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Preprints, Working Papers, ... Year : 2023

Reactmine: a statistical search algorithm for inferring chemical reactions from time series data

Abstract

Inferring chemical reaction networks (CRN) from concentration time series 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 build CRN structure and kinetics models. Existing ODE-based inference methods such as SINDy resort to least square regression combined with sparsity-enforcing penalization, such as Lasso. However, we observe that these methods fail to learn sparse models when the input time series are only available in wild type conditions, i.e. without the possibility to play with combinations of zeroes in the initial conditions. We present a CRN inference 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 on their supporting transitions, and re-optimizing the kinetic parameters of the CRN candidates on the whole trace in a final pass. We show that Reactmine succeeds both on simulation data by retrieving hidden CRNs where SINDy fails, and on two real datasets, one of fluorescence videomicroscopy of cell cycle and circadian clock markers, the other one of biomedical measurements of systemic circadian biomarkers possibly acting on clock gene expression in peripheral organs, by inferring preponderant regulations in agreement with previous model-based analyses. The code is available at https://gitlab.inria.fr/julmarti/crninf/ together with introductory notebooks.
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Dates and versions

hal-03769872 , version 1 (05-09-2022)
hal-03769872 , version 2 (21-01-2023)

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Cite

Julien Martinelli, Jeremy Grignard, Sylvain Soliman, Annabelle Ballesta, François Fages. Reactmine: a statistical search algorithm for inferring chemical reactions from time series data. 2023. ⟨hal-03769872v2⟩
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