Trace Simplifications Preserving Temporal Logic Formulae with Case Study in a Coupled Model of the Cell Cycle and the Circadian Clock

Abstract

Abstract. Calibrating dynamical models on experimental data time series is a central task in computational systems biology. When numerical values for model parameters can be found to fit the data, the model can be used to make predictions, whereas the absence of any good fit may suggest to revisit the structure of the model and gain new insights in the biology of the system. Temporal logic provides a formal framework to deal with imprecise data and specify a wide variety of dynamical behaviors. It can be used to extract information from numerical traces coming from either experimental data or model simulations, and to specify the expected behaviors for model calibration. The computation time of the different methods depends on the number of points in the trace so the question of trace simplification is important to improve their performance. In this paper we study this problem and provide a series of trace simplifications which are correct to perform for some common temporal logic formulae. We give some general soundness theorems, and apply this approach to period and phase constraints on the circadian clock and the cell cycle. In this application, temporal logic patterns are used to compute the relevant characteristics of the experimental traces, and to measure the adequacy of the model to its specification on simulation traces. Speed-ups by several orders of magnitude are obtained by trace simplification even when produced by smart numerical integration methods.

Model-based investigation of the circadian clock and cell cycle coupling in mouse embryonic fibroblasts: Prediction of RevErb-α up-regulation during mitosis

“…More precisely, we use the temporal logic formula patterns described in [20,21] and implemented in our modeling software Biocham [19] to specify the constraints about the successive peaks of concentrations between either the same molecular species (period constraints) or different molecular species (phase constraints). Biocham then provides commands for automatically…”

“…This computational model has been built using the Biocham modeling software [19] for 1. importing and exporting models in SBML, and modeling the molecular interactions of the coupling of the models, 2. specifying the observed behavior in quantitative temporal logic using pattern formulae for periods and phases [20,21], 3. searching parameter values [22] and measuring robustness and parameter sensitivity indices [23] with respect to the temporal logic specification of the dynamical behavior 1 .…”

On Robustness Computation and Optimization in BIOCHAM-4

“…However, it is worth noticing that even in the case of bound constraints, the validity domain of a temporal formula can contain an exponential number of polyhedra in the number of free variables in the FO-LTL(R lin ) formula [5]. The issue of trace simplification, such as keeping in the trace only the time points that are local extrema for one state variable, is also important to reduce computation time and justified in a number of practical cases [15].…”