AMICI - Scalable numerical simulation and sensitivity analysis of dynamical systems

常微分方程(ODE)和微分代数方程(DAE)模型是生命科学、工程等众多研究领域的重要工具。它们允许对不同种类的数据进行综合分析，以加深对动力系统的理解。然而，ODE和DAE模型的模拟和参数化需要量身定制且易于使用的工具。为了支持不断增大的模型的参数化，可伸缩性和性能是基本要求。为此，我们开发了研究软件AMICI(高级多语言接口到CVODES和IDAS)，它允许对此类模型进行高效和可扩展的模拟。Amici建立在成熟的日规解算器C库(Hindmarsh等人，2005)的基础上，它为其提供了易于使用的高级接口(MatLab和Python)，以及与系统生物学家和相关领域的研究人员相关的一系列附加功能。AMICI已经在至少15个研究小组和一家公司使用。这个项目的目的是使研究软件AMICI可供重复使用，并可能在其原始环境之外进行进一步开发，并通过专业社区建立质量保证。为了实现这一点，我们将使软件开发专业化。我们将协调Python、MatLab和C++接口，提高代码库的可用性、可访问性和整体质量。此外，我们将通过扩大对社区标准的支持和实施通用输入格式来增加AMICI的多功能性。为了支持以用户为中心的开发，我们将通过提供用户培训和开发人员工作坊，建立一个活跃的用户和开发人员社区。为了评估和改进AMICI，我们将利用它来研究一套全面的已发布基准。这包括我们实验室开发的癌症信号的高维模型。由于AMICI允许解决大规模生化过程的正反问题，该项目将有助于--超越纯粹的软件和方法开发--对细胞信号处理的新见解，以及潜在的在其他研究领域研究的过程。

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.