Martin obtained his Ph.D. in 1996 from McGill University in Montreal, Canada, in the field of protein biochemistry by characterizing in detail the processing of a sperm motility inhibitor by prostate-specific antigen, a widely-used prostate cancer biomarker. He went on to pursue postdoctoral training at Chugai Pharmaceuticals, Ltd (now part of the Roche group) near Tsukuba, Japan working on cellular senescence and protein-protein interaction, the discovery and cloning a novel ubiquitin ligase, and also developing and using stable cell lines for cytokine discovery. In 2001 he joined Euroscreen S.A., a biotech company in Brussels, Belgium as project manager for the functional characterization of novel orphan G-protein coupled receptors (GPCR).

To pursue his interest in the field of systems biology, Martin joined the Institute for Advanced Biosciences (IAB), Keio University in 2003, as Assistant Professor. There, he currently oversees the development and application of functional proteomics/metabolomics platforms for discovering and characterizing E. coli enzymes and gene products whose function remains unknown. In multi-omics analysis of E. coli and experimental/informatic studies to facilitate metabolite identification in metabolimics datasest are other ongoing projects. Data processing and analysis tools for metabolomics are also under development. As a faculty member of the Systems Biology Graduate Program at Keio-SFC, Martin is also teaching an undergraduate Proteome Analysis Laboratory Practice course and a graduate course about Communicating Bioscience. He is also advising and co-supervising several graduate students.

In his spare time, among other things Martin enjoys photography and traveling

Research Activities & Projects:

Main research site:  http://mr.iab.keio.ac.jp/home

The cellular processes involved in the maintenance of cellular activity, including, cell movement, replication, responses and adaptation to the environment, energy metabolism and biosynthesis etc. form an extremely complex network of interacting biomolecules from which emerge the basic properties of life. The thorough understanding of such processes is an immense challenge even for some of the most basic living organisms such as the bacterium E. coli, a common human digestive tract inhabitant. However, we believe this simpler model organism is a logical starting point to begin such an ambitious project. Moreover, there are good reasons to believe that this simpler life form will also provide information that will be broadly applicable to more complex eukaryotic and multicellular organisms. On the other hand, while the metabolic pathways of E. coli are among the best characterized, numerous uncharacterized components and activities remain to be discovered.

 Our current research projects are part of larger research efforts at the Institute for Advanced Biosciences (IAB) that focus on E. coli systems biology. The main objective of most of our research efforts is to interrogate different levels of biological information in both a targeted or large-scale fashion, to better understand the function and properties of biological molecules and reveal how their interactions control cellular functions.

Functional genomics through proteomics and metabolomics
The availability of complete genome sequence information for multiple organisms has brought about a new era of research, the so-called post-genomics. While sequence information is extremely valuable it does not readily allow functional elucidation in most cases. To tackle this problem and investigate the biological function of the large number of uncharacterized genes and their products, we make use of genome-wide experimental resources. We can thus systematically analyze gene function by investigating cellular response to gene deletion, overexpression or environmental perturbation.

The characterization of cellular activities on a global scale requires the analysis of multiple levels of biological information including, but not necessarily exclusively, those at the transcriptome, proteome, metabolome, and phenotypic level. Most of these analyses require an exhaustive survey of all the components or biomolecules in the cell under a particular set of conditions. Powerful analytical tools are necessary for this task and our facilities are well equipped to do this. We are putting particular emphasis on the comprehensive characterization and identification of intracellular proteins and metabolites. Various separation methods such as cappilary electrophoresis (CE) and liquid chromatography are  combined with mass spectrometry-based analytical systems (CE-MS, LC-MS) under various configurations (single quadrupole, time of flight (TOF), Q-TOF, triple quadrupole, etc.). Together with two-dimensional differential gel electrophoresis (2D-DIGE) and MALDI-TOF these are used to characterize metabolites and proteins, and their interactions. Proteins can be identified using standard tandem mass spectrometry and database search. Metabolites can be identified using chemical standards, by obtaining tandem mass spectra, and through chemical formula elucidation using high mass resolution and accuracy mass spectrometers as well as computational predictions.

