ENFIN An Experimental Network for Functional · PDF fileAn Experimental Network for Functional...
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Project No. LSHG-CT-2005-518254
ENFIN
An Experimental Network for Functional Integration
Instrument: Network of Excellence
Thematic Priority: LSH-2004-1.1.4-1
D7.2
Report on the across-analysis workshop
Due date of deliverable: 15.05.08
Actual submission date: 15.05.08
Start date of project: 13.11.2005 Duration: 60 months
Organisation name of lead contractor for this deliverable: EMBL-BIRNEY
Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006)
Dissemination Level
PU Public X
PP Restricted to other programme participants (including the Commission Services)
RE Restricted to a group specified by the consortium (including the Commission Services)
CO Confidential, only for members of the consortium (including the Commission Services)
Project deliverable: ENFIN
2
Contributors
EMBL-EBI, CNIO, SPRI
INTRODUCTION
D7.2: Report on the across-analysis workshop
To pursue our efforts in assessing computational methods in systems biology, we have organized a workshop to present and discuss
the experience gained within different initiatives such as the ENFIN Consortium, CASP (Critical Assessment of Techniques for
Protein Structure Prediction) and DREAM (Dialogue for Reverse Engineering Assessments and Methods) groups. By far this is not an
exclusive list of groups and we are pursuing the identification of potential partners to share our experience, success and failures with.
Methods
The workshop called “ENFIN-DREAM” was hosted by Alfonso Valencia at the CNIO (Madrid, Spain) on April 28th - 29
th, and the
program Committee included the organizers of the previous DREAM2 meeting, G. Stolovitzky (IBM T.J. Watson Research Center,
New York, USA) and A. Califano (Columbia University Medical Center, New York, USA), as well as ENFIN partners Ioannis
Xenarios (SPRI / Swiss Institute of Bioinformatics), Alfonso Valencia (CNIO) and Pascal Kahlem (EMBL-EBI).
The proceedings of this workshop will be published with those of the DREAM2 meeting in an upcoming volume of the Annals of the
New York Academy of Sciences.
Most of the presentations of the meeting can be found on the ENFIN website, in the section meetings:
http://www.enfin.org/page.php?page=workshops
The program and the list of abstracts can be found in Annex.
DREAM: Dialogue for Reverse Engineering Assessments and Methods. http://wiki.c2b2.columbia.edu/dream/index.php/The_DREAM_Project DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
Results (if applicable, interactions with other workpackages)
ENFIN collaborations of computational predictions and experimental validations were presented, raising a discussion on the technical
limitations of experimental approaches to significantly validate the computational approaches. It was shown that an increase of the
confidence level of the prediction could be gained by the integration of several computational approaches (e.g. mitotic spindle protein
predictions).
Experiences from the DREAM2 challenges were presented by Gustavo Stolovitzky, including the gold standard used to compare the
results for each challenge.
Challenges were aimed at reconstructing gene networks. One of the challenges, for example, had consisted in retrieving the correct set
of genes being targets of the transcription factor BCL6, after adding this set of genes amongst a large number of other genes (namely
decoy genes).
The presentation showed that in most challenges, with a few exceptions, most computational methods had worked rather poorly
(prediction close to random), which raised concerns with regard to the standard use of these methods in research. For confidentiality
reasons, the authors of the methods were not divulgated.
The organizers of the DREAM initiative plan to increase the scope of the challenges by adding new types of data, such as
phosphorylation states or proteomics profiles, and will request the competitors to predict not only qualitative results, but also
quantitative. One additional experimental datasets are measurement of perturbed biological systems, which is similar in approach to
the TGF-beta and the diauxic shift ENFIN modelling projects. This should be closely monitored so that synergies could be identified. An overview of the last CASP challenge was presented by Ana Tramontano. The presentation showed the methods used to assess the predictions of protein structure in comparison to the true structure obtained by crystallography. The last CASP challenged included the possibility to predict, beyond the protein structure, the function of the protein. Unfortunately, the too few predictions per target did not allow deriving any sensible conclusion.
Project deliverable: ENFIN
3
Perspectives To continue the collaboration with the DREAM initiative, ENFIN intends to provide a dataset for the challenge DREAM 2009.
