Nonlinear Dynamics and Stochastic Methods: From Neuroscience to Other Biological Applications

“非线性动力学和随机方法：从神经科学到其他生物学应用”会议将于2014年3月10日至12日在匹兹堡大学举行。近年来，生物数据的爆炸式增长需要新的定量方法来解释和分析。与此同时，政府和国立大学都集中精力培养具有数学、统计学和生物学跨学科专业知识的新一代研究人员。然而，对这种专业技能的需求仍然没有得到满足，因为很少有数学和生物科学家接受过这两方面的培训。会议将汇集高级和初级科学家，报告在数学神经科学中被证明成功的理论方法，并鼓励其传播和应用于计算医学和其他生物领域的建模。在过去的二十年中，在所有生物领域中，神经科学可以说是受定量方法影响最大的。计算模型被用来提出解剖和功能相关的假设，相反，新的实验发现导致了新的数学理论的发展。虽然这些理论的进步大多是由神经系统的研究推动的，但它们足够普遍，可以成功地转化为生物学和医学其他领域的建模问题。会议将结合教育和研究，向年轻的研究人员介绍一个有用和有效的定量生物学数学工具箱，同时展示该领域的最新进展。

The conference "Nonlinear Dynamics and Stochastic Methods: From Neuroscience to Other Biological Applications" will be held at the University of Pittsburgh on March 10-12, 2014. Recent years have witnessed an explosion of biological data that require novel quantitative approaches for their interpretation and analysis. In parallel, concentrated efforts were made by both the government and the national universities to educate a new generation of researchers with interdisciplinary expertise in mathematics, statistics and biology. However, the demand for such specialized skills is still to be met as few mathematical and biological scientists are trained in both topics. The meeting will bring together a mix of senior and junior scientists to report on theoretical methods that proved successful in mathematical neuroscience, and to encourage their dissemination and application to modeling in computational medicine and other biological fields.In the past two decades, among all biological fields, neuroscience is arguably the most influenced by quantitative methods. Computational models were used to propose anatomical- and functional-related hypotheses and, conversely, novel experimental findings have led to the development of new mathematical theories. While most of these theoretical advancements were driven by the investigation of the nervous system, they are general enough to be successfully translated to modeling problems in other areas of biology and medicine. The meeting will integrate education and research by introducing young investigators to a useful and effective mathematical toolbox for quantitative biology, while presenting at the same time the most recent advancements in the field.