

>2 per year

scientific events

organized by COSBI26~3 per year

seminarsorganized by COSBI

2891 every 18 days

COSB

I invit

ed to scientific events

>260

1 invit

ation

every 2 weeks

>30 videos

COSBI invited talks

~210 >200h presentations>7500 slides

1 every 18 days

events organized by COSBI5 student competitions

8 school visits

non scientific dissemination

~3 per year, >800 participants

27 8 artistic exhibitions

6 open doors and dissemination

23 stages, 60 in events

16 bachelor theses

32 master theses

6 international master theses

16 PhD theses

1 student every 7 days contacts COSBI

COSBI in education: COSBI theses70

COSBI media clips

1 clip every 2 week 640 24 radio clips 585 press clips

31 TV clips

4,3avg IF

>165% avg world fieldCOSBI research quality

>16,5M>1,6 M per year

COSBI non local funds

COSBI in presti

gious

scientific bodies world

-wide

17

~1 per resea

rcher

COSBI educated people

in worldwide research13 in local research system

34

www.cosbi.eu

COSBI is a bioinformatics research center operating in the ... · annotation analysis...

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