Publications (if applicable)
Proceedings of the meeting will be published in the Annals of the New York Academy of Sciences in the course of this year 2008.
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
ENFIN – DREAM Conference
Assessment of Computational Methods in Systems Biology April 28 – 29, 2008
www.enfin.org
Spanish National Cancer Research Centre (CNIO) (Auditorium)
C/ Melchor Fernández Almagro, 3, E-28029 Madrid + (34) 917 328 000 + (34) 912 246 900
Madrid, Spain
Organisers: Alfonso Valencia - [email protected]
Ana Rojas Mendoza [email protected]
Pascal Kahlem - [email protected] (Mob. +49 15209854747)
Scope:
The European Network of Excellence ENFIN develops infrastructure, tools and methods to enhance
Integrative Systems Biology in Europe. The project addresses three fields of research i) discrete function
prediction, ii) network reconstruction, iii) systems level modeling. One concept of the Network is the
strong collaboration between dry and wet laboratories, which cycle data between computational predictions
and experimental validations.
Although wet experiments are used to validate chosen computational predictions, they often do not allow
assessing the quality of computational methods, because of limited scale, technology and resources.
Because of the growing number of bioinformatics tools available in Systems Biology, strategies are needed
to assess the accuracy of the computational predictions.
In collaboration with the DREAM project, we organize the first European ENFIN-DREAM Conference.
Participants of ENFIN along with other researchers will present strategies to assess methods in the field of
Systems Biology.
ENFIN: Experimental Network for Functional Integration. http://www.enfin.org
DREAM: Dialogue for Reverse Engineering Assessments and Methods.
http://wiki.c2b2.columbia.edu/dream/index.php/The_DREAM_Project
Important dates:
Jan 31, 2008: Papers submission deadline
March 1, 2008: Papers acceptance issued
March 15, 2008: Registration deadline
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
Speakers
EMBL-European Bioinformatics Institute, Hinxton, UK
Ewan Birney ([email protected]) Technical University of Denmark, Lyngby, Denmark
Soren Brunak ([email protected])
University College London, London, UK
Christine Orengo ([email protected]) QureTec, Tartu, Estonia
Jaak Vilo ([email protected])
CERTH, Thessaloniki, Greece
Christos Ouzounis ([email protected]) Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
Ioannis Xenarios ([email protected])
Genoscope, Evry, France
Vincent Schachter ([email protected]) Spanish National Cancer Research Centre, Madrid, Spain
Alfonso Valencia ([email protected])
IBM T.J. Watson Research Center, New York, USA
Gustavo A. Stolovitzky ([email protected]) Columbia University Medical Center, New York, USA
Andrea Califano ([email protected]) Telethon Institute of Genetics and Medicine (TIGEM), Naples, Italy
Diego di Bernardo ([email protected])
University of Rome "La Sapienza" - Department of Biochemical Sciences "Rossi Fanelli", Italy
Anna Tramontano ([email protected])
Department Biochemistry & Molecular Biology (Procel lab), University of Malaga, Spain
Ian Morilla ([email protected])
University of Padova, Italy
Alberto Corradin ([email protected])
INSERM ERM 206, Marseille, France
Denis Thieffry ([email protected])
Programme Committee
Gustavo A. Stolovitzky ([email protected])
Andrea Califano ([email protected])
Alfonso Valencia ([email protected]) Ioannis Xenarios ([email protected])
Pascal Kahlem ([email protected])
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
Day 1: April 28 12:00 – 13:00 Arrivals - Registrations
13:00 – 13:30 E. Birney: Introduction
SESSION 1: Protein function prediction
13:30 – 14:00 C. Orengo “Gene3D: Integrating complex data to reveal protein networks”
14:00 – 14:30 I. Morilla Dominguez “Biomathematical improvement of protein high-throughput
functional prediction”
14:30 – 15:00 V. Schächter "Assessment of metabolic models"
Coffee Break
SESSION 2: Network reconstruction
15:30 – 16:00 J. Vilo “Usage of gene expression data for pathway reconstruction”
16:00 – 16:30 A. Corradin “In silico assessment of four reverse engineering algorithms: role of network
complexity and multi-experiment design in network reconstruction and hub detection”
16:30 – 17:00 C. Ouzounis "Evolutionary analysis of biological pathways: implications for curation and
inference"
Coffee Break
SESSION 3: Systems-level modeling
