Efficient Modeling and Simulation of Space-Dependent Biological Systems

Rosati, Elise; Madec, Morgan; Kammerer, Jean-Baptiste; Hébrard, Luc; Lallement, Christophe; Haiech, Jacques

doi:10.1089/cmb.2018.0012

Cited by 4 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other computational models for qualitative description of interactions and behavioral logic in BRNs involve Petri nets (Mazur, 2012;Sun et al, 2012;Schnoerr et al, 2017), probabilistic Boolean networks (Mizera et al, 2014;Liu et al, 2012;Mazur, 2012), continuous time recurrent neural networks (Berrones et al, 2016), and agent based model (ABM) . Hardware description language (HDL) which is normally used to describe logic of electronic circuits is adopted for the case of spatially-dependent biological systems described by PDEs in (Rosati et al, 2018). Multi-parameter space was mapped to 1D manifold in (Zimmer et al, 2014).…”

Section: Other Models Of Brnsmentioning

confidence: 99%

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

2019

View full text Add to dashboard Cite

The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.

show abstract