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https://umu.diva-portal.org/smash/project.jsf?pid=project:1217
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Title [sv]
Kartläggning och modellering av informationsflöden i levande system
Title [en]
Mapping and modeling of information flow in living systems
Abstract [sv]
Where there is life, there is information: nerves transmit information, cells process information, information flows in ecosystems, information is encoded in the DNA. To comprehend the important role information plays in living systems, we must better understand how information flows through biological and social systems. Scientists and engineers use information theory to quantify communication between a signaler and a receiver and network theory to analyze how information spreads through a system. These tools give us insight into systems with simple interactions without feedback. But most interactions in biological and social systems are complex, such as the interactions between quorum-sensing bacteria and their environments on an evolutionary timescale or between strategic individuals who seek to avoid infection during an epidemic. These interactions call for new tools that capture the integrative and strategic role of information in living systems.I combine mechanistic modeling with powerful tools for revealing organization in empirical data for causal understanding and scientific discovery and focus on the three research questions:1. How does strategic decision-making change the flow of information through living systems?2. How can we make use of today´s data explosion to comprehend the flow of information through living systems?3. How can we use information theory to quantify the information generated by natural selection?
Principal Investigator
Rosvall, Martin
Umeå University
Coordinating organisation
Umeå University
Funder
Vetenskapsrådet
Period
2013-01-01 - 2016-12-31
National Category
Condensed Matter Physics
Bioinformatics and Systems Biology
Evolutionary Biology
Identifiers
DiVA, id: project:1217
Project, id: 2012-03729_VR
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Condensed Matter Physics
Bioinformatics and Systems Biology
Evolutionary Biology
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