17:30 – 18:00 I. Xenarios “A qualitative modeling approach of TNF-alpha / TGF-beta regulatory
network: Advantages and Limitations”
18:00 – 18:30 D. Thieffry “Logical modelling and analysis of biological regulatory networks:
identification of stable states and feedback circuit analysis”
18:30 – 19:00 A. Valencia "Text mining and assessment of computational methods in Systems Biology"
Day 2: April 29
SESSION 4: Learning from DREAM and CASP
9:00 – 9:30 Andrea Califano “Integrated, biochemically validated molecular interaction networks
reveal master regulators of human malignancies”
9:30 – 10:00 Gustavo Stolovitzky “Dialogue on Reverse Engineering Assessment and Methods: the
DREAM of high throughput pathway inference”
10:00 – 10:30 Diego Di Bernardo “Reverse engineering gene network in genetic diseased and drug
discovery”
Coffee Break
11:00 – 11:30 Anna Tramontano “The CASP experiment: opportunities and pitfalls”
11:30 – 12:00 S. Brunak “Prediction of Protein Categories from Sequence“
12:00 End of meeting
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
Abstracts
Gustavo Stolovitzky
“Dialogue on Reverse Engineering Assessment and Methods: the DREAM of high throughput pathway
inference. “
The biotechnological advances of the last decade have confronted us with an explosion data that need to be
organized and structured before they may provide a coherent biological picture. To accomplish this task,
the availability of an accurate map of the physical interactions in the cell that are responsible for cellular
behavior and function would be exceedingly helpful, as these data are ultimately the result of such
molecular interactions. However, all we have at this time is partially correct representation of the
interactions between genes, their byproducts, and other cellular entities. DREAM, the Dialogue on Reverse
Engineering Assessment and Methods, is fostering a concerted effort by computational and experimental
biologists to understand the limitations and enhance the strengths of the efforts to reverse engineering
cellular networks from high throughput data. In this talk I will discuss the salient arguments of the recent
DREAM2 conference, where we challenged the community to blindly infer networks known to the
organizers from high throughput data. I will highlight the strategies that have achieved the better inference
results and discuss the state of the art in Reverse Engineering, as well as some of the challenges and
opportunities awaiting us.
Anna Tramontano
“The CASP experiment: opportunities and pitfalls”
In 1994, John Moult proposed a world-wide experiment named CASP aimed at establishing the current
state of the art in protein structure prediction, identifying what progress has been made, and highlighting
where future effort may be most productively focused.
Experimental structural biologists who are about to solve a protein structure are asked to make the
sequence of the protein available, together with a tentative date for the release of the final coordinates. In
the recent past, structural genomics consortia have significantly contributed to the set of CASP targets.
Predictors produce and deposit models for these proteins before the structures are made public. Finally, a
panel of three assessors compares the models with the structures as soon as they are available and tries to
evaluate the quality of the models and to draw some conclusions about the state of the art of the different
methods. The experiment is run blindly, that is, the assessors do not know who the predictors are until the
very end of the experiment.
Each of the routes to the prediction of a protein structure commonly used has traditionally been mirrored by
a CASP “category”, evaluated by one of the three assessors. The results of the comparison between the
models and the target structures are discussed in a meeting where assessors and predictors convene. The
conclusions are made available to the whole scientific community through the World Wide Web and
through the publication of a special issue of the journal “Proteins: Structure, Function, and Bioinformatics”.
The method for assessing protein structure predictions in CASP has developed throughout the years
becoming very professional and somewhat standardized, however several other categories have been
introduced in CASP throughout the years, such as prediction of function, domain boundaries, disordered
regions, and model quality and each of them has introduced novel problems that required "ad hoc"
solutions that I will discuss.
