AMICI - Scalable numerical simulation and sensitivity analysis of dynamical systems

点击翻译按钮获取中文摘要

Ordinary differential equation (ODE) and differential algebraic equation (DAE) models are important tools in life sciences, engineering and many other research fields. They allow for the integrative analysis of heterogeneous data to further the understanding of dynamical systems. However, the simulation and parameterization of ODE and DAE models requires tailored and easy-to-use tools. To support parameterization of models of ever increasing size, scalability and performance are essential requirements. To this end, we developed the research software AMICI (Advanced Multi-language Interface to CVODES and IDAS) which allows for the efficient and scalable simulation of such models. AMICI builds upon the well-established SUNDIALS solver C library (Hindmarsh et al., 2005), to which it provides an easy-to-use high-level interface (Matlab and Python), and a wide array of additional features relevant to systems biologists as well as researchers of related fields. AMICI is already used in at least 15 research groups and one company.The aim of this project is to make the research software AMICI available for reuse and possible further development beyond its original context, and to establish a quality assurance through a professional community. To achieve this, we will professionalize the software development. We will harmonize the Python, Matlab and C++ interfaces, and improve usability, accessibility and overall quality of the code base. Furthermore, we will increase the versatility of AMICI by extending the support of community standards and implementing general-purpose input formats. To support user-centered development, we will build an active community of users and developers by offering user trainings and developer workshops.To evaluate and improve AMICI, we will use it to study a comprehensive set of published benchmarks. This includes a high-dimensional model of cancer signalling developed in our lab. As AMICI allows to tackle forward and inverse problems for large-scale biochemical processes, this project will contribute -- beyond the pure software and method development -- to novel insights into cellular signal processing and potentially also processes studied in other research fields.