Aurélien Naldi

Aurelien's homepage

profile picture I am a postdoctoral researcher in bioinformatics in the Lifeware team at INRIA Saclay - Île-de-France.

Contact: aurelien.naldi [AT] inria.fr

I work on deciphering the mechanisms underlying complex biological systems through formal analysis of qualitative dynamical models. These models are based on mechanistic details identified experimentaly. By confronting model predictions with known phenotypes, we aim to highlight missing or inconsistent knowledge, as well as to guide experimental design. as summarized below, this work combines methodological developments, software implementation, and applications to biological systems.

Methodological work

I work on formal approaches enabling the study of large models for which classical simulations do not scale.

I proposed a method for the identification of stable states (fixed points) by turning individual functions into stability conditions for each components. Stable states can then be identified using efficient constraint solving methods. This approach has since been generalized for the identification of stable motifs which provide a good approximation of more complex attractors.

Decision Diagrams for the Representation and Analysis of Logical Models of Genetic Networks. A. Naldi, D. Thieffry, C. Chaouiya. Computational Methods in Systems Biology :233--247 (2007).
[ doi:10.1007/978-3-540-75140-3_16 ]

I also proposed a model reduction method, allowing to remove some manually-selected components while preserving important dynamical properties. We could show in particular that this reduction preserves stable states (minimal stable motifs are also preserved) and gives an under-approximation of reachability properties.

Dynamically consistent reduction of logical regulatory graphs. A. Naldi, E. Remy, D. Thieffry, C. Chaouiya. Theoretical Computer Science 412:2207--2218 (2011).
[ doi:10.1016/j.tcs.2010.10.021 ]

Software tools

Since 2006, I am the main developer of the GINsim software for the definition and analysis of logical models. This tool provides a graphical interface for model design and a number of analytical tools, including my formal works on logical models.

The bioLQM toolbox further provides command line and programmatic interfaces for the core data structures and algorithms available in GINsim, as well as import/export bridges to other software tools.

Logical modelling and analysis of cellular regulatory networks with GINsim 3.0. A. Naldi, C. Hernandez, W. Abou-Jaoudé, P. Monteiro, C. Chaouiya, D. Thieffry. Frontiers in Physiology 9:646 (2018).
[ doi:10.3389/fphys.2018.00646 ]

BioLQM: a java toolkit for the manipulation and conversion of Logical Qualitative Models of biological networks. A. Naldi. Frontiers in Physiology 9:1605 (2018).
[ doi:10.3389/fphys.2018.01605 ]

I have been involved in the definition of the qual extension for SBML, an exchange format for qualitative models. This effort led to the creation of the CoLoMoTo consortium and to the recently introduction of the CoLoMoTo notebook as a complete modelling platform integrating a collection of complementary software tools. This platform further focuses on reproducible results through the use of Docker images (providing frozen snapshots of a complex software environment) and Jupyter notebooks to build and share complex analysis workflows.

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. C. Chaouiya, D. Bérenguier, S. Keating, A. Naldi, M. van Iersel, N. Rodriguez, A. Dräger, F. Büchel, T. Cokelaer, B. Kowal, B. Wicks, E. Gonçalves, J. Dorier, M. Page, P. Monteiro, A. Kamp, I. Xenarios, H. de Jong, M. Hucka, S. Klamt, D. Thieffry, N. Novère, J. Saez-Rodriguez, T. Helikar. BMC Systems Biology 7:135 (2013).
[ doi:10.1186/1752-0509-7-135 ]

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks. A. Naldi, C. Hernandez, N. Levy, G. Stoll, P. Monteiro, C. Chaouiya, T. Helikar, A. Zinovyev, L. Calzone, S. Cohen-Boulakia, D. Thieffry, L. Paulevé. Frontiers in Physiology 9:680 (2018).
[ doi:10.3389/fphys.2018.00680 ]

Logical models and data analysis for biological systems

Qualitative models have been applied to a wide range of biological processes. In particular, GINsim has been used to study dozens of models, either through collaborations or by independent researchers.

I have been personally involved mainly in the study of cell cycle, and differentiation of T helper cells.

Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle. A. Faure, A. Naldi, C. Chaouiya, D. Thieffry. Bioinformatics 22:e124--e131 (2006).
[ doi:10.1093/bioinformatics/btl210 ]

Diversity and Plasticity of Th Cell Types Predicted from Regulatory Network Modelling. A. Naldi, J. Carneiro, C. Chaouiya, D. Thieffry. PLoS Computational Biology 6:e1000912 (2010).
[ doi:10.1371/journal.pcbi.1000912 ]

During my postdoctoral work in Lausanne (Switzerland), I was involved in the study of genomics data (microarray and ChIP-seq) in the context of metabolic processes.

Genome-Wide Analysis of SREBP1 Activity around the Clock Reveals Its Combined Dependency on Nutrient and Circadian Signals. F. Gilardi, E. Migliavacca, A. Naldi, M. Baruchet, D. Canella, G. Martelot, N. Guex, B. Desvergne. PLoS Genetics 10:e1004155 (2014).
[ doi:10.1371/journal.pgen.1004155 ]

System analysis of cross-talk between nuclear receptors reveals an opposite regulation of the cell cycle by LXR and FXR in human HepaRG liver cells. L. Wigger, C. Casals-Casas, M. Baruchet, K. Trang, S. Pradervand, A. Naldi, B. Desvergne. PLOS ONE 14:e0220894 (2019).
[ doi:10.1371/journal.pone.0220894 pubmed:31437187 ]