A. Di Cara1, L. Mendoza2, A. Garg3, G. Di Michieli3, I. Xenarios4
1 Merck-Serono, Geneva, Switzerland
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
2 UNAM, Mexico City, Mexico
3 EPFL, Lausanne, Switzerland
4 Swiss Institute of Bioinformatics /Vital-IT, Lausanne, Switzerland
“A qualitative modeling approach of TNF-alpha / TGF-beta regulatory network: Advantages and
Limitations”
The understanding of the dynamical behavior of any biological system is a holy grail of systems biology.
Within the ENFIN network (www.enfin.org) our aim is to provide methodologies to a wide variety of
“wet” and “dry” scientists to tackle that important challenge.
As a test case we started to study the interplay between TNFa!and TGFb!pathways, by modeling these
networks and identifying key molecular regulators. We use a qualitative boolean modeling approach. This
modeling technique only requires the topology of the interactions and their net effect defined as activation
or inhibition. There is no need to accumulate vast amount of kinetic data (at this stage) and then fit these
onto the model.
Our aim is to use this model as a guide to identify key “wet” experiments to perform. The drug discovery
process could benefit from using this model for biomarker discovery and mode of action studies.
Our approach to study the TNFa!and TGFb!pathway interactions comprised four steps: (I) Model building of
the two interconnected pathways which consists of currently 26 components, extracted from experimental
literature with emphasis on the identification of feedback loops. (II) Using a generalized logical analysis1
method we identified steady state(s) of our network. (III) dynamical simulations where we perturbed the
steady states with TNFa, TGFb!or both ligands. (IV) Validation of the model in which TNFa!and TGFb!are
added simultaneously. Here we observe a dominance of the TGFb!pathway, which is in accordance with
experimentally derived data in a dendritic cell context.
Altogether our results show that using this modeling approach we are able to recapitulate the crosstalk
between the TNFa!and TGFb!pathways and identify key components involved in the functional behavior of
these two signaling networks. The final step of our modeling is to experimentally test some of our
predictions that shed some light on novel cellular behavior.
1L. Mendoza, I. Xenarios, Theor Biol Med Model, 2006 Mar, 16;3:13
2 F. Geissmann, P. Revy et al., Jour. Immun., 1999, 162: 4567-4575
Christos Ouzounis
"Evolutionary analysis of biological pathways: implications for curation and inference".
The analysis of metabolic and signaling pathways will be presented in a few case studies, covering a wide
range of phylogenetic distances, from archaea and bacteria to vertebrates. These studies have revealed both
the remarkable conservation of core metabolism and the surprising diversification of signaling cascades.
Methods that accurately detect enzyme specificity will also be presented and the limitations of similar
approaches for effector specificity will be examined. Finally, possible implications for curation and
inference of biological pathways will be discussed.
Audit, B., Levy, E.D., Gilks, W.R., Goldovsky, L. and Ouzounis, C.A. (2007) CORRIE: enzyme sequence
annotation with confidence estimates. BMC Bioinformatics, 8 Suppl 4, S3.
Ouzounis, C.A. and Karp, P.D. (2000) Global properties of the metabolic map of Escherichia coli. Genome
Res., 10, 568-576.
Peregrin-Alvarez, J.M., Tsoka, S. and Ouzounis, C.A. (2003) The phylogenetic extent of metabolic enzymes
and pathways. Genome Res., 13, 422-427.
Tsoka, S. and Ouzounis, C.A. (2001) Functional versatility and molecular diversity of the metabolic map of
Escherichia coli. Genome Res., 11, 1503-1510.
Tsoka, S., Simon, D. and Ouzounis, C.A. (2003) Automated metabolic reconstruction for Methanococcus
jannaschii. Archaea, 1, 223-229.
von Mering, C., Zdobnov, E.M., Tsoka, S., Ciccarelli, F.D., Pereira-Leal, J.B., Ouzounis, C.A. and Bork, P.
(2003) Genome evolution reveals biochemical networks and functional modules. Proc. Natl. Acad. Sci.
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
USA, 100, 15428-15433.
Diego Di Bernardo
“Reverse engineering gene network in genetic diseased and drug discovery”
We will present a reverse engineering method to identify gene networks from multiple measurements of
gene expression at steady-state following single-gene perturbation, or from time-series data following a
single perturbation experiment. We will then present their application to a synthetic gene network we built
in the yeast S. cerevisiae. We will conclude with a brief comparison we made between the performance of
different reverse engineering methods, and how we measured the performance.
