Key research themes
1. What governance frameworks best address the challenges posed by digital sequence information (DSI) in genetic resource access and benefit-sharing?
This theme focuses on the evolving policy and legal challenges arising from the digitization of genetic resources, particularly the implications of DSI for access and benefit-sharing (ABS) regimes. The research addresses how existing international agreements like the Convention on Biological Diversity (CBD), Nagoya Protocol (NP), and Pandemic Influenza Preparedness Framework (PIP) adapt or require reform to integrate DSI, reconciling material access to genetic resources with emergent digital data flows. Given the crucial role of DSI in biotechnology, synthetic biology, and breeding, effective governance frameworks are vital for equity, sovereignty, and sustainable utilization.
2. How can efficient computational methods enhance sequence analysis and decoding without reliance on traditional alignment?
This theme investigates algorithmic and computational innovations for analyzing and decoding sequence data, focusing on methods that address challenges posed by long-range dependencies, alignment complexity, and large data volumes inherent in various types of symbolic sequences including biological data. The research explores approaches such as local decoding, alignment-free dissimilarity measures, and efficient code constructions based on Lempel-Ziv and related compression methods, which facilitate scalable, information-rich analysis of sequence similarity, periodicity, and complexity.
3. What sequence analysis methodologies effectively reveal structural and periodic features in symbolic biological and textual sequences?
This theme investigates mathematical and information-theoretic approaches to identify intrinsic periodicities, correlations, and symbolic structures within sequences. By advancing approaches that avoid simple numerical transformations and instead utilize information decomposition, co-occurrence statistics, and novel correlation measures, these studies reveal hidden structural properties relevant to genomic, proteomic, and textual data. Understanding such patterns is crucial for elucidating functional and evolutionary relationships in biological sequences as well as in analyzing complex symbolic datasets.