Computational approaches with the objective to explore intra and cross-species interactions and their role in all domains of life

• Grant number: 19/22845-9
• Support Opportunities: Regular Research Grants
• Start date: March 01, 2020
• End date: February 29, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computational Mathematics
• Agreement: INRIA
• Mobility Program: SPRINT - Projetos de pesquisa - Mobilidade
• Principal Investigator: André Fujita
• Grantee: André Fujita
• Principal researcher abroad: Marie-France Sagot
• Institution abroad: Institut National de Recherche en Informatique et en Automatique (INRIA Rhône-Alpes), France
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated researchers: Ariel Mariano Silber ; Carlos Alberto Moreira Filho ; Carlos Eduardo Ferreira ; Cristina Gomes Fernandes
• Associated research grant: 18/21934-5 - Network statistics: theory, methods, and applications, AP.TEM

Abstract

The CAPOEIRA project will cover theoretical computer science (essentially graph theory), mathematics (combinatorics, statistics, and probability), and the development of algorithms to address various biological questions, in particular, the intra and cross-species interactions, which have implications in all aspects of life sciences, including health, ecology, and environment. Two main general topics will be addressed, namely evolution/co-evolution, and biological network (graph/hypergraph) analysis and comparison. Both have already been explored by the partners (see Section 11.1 on the Joint publications of the partners). Some of the specific questions to be treated within each problem will thus represent a continuation of previous works. Each problem however also contains entirely new questions. Furthermore, the interaction with biologists within the project at both the modelling and validation steps is entirely new in the context of the past collaboration between the two partners. The first topic concerns better understanding and characterising the moment of speciation leading to new species on one hand, and on the other, how one set of species may influence the evolution of another. The second topic concerns metabolism on one hand, and (post-)transcriptional regulation on the other, with the post-transcriptional level involving also inference "from scratch" of the main actors, namely the non-coding RNAs and their targets, and the regulatory network they form. In the first two cases (of metabolism and transcriptional regulation), we will assume that the networks are already inferred, albeit with possibly numerous missing and incorrect data. Finally, in the case of regulation, we will also consider the problem of inferring variants, notably related to alternative splicing, from a set of RNA-seq data using a de Bruijn graph approach. Overseeing these two main topics are the issues of knowledge representation and model revision that will also be addressed. These are crucial in the life sciences, and notably in the context of post-transcriptional regulation by non-coding RNAs, for which the different actors, features, and overall mechanisms are constantly being questioned and revised. (AU)

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

GUZMAN, GROVER E. C.; STADLER, PETER F.; FUJITA, ANDRE. Efficient eigenvalue counts for tree-like networks. JOURNAL OF COMPLEX NETWORKS, v. 10, n. 5, p. 15-pg., 2022-08-23. (20/08343-8, 19/22845-9, 18/21934-5)

GUZMAN, GROVER E. C.; FUJITA, ANDRE. A fast algorithm to approximate the spectral density of locally tree-like networks with assortativity. JOURNAL OF COMPLEX NETWORKS, v. 11, n. 2, p. 15-pg., 2023-02-23. (19/22845-9, 18/21934-5)

SANTOS, SUZANA DE SIQUEIRA; FUJITA, ANDRE; MATIAS, CATHERINE. Spectral density of random graphs: convergence properties and application in model fitting. JOURNAL OF COMPLEX NETWORKS, v. 9, n. 6, p. 27-pg., 2021-10-20. (18/21934-5, 19/22845-9, 17/12074-0, 20/08343-8, 15/21162-4)

RAMOS, TAIANE COELHO; MOURAO-MIRANDA, JANAINA; FUJITA, ANDRE. Spectral density-based clustering algorithms for complex networks. FRONTIERS IN NEUROSCIENCE, v. 17, p. 14-pg., 2023-03-30. (20/08343-8, 18/21934-5, 19/22845-9)