Inferring chemical reaction networks (CRN) from concentration time series is a challenge encouragedby the growing availability of quantitative temporal data at the cellular level. This motivates thedesign of algorithms to infer the preponderant reactions between the molecular species observed ina given biochemical process, and build CRN structure and kinetics models. Existing ODE-basedinference methods such as SINDy resort to least square regression combined with sparsity-enforcingpenalization, such as Lasso. However, we observe that these methods fail to learn sparse modelswhen the input time series are only available in wild type conditions, i.e. without the possibility toplay with combinations of zeroes in the initial conditions. We present a CRN inference algorithmwhich enforces sparsity by inferring reactions in a sequential fashion within a search tree of boundeddepth, ranking the inferred reaction candidates according to the variance of their kinetics on theirsupporting transitions, and re-optimizing the kinetic parameters of the CRN candidates on the wholetrace in a final pass. We show that Reactmine succeeds both on simulation data by retrievinghidden CRNs where SINDy fails, and on two real datasets, one of fluorescence videomicroscopyof cell cycle and circadian clock markers, the other one of biomedical measurements of systemiccircadian biomarkers possibly acting on clock gene expression in peripheral organs, by inferringpreponderant regulations in agreement with previous model-based analyses. The code is available athttps://gitlab.inria.fr/julmarti/crninf/ together with introductory notebooks.

翻译：从浓度时间序列中推断化学反应网络（CRN）是细胞层面定量时间数据日益可获取所推动的一项挑战。这激励了相关算法的设计，旨在推断特定生物过程中观测到的分子物种之间主要发生的反应，并构建CRN结构与动力学模型。现有的基于常微分方程的推断方法（如SINDy）采用最小二乘回归结合稀疏性强制惩罚（如Lasso）。然而，我们观察到，当输入时间序列仅来自野生型条件（即无法利用初始条件中的零组合）时，这些方法无法学习到稀疏模型。我们提出了一种CRN推断算法，该算法通过在有界深度搜索树中按顺序推断反应来强制稀疏性，根据反应候选在支撑转换上的动力学方差对其进行排序，并在最终遍历中重新优化CRN候选的动力学参数。我们证明，Reactmine在模拟数据上能够成功恢复SINDy无法发现的隐藏CRN；在两组真实数据集上——其一为细胞周期与昼夜节律标志物的荧光视频显微镜数据，其二为可能作用于外周器官时钟基因表达的全身性昼夜节律生物标志物的生物医学测量数据——通过推断出与先前基于模型的分析一致的主要调控关系，验证了算法的有效性。代码及入门指南参见https://gitlab.inria.fr/julmarti/crninf/。