Uploaded May 2026 | Updated September 2026, 2 weeks ago
Title: Information Propagation in Multiscale Systems, From Biochemical Signaling to Transduction Mechanisms
Speaker: Daniel M. Busiello, Max Planck Institute for the Physics of Complex Systems
Abstract: The presence of interconnected fluctuating processes occurring across multiple temporal scales is a fundamental characteristic of neural networks, ecological communities, biochemical architectures, and many other complex systems. Such processes can interact both directly and indirectly, with couplings across timescales often exhibiting intricate internal properties. This complexity makes understanding the relationships between the components of these multiscale systems a formidable challenge.
In this talk, I will begin by exploring how the distinct timescales associated with each process influence their effective couplings. By examining the probabilistic structure of a general multiscale system, I will uncover the underlying principles that govern information propagation across different timescales. In doing so, I will clarify the interplay between mutual information and coupling structure, revealing the origin of the critical distinction between causal and functional interactions in complex stochastic systems. I will then demonstrate how this emerging information structure can be harnessed to study minimal models of biochemical signaling networks and stochastic neural populations. Additionally, I will apply this framework to investigate how biological systems transduce information from hidden degrees of freedom through a set of accessible observables. I will show that, even within a limited energy budget, optimal transduction strategies can enhance information harvesting. This approach highlights a connection between mechanical stress and transduction efficiency in red blood cells. The ideas presented in this talk provide novel insights into the processing capabilities of complex multiscale systems.
Profile: Daniel M. Busiello earned his PhD in Physics from the University of Padua in 2018. He subsequently joined the Statistical Biophysics Lab at EPFL as a postdoctoral researcher. In 2022, he was appointed to an independent research position at the Max Planck Institute for the Physics of Complex Systems in Germany, where he established his research line at the interface of information theory, stochastic thermodynamics, and chemical reaction networks. Since early 2025, he has been a Group Leader at the University of Padua.
Daniel was awarded the Early Career Scientist Prize in Statistical Physics in 2025 and received the IgNobel Prize in Physics for his work on the phase behavior of Cacio e Pepe sauce.
Find out more about the TSVP on the program website: oist.jp/visiting-program
#ComplexSystems #MultiscaleSystems #CausalInference #StochasticSystems #BiochemicalNetworks #NeuralNetworks #OIST #TSVP #Theoretical #Science #VisitingProgram #Okinawa #research
Title: Information Propagation in Multiscale Systems, From Biochemical Signaling to Transduction Mechanisms
Speaker: Daniel M. Busiello, Max Planck Institute for the Physics of Complex Systems
Abstract: The presence of interconnected fluctuating processes occurring across multiple temporal scales is a fundamental characteristic of neural networks, ecological communities, biochemical architectures, and many other complex systems. Such processes can interact both directly and indirectly, with couplings across timescales often exhibiting intricate internal properties. This complexity makes understanding the relationships between the components of these multiscale systems a formidable challenge.
In this talk, I will begin by exploring how the distinct timescales associated with each process influence their effective couplings. By examining the probabilistic structure of a general multiscale system, I will uncover the underlying principles that govern information propagation across different timescales. In doing so, I will clarify the interplay between mutual information and coupling structure, revealing the origin of the critical distinction between causal and functional interactions in complex stochastic systems. I will then demonstrate how this emerging information structure can be harnessed to study minimal models of biochemical signaling networks and stochastic neural populations. Additionally, I will apply this framework to investigate how biological systems transduce information from hidden degrees of freedom through a set of accessible observables. I will show that, even within a limited energy budget, optimal transduction strategies can enhance information harvesting. This approach highlights a connection between mechanical stress and transduction efficiency in red blood cells. The ideas presented in this talk provide novel insights into the processing capabilities of complex multiscale systems.
Profile: Daniel M. Busiello earned his PhD in Physics from the University of Padua in 2018. He subsequently joined the Statistical Biophysics Lab at EPFL as a postdoctoral researcher. In 2022, he was appointed to an independent research position at the Max Planck Institute for the Physics of Complex Systems in Germany, where he established his research line at the interface of information theory, stochastic thermodynamics, and chemical reaction networks. Since early 2025, he has been a Group Leader at the University of Padua.
Daniel was awarded the Early Career Scientist Prize in Statistical Physics in 2025 and received the IgNobel Prize in Physics for his work on the phase behavior of Cacio e Pepe sauce.
Find out more about the TSVP on the program website: oist.jp/visiting-program
#ComplexSystems #MultiscaleSystems #CausalInference #StochasticSystems #BiochemicalNetworks #NeuralNetworks #OIST #TSVP #Theoretical #Science #VisitingProgram #Okinawa #research
