Inferring chemical reaction networks (CRN) from time series data is a challenge encouraged by the growing availability of quantitative temporal data at the cellular level. This motivates the design of algorithms to infer the preponderant reactions between the molecular species observed in a given biochemical process, and help to build CRN model structure and kinetics. Existing ODE-based inference methods such as SINDy resort to least square regression combined with sparsity-enforcing penalization, such as Lasso. However, when the input time series are only available in wild type conditions in which all reactions are present, we observe that current methods fail to learn sparse models. Results: We present Reactmine, a CRN learning algorithm which enforces sparsity by inferring reactions in a sequential fashion within a search tree of bounded depth, ranking the inferred reaction candidates according to the variance of their kinetics, and re-optimizing the CRN kinetic parameters on the whole trace in a final pass to rank the inferred CRN candidates. We first evaluate its performance on simulation data from a benchmark of hidden CRNs, together with algorithmic hyperparameter sensitivity analyses, and then on two sets of real experimental data: one from protein fluorescence videomicroscopy of cell cycle and circadian clock markers, and one from biomedical measurements of systemic circadian biomarkers possibly acting on clock gene expression in peripheral organs. We show that Reactmine succeeds both on simulation data by retrieving hidden CRNs where SINDy fails, and on the two real datasets by inferring reactions in agreement with previous studies.
翻译:从时间序列数据中推断化学反应网络(CRN)是一个挑战,由蜂窝一级数量性时间数据不断增多的可得性鼓励。这促使设计算法,以推断在特定生物化学过程中观察到的分子物种之间的主要反应,帮助建立CRN模型结构和动能。现有的基于OD的推论方法,如Sindy采用最小平方回归法,加上宽度惩罚,如Lasso。然而,当输入时间序列仅在所有反应都存在的野性类型条件下提供时,我们观察到当前方法无法学习稀释模型。结果:我们展示Reactmine,CRN学习算法,通过在封闭深度的搜索树上以顺序推导反应,根据运动变异性将推断的反应对象排到最低平方位,并重新优化CRCRN的全程动能参数,通过推断的CRRN候选人在最后分级中排名。我们首先从一个隐藏的CRNSMER的基质反应基准中评估其模拟数据的运行情况,同时从一个真实的CRISBER数据序列中,从一个实验性机极的磁标上进行一次的磁标分析。