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.
Origin : Files produced by the author(s)