Modeling and Data Analysis for Complex Systems and Dynamics
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Jianzhong Su
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The University of Texas at Arliington
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USA
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lixia Duan
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North China University of Technology
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Peoples Rep of China
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Pengcheng Xiao
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Kennesaw State University
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USA
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Abstract:
| | Many dynamical systems, from small scale neuronal systems, genomic systems, to large scale ecosystems, are featured by nonlinear and complex patterns in spatial and temporal dimensions. These phenomena that are represented by massive amount of data, carry significant information and regulate down-stream dynamics. Understanding the mechanisms underlying such events by quantitative modeling represents a mathematical challenge of current interest. Yet all these systems share the similar dynamical system issues in ordinary/partial different equation such as bifurcation, stability, oscillations, stochastic noise as well as issues in determining model parameters from experimental data sets and computational errors of the models. This special session offers a forum to exchange the state of the art theoretical advances related to this promising area as well as computational tools. It will foster and encourage communication and interaction between researchers in these directions. The common themes include mathematical models and data analysis, theoretical analysis, computational and statistical methods of dynamical systems and differential equations for the bio-system focused models, as well as applications in brain research. The topics may include but not restrict to:
1. Dynamics and computation of neuronal systems • Modeling and dynamical analysis of biological neurons and neuronal networks. • Generation, encoding and transduction of neuronal signals and patterns. • Modeling and analysis of cognitive information processing mechanisms • Dynamic abnormality in neuronal systems due to diseases.
2. Dynamics of immune systems • Modeling biomedical processes, including tumor growth, cardio-vascular diseases, infection, and healing, mediated by immunologic mechanisms. • Analysis of mathematical models for dynamics features such as instabilities, bifurcations that provide insight into the nature of the underlying bio-physical mechanisms. • Modeling wound healing and inflammatory responses, including cell to cell interactions, foreign body reactions and quantitative as well as qualitative comparison with experimental data.
3. Data analysis and modeling of whole brain activities • Complexity theory applied to brain • Perception, learning and memory functions in brain. • Computational evolutionary biology. • Models, analysis and algorithms in Bioinformatics.
4. Data analysis and modeling in engineering, science, industry and agriculture, in particular these involving water, soil and other natural resources, that develop and utilizes data science methodology to interpret and reduce the complexity and dimensions of data sets that lead to predictive dynamics models. |
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List of approved abstract |
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