Hedi Peterson, Jaak Vilo
“Usage of gene expression data for pathway reconstruction”
We will describe a study that investigates the suitability of expression data for pathway reconstruction,
using a variety of pathways. In light of these experiments we can hypothesize on the chances of discovering
new "missing" members of the pathways. We are currently exploring any significant relationships between
the datasets picked out by our method and their biological significance to the pathway in question; for
example, if the pathway in question is related to apoptosis, are the cancer datasets the best descriptors for
this pathway?
We validate the effectiveness of our methodology on Reactome pathways by leave-one-out cross-validation
experiments. During validation a gene from the known pathway is deliberately excluded during the
development of our model, and then used as a test point to validate the model. We will use our results to
determine those pathways for which it is easier to predict new members using gene expression. Finally we
will suggest the possible biological reasons behind the given conclusions.
Ian Morilla (1), Adam Reid (2), Corin Yeats (2), Jonathan Lees (2), Christine Orengo (2), Juan A. G.
Ranea (1)
“BIOMATHEMATICAL IMPROVEMENT OF PROTEIN HIGH-THROUGHPUT FUNCTIONAL
PREDICTION”
(1) Department Biochemistry & Molecular Biology (Procel lab), Faculty of Sciences, University of
Malaga, UMA, Spain
(2) Department of Biochemistry and Molecular Biology, University College London, London WC1E 6BT,
UK
The aim of the most high-throughput experiments is to discover new molecules functionally associated to
particular biological systems or processes. So a large of biological datasets are generated from any of the
current proteomic or genomic experiments. The individual sequences are usually identified and functionally
annotated by single homology searches run on the extant available sequence databases. But this is just a
first simple approach for allowing to validate the experiments (true or false positives). Unfortunately, in
many cases, these high-throughput experiments show a poor overall performance with high rates of false
positive and false negative hits. Therefore a complementary bioinformatics treatment is required to obtain a
more reliable functional prediction.
Systems biology is provided with various high-throughput technologies in order to analyze global
biological datasets. Each of these technologies has different levels of accuracy and system coverage
(statistical power) and reflect error rates when are used individually. These error rates can be reduced by
integrating multiple datasets from different high throughput technologies and so capture complementary
information. In this work we are right integrating the Fused Domain (FD), Inherited Protein-Protein
interaction data (hiPPI), GEC(gene expression profile comparison) and Semantic Similarity (SS)
bioinformatics methods. We have chosen Fisherís weighted and non weighted methods and Shannon
information theory [2] for integrating predictions. In this way, we deal effectively with the difference in
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
statistical power, thus reducing the error rates and improving predictions benchmarked on the yeast
proteome annotated by GO (Gene Ontology database [3,4]).
[1] Hwang D. et al. (2005) A data integration methodology for systems biology. PNAS 102(48), 17296-
17301.
[2] Shannon C. E. (1948) A Mathematical Theory of Communication. The Bell System Technical Journal,
Vol. 27, pp. 379-423, 623-656.
[3] Zeeberg, B.R., Feng, W., Wang, G., Wang, M.D., Fojo, A.T., Sunshine, M., Narasimhan, S., Kane,
D.W., Reinhold, W.C., Lababidi, S., et al. (2003) GoMiner: a resource for biological interpretation of
genomic and proteomic data. Genome Biol., 4, R28.
[4] The Gene Ontology Consortium. (2000) Gene Ontology: tool for the unification of biology. Nature
Genet, 25, 25-29.
Alberto Corradin, Barbara Di Camillo, Gianna Maria Toffolo, Claudio Cobelli.
“In silico assessment of four reverse engineering algorithms: role of network complexity and multi-
experiment design in network reconstruction and hub detection”.
Background.
