Autocatalysis is a deceptively simple concept, referring to the situation that a chemical species $X$ catalyzes its own formation. From the perspective of chemical kinetics, autocatalysts show a regime of super-linear growth. Given a chemical reaction network, however, it is not at all straightforward to identify species that are autocatalytic in the sense that there is a sub-network that takes $X$ as input and produces more than one copy of $X$ as output. The difficulty arises from the need to distinguish autocatalysis e.g. from the superposition of a cycle that consumes and produces equal amounts of $X$ and a pathway that produces $X$. To deal with this issue, a number of competing notions, such as exclusive autocatalysis and autocatalytic cycles, have been introduced. A closer inspection of concepts and their usage by different authors shows, however, that subtle differences in the definitions often makes conceptually matching ideas difficult to bring together formally. In this contribution we make some of the available approaches comparable by translating them into a common formal framework that uses integer hyperflows as a basis to study autocatalysis in large chemical reaction networks. As an application we investigate the prevalence of autocatalysis in metabolic networks.
Bridging two theoretical frameworks of autocatalysis: RAF sets and stoichiometric autocatalysis
Leipzig University（莱比锡大学）；Max Planck Institute for Mathematics in the Sciences（马普科学数学研究所）；University of Vienna（维也纳大学）；Universidad National de Colombia（哥伦比亚国立大学）；Santa Fe Institute（圣塔菲研究所）
Minimality in Reflexive and Stoichiometric Autocatalysis
Leipzig University（莱比锡大学）；Max Planck Institute for Mathematics in the Sciences（马克斯·普朗克科学数学研究所）；University of Vienna（维也纳大学）；Universidad Nacional de Colombia（哥伦比亚国立大学）；University of Copenhagen（哥本哈根大学）
Enumeration of Autocatalytic Subsystems in Large Chemical Reaction Networks
Leipzig University（莱比锡大学）；Interdisciplinary Center for Bioinformatics, Leipzig University（莱比锡大学跨学科生物信息中心）；Zuse School for Embedded and Composite Artificial Intelligence (SECAI)（泽苏嵌入式与复合人工智能学院）；Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University（莱比锡大学可扩展数据分析与人工智能中心）；Max Planck Institute for Mathematics in the Sciences（马普科学数学研究所）；University of Vienna（维也纳大学）；University of Copenhagen（哥本哈根大学）；Facultad de Ciencias, Universidad Nacional de Colombia（哥伦比亚国立大学理学院）；Santa Fe Institute（圣塔菲研究所）
本文提出了一种高效算法，用于在大型化学反应网络中枚举自催化子系统和核心，通过 bipartite König 表示法推导出充分条件，并展示了在大肠杆菌等生物体中的应用。
Biomass transfer on autocatalytic reaction network: a delay differential equation formulation
Institute of Molecular Biology, Academia Sinica（中央研究院分子生物学研究所）；National Center for Theoretical Sciences, Physics Division（国家理论科学中心物理组）；Sarafan Chemistry, Engineering, and Medicine for Human Health Institute, Stanford University（斯坦福大学萨弗兰化学、工程与人类健康医学研究所）；Howard Hughes Medical Institute, Stanford University（霍华德·休斯医学研究所）
Autocatalytic Cores in Reaction Networks with Explicit Catalysis
Leipzig University（莱比锡大学）；Max Planck Institute for Mathematics in the Sciences（马克斯·普朗克科学促进学会数学研究所）；University of Vienna（维也纳大学）；Universidad National de Colombia（哥伦比亚国立大学）；Santa Fe Institute（圣塔菲研究所）
An All-Encompassing Global Convergence Result for Processive Multisite Phosphorylation Systems
磷酸化，即酶介导的磷酸基团添加到分子上，是生物学中普遍存在的化学机制。多位点磷酸化，即磷酸基团添加到单个分子的多个位点，可以是分布性的或进行性的。分配系统可以是稳定的，而过程系统最近被证明是全局稳定的。然而，这种全局收敛结果仅被证明是进行性磷酸化/去磷酸化的特定机制（即所有催化反应都是可逆的）。因此，我们将这一结果推广到允许进行性磷酸化网络，其中每个反应可能是不可逆的，也考虑到可能的产物抑制。我们通过定义一个包罗万象的进程网络来实现这一点，该网络封装了所有这些方案，然后诉诸于Marcondes de Freitas，Wiuf和Feliu的最新结果，这些结果通过单调系统理论和网络/图约简（对应于去除中间复合物）来断言全局收敛。我们的研究结果形成了一个案例研究的问题时，全球收敛的反应和/或中间复合物被添加到或从网络中删除。
Rule-Based Gillespie Simulation of Chemical Systems
University of Southern Denmark（南丹麦大学）；Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学）；Bielefeld University（比勒费尔德大学）；University of Vienna（维也纳大学）；Leipzig University（莱比锡大学）；Max Planck Institute for Mathematics in the Sciences（马克斯·普朗克科学数学研究所）；Universidad National de Colombia（哥伦比亚国立大学）；University of Copenhagen（哥本哈根大学）