 

Using these methods, we are currently using a functional proteomics/metabolomics platform for characterizing the large number of E. coli enzymes and gene products whose function remains unknown.

In addition, kinetics parameters for major metabolic pathways are being experimentally collected in an automated and standardized fashion and used for modeling. Software solutions, such as MathDAMP and JDAMP are also being developed to facilitate the analysis of complex metabolome profiles. As shown in the figure below, this tool can highlight specific and statistically significant differences in profiles, a task that was until now very tedious and time-consuming.

Additional projects in proteome, metabolome and phenotype profiling of E. coli gene deletion mutants are also ongoing. These aim at understanding the global response of the cell to both genetic and environmental perturbations. The project web site is ecoli_multiomics. A figure describing the different levels of information under study and the methods used to analyze the cells is shown below.

 

Systems biology and cell simulation
While the overall description of all these level of biological information is an extremely large and complex undertaking that is our long-term objective, we believe a simpler picture of global cellular responses will emerge from an underlying complex network of elements. To integrate the huge amount of complex data produced by this type of large-scale experiments, we are developing and making extensive use of bioinformatics software and tools for both data handling and analysis.

In collaboration with other IAB and external researchers we are aiming to use the collected data for modeling of cellular activities using powerful and general simulation tools such as the E-CELL software environment. E-CELL allows to analyze and simulate various cell functions and activities, in silico. By feeding experimental data obtained using the above approaches as inputs to the E-CELL system we eventually aim to produce a powerful cell simulation tool. Following the generation of a new set of hypotheses to be tested experimentally, such tools will allow to predict and understand the effect of environmental or genetic perturbations (e.g. gene knock-out, nutrient change, drug treatment) on the cell’s capacity to grow and react to those changes. Finally, this system biology approach may facilitate the manipulation of the cell to perform useful functions such as the production of medically or industrially valuable products.

A brief research feature on Shonan-Fujisawa Campus' Who's Who pages.

Research Highlight on IAB web site

Press release 

Our work viewed by others

Science STKE (EDITORS' CHOICE) 

Perspectives (in Science) 

Scientific societies

• Metabolomics Society
• Japanese Society for Bioinformatics

Keywords: 

Functional genomics, proteomics, metabolomics, E-CELL, cell simulation, mass spectrometry, systems biology 

 

Project sites:

Metabolomics data analysis: MathDAMP    JDAMP
E. coli multi-omics analysis project: http://ecoli.iab.keio.ac.jp/

Links:

Group Research site

My Linkedin profile

My ResearcherID

 

Shonan-Fujisawa Campus of Keio University

Selected publications:

1. Sugimoto M., Kawakami M., Robert M., Soga T., Tomita M. (2012) Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis, Current Bioinformatics, 7, 96-108
2. Saito, N. & Robert, M. Metabolomics approach for enzyme discovery. Seikagaku 83, 1039-1043 (2011). (Review in Japanese)
3. Sugimoto M., Hirayama A., Robert M., Abe S., Soga T., and Tomita M. (2010) Prediction of metabolite identity from accurate mass-, migration time prediction-, and isotopic pattern information in CE-TOF-MS data. Electrophoresis, 31, 2311-2318.
   
4. Sugimoto M., Hirayama A., Ishikawa T., Robert M., Baran R., Uehara K., Kawai K., Soga T., and Tomita M. (2010). Differential Metabolomics Software for Capillary Electrophoresis-Mass Spectrometry Data Analysis. Metabolomics 6, 27-41
5. Saito, N., Robert, M., Kochi, H., Matsuo, G., Kakazu, Y., Soga, T., and Tomita, M. (2009) Metabolite profiling reveals YihU as a novel hydroxybutyrate dehydrogenase for alternative succinic semialdehyde metabolism in Escherichia coli. J. Biol. Chem. 284, 24, 16442-16451.
6. Ishii, N*., Nakahigashi, K*., Baba, T*., Robert, M*., Soga, T*., Kanai, A*., Hirasawa, T*., Naba, M., Hirai, K., Hoque, A., et al. (2007). Multiple High-Throughput Analyses Monitor the Response of E. coli to Perturbations. Science 316, 593-597. * These authors contributed equally.
   