An important problem in systems biology is the inference of regulatory networks from gene expression
data. Several reverse engineering methods have been proposed in the literature, among them linear and
nonlinear Dynamic Bayesian Networks, DBNs, (Ferrazzi et al., 2007), ARACNe (Margolin et al., 2006)
and Graphical Gaussian Models, GGMs, (Schafer and Strimmer, 2005). These algorithms use different
approaches: linear and nonlinear DBNs are model-based methodologies, whereas ARACNe and GGMs
exploit pair-wise profile comparison based on mutual information and partial correlation, respectively.
Since no biological network is understood well enough to serve as a standard, reverse engineering methods
are usually assessed in silico. Recently a novel simulator, which takes into account topological properties
(e.g. scale-free connectivity), interactions among regulators and complex dynamics, has been developed
(Di Camillo et al., 2006). The purpose here is to assess the ability of the four reverse engineering methods
to reconstruct network topology and to detect hubs.
Methods.
300 different network topologies were generated with number of genes N=12, 20 or 100. For each of them,
gene expression datasets were simulated starting from S= 10, 3 or 1 different initial conditions, each
corresponding to an experiment. For each condition, M=50, 10, 5 or 2 time samples were collected.
Algorithms were tested both with and without gaussian noise (mean=0, SD=1 corresponding to
CV≥10%), but reported results only refer to noisy data.
Algorithms were scored using F-measure (Ferrazzi et al., 2007), which combines sensitivity S and positive
predictive value PPV: F=2*S*PPV/(S+PPV).
Statistically significant differences were assessed using exact Wilcoxon tests.
Results.
All methods perform poorly with networks having N=100 genes (F<40%). With lower N, linear DBNs
perform best in data-rich situations (S=10, M=50), e.g. with N=12, F=0.62± 0.11 (mean±SD). However,
DBNs performance deteriorates with lower M (e.g. with N=12: F=0.54± 0.12, F=0.39± 0.11, F=0.23±0.09
for M=10, 5, 2, respectively), whereas ARACNe is less sensitive to M (N=12: F=0.53±0.10, F=0.52±0.07,
F=0.51± 0.11, F=0.43±0.16 for M=50, 10, 5, 2, respectively) and performs best in data-poor situations.
In our simulations, GGMs and nonlinear DBNs are always overcome by the other algorithms.
All methodsí results deteriorate with lower S (e.g. with N=12, M=50, S=3: F= 0.47±0.13 with linear DBNs,
which scores highest), reaching poor performance (F<40%) when S=1. The four methods perform similarly
in hub detection: the F-measure of the connections of identified hubs is reasonable (F=0.63±0.14 with
ARACNe, which performs best when N=20, S=10, M=50), but the number of hubsí connections is
underestimated.
Conclusions.
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
Reverse Engineering methods are able to formulate reliable hypothesis about networks with a limited
number of genes if their expression is adequately monitored during multiple experiments, so to excite
different states of the system.
The method of choice depends on the number of samples: linear DBNs outperform in data-rich situations
while ARACNe in data-poor ones.
Denis Thieffry
“Logical modelling and analysis of biological regulatory networks: identification of stable states and
feedback circuit analysis”
The complexity of biological regulatory networks calls for the development of proper mathematical
methods to model their structures and to obtain insight in their dynamical behaviours. One qualitative
approach consists in modelling regulatory networks in terms of logical equations, using Boolean or multi-
level variables.
Recently, we have proposed a novel implementation of the multi-level logical modelling approach by
means of Multi-valued Decision Diagrams This representation enabled the development of two efficient
algorithms for the dynamical analysis of parameterised regulatory graphs. A first algorithm allows the
identification of all stable states without generating the state transition graph. A second algorithm assesses
the conditions insuring the functionality of the feedback circuits found in the regulatory graph.
These algorithms have been implemented into a novel development version of our logical modelling
software GINsim. Their application to logical models of T cell activation and differentiation will be briefly
presented.
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.
Local Information
To find hotels near venue, please visit:
http://www.cnio.es/eventos/listhotels.asp
For directions, please visit: http://www.cnio.es/ing/comollegar.asp
The ENFIN project is funded by the European Commission within its FP6 Programme, under the thematic area
"Life sciences, genomics and biotechnology for health”; Contract number LSHG-CT-2005-518254
DREAM is sponsored by Columbia University MAGNet Center, the NIH Roadmap Initiative and the IBM Computational Biology Center.