Cell Painting and Chemical Structure Read-Across Can Complement Each Other for Rat Acute Oral Toxicity Prediction in Chemical Early Derisking.

Early derisking decisions in the development of new chemical compounds enable the identification of novel chemical candidates with improved safety profiles. In vivo studies are traditionally conducted in the early assessment of acute oral toxicity of crop protection products to avoid compounds, which are considered "very acutely toxic", with an in vivo lethal dose of 50% (LD50) ≤ 60 mg/kg body weight. Those studies are lengthy and costly and raise ethical concerns, catalyzing the use of nonanimal alternatives.

Stacked Generalization with Applicability Domain Outperforms Simple QSAR on in Vitro Toxicological Data.

The development of in silico tools able to predict bioactivity and toxicity of chemical substances is a powerful solution envisioned to assess toxicity as early as possible. To enable the development of such tools, the ToxCast program has generated and made publicly available in vitro bioactivity data for thousands of compounds. The goal of the present study is to characterize and explore the data from ToxCast in terms of Machine Learning capability.

G-Networks to Predict the Outcome of Sensing of Toxicity.

G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through the prediction of their bioactivity from the compounds' physico-chemical structure, and propose that it be automated using machine learning (ML) techniques. Specifically the Random Neural Network is shown to be an effective analytical tool to this effect, and the approach is illustrated and compared with several ML techniques.

On circuit functionality in Boolean networks.

It has been proved, for several classes of continuous and discrete dynamical systems, that the presence of a positive (resp. negative) circuit in the interaction graph of a system is a necessary condition for the presence of multiple stable states (resp. a cyclic attractor).

Regulatory networks.

The usefulness of mathematical models for the biological regulatory networks relies on the predictive capability of the models in order to suggest interesting hypotheses and suitable biological experiments. All mathematical frameworks dedicated to biological regulatory networks must manage a large number of abstract parameters, which are not directly measurable in the cell. The cornerstone to establish predictive models is the identification of the possible parameter values.

Boolean models of biosurfactants production in Pseudomonas fluorescens.

Cyclolipopeptides (CLPs) are biosurfactants produced by numerous Pseudomonas fluorescens strains. CLP production is known to be regulated at least by the GacA/GacS two-component pathway, but the full regulatory network is yet largely unknown. In the clinical strain MFN1032, CLP production is abolished by a mutation in the phospholipase C gene (plcC) and not restored by plcC complementation.

On the impact of the distance between two genes on their interaction curve.

We analyze a basic building block of gene regulatory networks using a stochastic/geometric model in search of a mathematical backing for the discrete modeling frameworks. We consider a network consisting only of two interacting genes: a source gene and a target gene. The target gene is activated by the proteins encoded by the source gene.

Stable periodicity and negative circuits in differential systems.

We provide a counter-example to a conjecture of René Thomas on the relationship between negative feedback circuits and stable periodicity in ordinary differential equation systems (Kaufman et al. in J Theor Biol 248:675-685, 2007). We also prove a weak version of this conjecture by using a theorem of Snoussi.

Topology-based abstraction of complex biological systems: application to the Golgi apparatus.

Many complex cellular processes involve major changes in topology and geometry. We have developed a method using topology-based geometric modelling in which the edge labels of an n-dimensional generalized map (a subclass of graphs) represent the relations between neighbouring biological compartments. We illustrate our method using two topological models of the Golgi apparatus.

Validation of biological models with temporal logic and Timed Hybrid Petri Nets.

The Hybrid Functional Petri Nets (HFPN) formalism has shown its convenience for modelling biological systems. This class of models has been fruitfully applied in biology but the remarkable expressiveness of HFPN often leads to incomplete validations. In this paper, we propose a logical framework for Timed Hybrid Petri Nets (THPN), a sub-class of HFPN.

Analysing gene regulatory networks by both constraint programming and model-checking.

In this article, we propose a formal method to analyse gene regulatory networks (GRN). The dynamics of such systems is often described by an ordinary differential equation system, but has also been abstracted into a discrete transition system. This modeling depends on parameters for which different values are possible.

Symbolic modeling of genetic regulatory networks.

Understanding the functioning of genetic regulatory networks supposes a modeling of biological processes in order to simulate behaviors and to reason on the model. Unfortunately, the modeling task is confronted to incomplete knowledge about the system. To deal with this problem we propose a methodology that uses the qualitative approach developed by Thomas.

Epigenetic acquisition of inducibility of type III cytotoxicity in P. aeruginosa.

Background: Pseudomonas aeruginosa, an opportunistic pathogen, is often encountered in chronic lung diseases such as cystic fibrosis or chronic obstructive pneumonia, as well as acute settings like mechanical ventilation acquired pneumonia or neutropenic patients. It is a major cause of mortality and morbidity in these diseases. In lungs, P.

Epigenesis and dynamic similarity in two regulatory networks in Pseudomonas aeruginosa.

Mucoidy and cytotoxicity arise from two independent modifications of the phenotype of the bacterium Pseudomonas aeruginosa that contribute to the mortality and morbidity of cystic fibrosis. We show that, even though the transcriptional regulatory networks controlling both processes are quite different from a molecular or mechanistic point of view, they may be identical from a dynamic point of view: epigenesis may in both cases be the cause of the acquisition of these new phenotypes. This was highlighted by the identity of formal graphs modelling these networks.

Application of formal methods to biological regulatory networks: extending Thomas' asynchronous logical approach with temporal logic.

Based on the discrete definition of biological regulatory networks developed by René Thomas, we provide a computer science formal approach to treat temporal properties of biological regulatory networks, expressed in computational tree logic. It is then possible to build all the models satisfying a set of given temporal properties. Our approach is illustrated with the mucus production in Pseudomonas aeruginosa.