SBI is engaged in a series of research program to best accomplish our mission to expand our knowledge on biological systems and applying it to address key challenges in medicine, global energy and climate issues. 


AI for Science


Nobel Turing Challenge

Artificial intelligence (AI) is poised to change the way science is done. Our work revolves around using the best AI technologies to the theme of expanding our knowledge on biological systems and its application to healthcare, medicine, global energy and climate issues.




Computational platforms such as representation standards, software for modeling, simulation, and analysis, as well as methodologies for modeling and analysis are vital for success of systems biology research. However, such resources are still largely missing in systems biology. Serious efforts and investments have to be made to reinforce our arsenal for high-impact research in systems biology. SBI and its precursor projects have played a major role in the creation and continuous development of Systems Biology Markup Language (SBML). With the success of SBML project, SBI is now expanding its activities in CPSB area. 




Robustness is phenomena that is ubiquitous and observed in various layers, from gene regulation to ecosystem. We believe it is one of the fundamental properties of biological systems where a basic principle of biological systems is embedded. This is because robustness and evolvability may be tightly coupled: mechanisms for enhancing robustness against environment perturbations also enhance evolvability, and more robust individuals tend to be selected through evolution. Thus, evolvable robust system has characteristic architectural features and common underlying mechanisms. At the same time, it inherently entails trade-offs among robustness, fragility, resource demands, and performance. Diseases such as cancer, diabetes mellitus, AIDS, and immunological disorders are manifestations of such trade-offs. Therefore, understanding biological robustness is expected to generate novel interpretation of diseases and therapeutic approaches accordingly. At SBI, we are working on both theoretical and experimental aspects of robustness in biological systems.




SBI launched its drug and therapy design project with a focus on cancer. Many diseases are multifactorial, involving numbers of genes. Oncogenes are often hubs of the network where intervention may result in serious side effects. One possible approach to overcome difficulties in current drug discovery may be to use multiple component multiple target approach where synergetic effects may attain both efficacy and selectivity. SBI has been developing theoretical, experimental, and computational platforms to verify this concept and explore the possibility of designing such combinatorial drugs. 


Cancer is a leading cause of death worldwide. Its disease phenotype demonstrates a high level of robustness against various therapeutic perturbations. One of the ultimate goals of SBI is to discover novel therapeutic approaches that can control and eventually cure cancer. Our fundamental approach is based on the recognition that cancer established itself as a robust, evolvable system; therefore, it also entails inherent trade-offs. Identification of the level of tumor robustness assists clinical decisions. Control of robustness may enable us to control tumor progression and even genuine tumor dormancy. If we can identify extreme fragility, it will lead us to dramatic outcomes. Accomplishment of this mission requires a rigid and sophisticated theoretical framework and experimentally supported understanding of biological robustness, as well as a set of powerful computational platforms and a proper approach to how to make the best use of computational power. SBI is committed to promoting cancer systems biology by integrating theory and practice. 






SBI is involved in an international project to create a comprehensive computational model of budding yeast. Currently, SBI is focusing on the development of signal transduction and cell cycle models. The project involves both computational and experimental approaches to create high-precision models.


The SBI's involvement in studying coral reefs aims to understand complex molecular dynamics of coral and its symbionts. Coral reefs are tropical rainforests of the sea that boast impressive biodiversity and CO2 absorption capabilities, offering a wealth of biological resources. Unfortunately, over 70% of coral reefs are in danger due to environmental stresses, including global climate change. The symbiotic relationship between scleractinian corals and photosynthetic algae underpins their ecological significance. Understanding the robustness and fragility of these reef-building corals against various stressors is critically important for both scientific and environmental reasons. This intricate balance is explained by the self-extended symbiosis theory, which highlights trade-offs inherent in biological systems.


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