ThESIS BY Aurélie Pirayre*, 2018 Yves Chauvin prize
Trichoderma reesei is a fungus that is being studied at IFPEN for its enzyme production used in 2nd-generation biofuel production processesa. A more accurate understanding of its genetic mechanisms is required in order to improve the efficiency of such processes. Hence, the purpose of this thesis was to identify the way in which their genes interact – directly or otherwise – in connection to enzyme production.
Biological data to be analyzed for this type of research possess a considerable volume and significant heterogeneity. They require the development of efficient bioinformaticb algorithms. An optimization tool suite, based on biological and structural constraints, was developed to construct gene interaction graphs. Hinged around the BRANEc concept, this suite includes BRANE Cut(1), a method dedicated to regulatory networks (Figure), and BRANE Clust(2) adding, to networks, gene clustering on the basis of their biological functions.
The improvement, with respect to published reference methods, was demonstrated, via benchmark datasetsd, on model microorganisms. Upon validation, BRANE was employed on Trichoderma reesei, with a meticulous and promising selection of candidate genes.
These new tools provide invaluable aid in rapidly identifying useful interaction cascades for enzyme production. Broadening the scope becomes reachable. This involves the alliance of new “omic” data and epigenetic mechanisms, extended to the lineage of our fungus.
a - Only using the non-edible parts of the plants.
b - Using the storage and analysis power of information technology to study life science fields.
c - Biologically-Related Apriori Network Enhancement.
d - Both real and simulated, made available by the DREAM consortium (http://dreamchallenges.org/).
*Thesis entitled « Reconstruction and Clustering with Graph optimization and Priors on Gene networks and Images »
(1) A. Pirayre, C. Couprie, F. Bidard, L. Duval, J-C. Pesquet. BRANE Cut: biologically-related a priori network enhancement with graph cuts for gene regulatory network inference, BMC Bioinformatics, 2015.
>> DOI: 10.1186/s12859-015-0754-2
(2) A. Pirayre, C. Couprie, L. Duval, J-C. Pesquet. BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, Vol. 15, Issue 3.
>> DOI: 10.1109/TCBB.2017.2688355