Stephen Hawking says that the next 21st century will be the century of complexity and indeed now Systems Biology or Medicine means dealing with complexity. This reality is that a huge amount of biological or physiological data, emerging from different -omics sources on very different scales, from genome to physiome, exceeds our abilities to analyse this data. Effective progress in research can be obtained only by merging experimental data mining with modeling and Big Data analysis in the frame of Quantitative Physiology. Indeed, recent advances in applied mathematics and nonlinear dynamics as well as in the development of new machine-learning algorithms, such as deep learning methods, have enabled understanding real mechanisms underlying observed complex phenomena. On the other hand, computational modeling is cheaper and faster than real experiments.
This Research Topic aims to provide state-of-the-art review of multi-scale modeling and data analysis to investigate the function in living systems, organisms, organ systems, organs, cells, and biomolecules carrying out the chemical or physical functions that exist in a living system. This topic is devoted to set a paradigm for quantitative physiology by integrating biology, mathematics, physics or informatics. In particular, new computational approaches allow us to translate methodology between sciences. New modeling approaches include construction of sophisticated models of gene expression, transcriptional regulation. calcium signaling, neural and glial dynamics, immune and autoimmune dynamics or evolutionary processes and games. Especially interesting is a recent discovery of new dynamical regimes in complex physiological systems including synchronization in complex or multiplex networks, emergence of collective or chimera states, dynamics induced by Levy noise, critical phenomena and delayed bifurcations, or other forms of complex dynamics.
A central concept of this topic is a generation and processing of information. As an example, recently it was shown that glial networks do not only support neural ones but take an active role in mediation of information transfer between neurons. Genetic systems have been proven to be more intelligent as it was assumed. It was shown that neural networks, such as a classical classifying or associative perceptron can be implemented even on the scale of a simple genetic network inside the cell.
Analysis of Big Data in Quantitative Physiology would be impossible without development of new methods of statistical analysis. Hence, we also welcome papers on new trends in approximate Bayesian computation or new network or graph analysis methods for the identification of novel bio-markers or functions.
This Research Topic welcomes all types of articles.
Stephen Hawking says that the next 21st century will be the century of complexity and indeed now Systems Biology or Medicine means dealing with complexity. This reality is that a huge amount of biological or physiological data, emerging from different -omics sources on very different scales, from genome to physiome, exceeds our abilities to analyse this data. Effective progress in research can be obtained only by merging experimental data mining with modeling and Big Data analysis in the frame of Quantitative Physiology. Indeed, recent advances in applied mathematics and nonlinear dynamics as well as in the development of new machine-learning algorithms, such as deep learning methods, have enabled understanding real mechanisms underlying observed complex phenomena. On the other hand, computational modeling is cheaper and faster than real experiments.
This Research Topic aims to provide state-of-the-art review of multi-scale modeling and data analysis to investigate the function in living systems, organisms, organ systems, organs, cells, and biomolecules carrying out the chemical or physical functions that exist in a living system. This topic is devoted to set a paradigm for quantitative physiology by integrating biology, mathematics, physics or informatics. In particular, new computational approaches allow us to translate methodology between sciences. New modeling approaches include construction of sophisticated models of gene expression, transcriptional regulation. calcium signaling, neural and glial dynamics, immune and autoimmune dynamics or evolutionary processes and games. Especially interesting is a recent discovery of new dynamical regimes in complex physiological systems including synchronization in complex or multiplex networks, emergence of collective or chimera states, dynamics induced by Levy noise, critical phenomena and delayed bifurcations, or other forms of complex dynamics.
A central concept of this topic is a generation and processing of information. As an example, recently it was shown that glial networks do not only support neural ones but take an active role in mediation of information transfer between neurons. Genetic systems have been proven to be more intelligent as it was assumed. It was shown that neural networks, such as a classical classifying or associative perceptron can be implemented even on the scale of a simple genetic network inside the cell.
Analysis of Big Data in Quantitative Physiology would be impossible without development of new methods of statistical analysis. Hence, we also welcome papers on new trends in approximate Bayesian computation or new network or graph analysis methods for the identification of novel bio-markers or functions.
This Research Topic welcomes all types of articles.