510 Multidisciplinary Alliance in Biosciences: Modeling, Computing, Technology and Clinical Applications
Riccardo Sacco, Politecnico di Milano
Giovanna Guidoboni, Indiana University and Purdue University at Indianapolis
Roderick Melnik, Wilfrid Laurier University
Life sciences are the crossroad for a multitude of disciplines: molecular biology, electrochemistry, mechanics, medicine, statistics, clinics, to name a few, so that a new winning vision for the next decade must rely upon a solid multidisciplinary alliance. Mathematical, statistical and computational modeling and methods have taken a leadership role in this alliance due to a number of factors: (1) mathematics is a universal language that makes the common denominator of scientific disciplines; (2) modeling based on solid biophysical and mathematical assumptions allows to design a virtual laboratory to investigate and compare different scenarios and solutions; (3) advanced computational techniques allow to numerically solve the formulated mathematical model producing the quantitative outcome to compare simulation and experimental data, answer open questions, assess or confute conjectures. In this minisymposium we aim at providing a forum in biosciences to facilitate: (a) break the barriers among disciplines; (b) confront different applications and related modeling approaches, including mechanistic and statistical formulations based on partial and/or ordinary differential equations, as well as other models; (c) illustrate the most recent and advanced computational methods; (d) validate models and methods in the simulation of complex problems. The organzizers welcome contributions in all areas of modelling in biosciences and quantitative analysis in clinical applications and experiments. Subjects of this minisymposium comprise, but are not limited to: theoretical analysis and numerical approximation of differential systems and other models in biosciences and biotechnology; high-performance scientific computing and software implementation; imaging techniques and data analysis with statistical methods.