7. Robert, M., Soga, T., and Tomita, M. (2007) E. coli metabolomics: capturing the complexity of a “simple” model. In: Metabolomics, A Powerful Tool in Systems Biology. Topics in Current Genetics. Eds. Nielsen J. Jewett M. pp 189-234, Springer Berlin/Heidelberg.
8. Baran R., Robert M*., Suematsu M., Soga T. and Tomita M.  (2007) Visualization of three-way comparisons of omics data. BMC Bioinformatics 8, 72. *as corresponding author.  This manuscript is flagged as “Highly accessed” by BMC Bioinformatics.
9. Baran, R., Kochi, H., Saito, N., Suematsu, M., Soga, T., Nishioka, T., Robert, M., and Tomita, M. (2006). MathDAMP: a package for differential analysis of metabolite profiles. BMC Bioinformatics 7, 530.
   
10. Shinoda, K., Sugimoto, M., Yachie, N., Sugiyama, N., Masuda, T., Robert, M., Soga, T., and Tomita, M. (2006). Prediction of Liquid Chromatographic Retention Times of Peptides Generated by Protease Digestion of the Escherichia coli Proteome Using Artificial Neural Networks. J Proteome Res 5, 3312-3317. 
    
11. Saito, N., Robert, M., Kitamura, S., Baran, R., Soga, T., Mori, H., Nishioka, T., and Tomita, M. (2006). Metabolomics approach for enzyme discovery. J Proteome Res 5(8): p. 1979-87.
12. Soga, T., Baran, R., Suematsu, M., Ueno, Y., Ikeda, S., Sakurakawa, T., Kakazu, Y., Ishikawa, T., Robert, M., Nishioka, T., and Tomita, M. (2006) Differential metabolomics reveals ophthalmic acid as an oxidative stress biomarker indicating hepatic glutathione consumption. J Biol Chem 281(24): p. 16768-76.
13. Arita, M., Robert, M., and Tomita, M. (2005). All systems go: launching cell simulation fueled by integrated experimental biology data. Curr Opin Biotechnol 16(3): 344-349.
14. Ishii, N., Robert, M., Nakayama, Y., Kanai, A., and Tomita, M. (2004) Toward large-scale modeling of the microbial cell for computer simulation. JournaI of Biotechnology, 113, 281-294.
15.  Soga T., Kakazu Y., Robert M., Tomita M., and Nishioka T. (2004). Qualitative and quantitative analysis of amino acids by capillary electrophoresis electrospray ionization tandem mass spectrometry.  Electrophoresis 25, 1964-72.
16. Robert, M. and Gagnon C. (1999). Semenogelin I: a coagulum forming, multifunctional seminal-vesicle protein. Cellular and Molecular Life Sciences. 55, 944-960.
17. Wadhwa, R., Takano, S., Robert, M., Yoshida, A., Nomura, H., Reddel, R. R., Mitsui, Y., and Kaul, S. C. (1998). Inactivation of tumor suppressor p53 by mot-2, a hsp70 family member. Journal of Biological Chemistry 273, 29586-91.
18. Robert, M., Gibbs, B. F., Jacobson, E., and Gagnon, C. (1997). Characterization of prostate-specific antigen proteolytic activity on its major physiological substrate, the sperm motility inhibitor precursor/semenogelin I. Biochemistry 36, 3811-3819.
19. Robert M., and Gagnon C. (1996). Purification and characterization of the active precursor of a human sperm motility inhibitor secreted by the seminal vesicles: identity with semenogelin. Biology of Reproduction 55, 813-821.
20. Robert M., and Gagnon C. (1995). Sperm motility inhibitor from human seminal plasma: association with semen coagulum. Human Reproduction 10, 2192-2197.
21. Robert M., and Gagnon C. (1994). Sperm motility inhibitor from human seminal plasma: presence of a precursor molecule in seminal vesicle fluid and its molecular processing after ejaculation. International Journal of Andrology 17, 232-240.
    

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