Identification of periodic attractors in Boolean networks using a priori information

Boolean networks (BNs) have been developed to describe various biological processes, which requires analysis of attractors, the long-term stable states. While many methods have been proposed to detection and enumeration of attractors, there are no methods which have been demonstrated to be theoretically better than the naive method and be practically used for large biological BNs. Here, we present a novel method to calculate attractors based on a priori information, which works much and verifiably faster than the naive method. We apply the method to two BNs which differ in size, modeling formalism, and biological scope. Despite these differences, the method presented here provides a powerful tool for the analysis of both networks. First, our analysis of a BN studying the effect of the microenvironment during angiogenesis shows that the previously defined microenvironments inducing the specialized phalanx behavior in endothelial cells (ECs) additionally induce stalk behavior. We obtain this result from an extended network version which was previously not analyzed. Second, we were able to heuristically detect attractors in a cell cycle control network formalized as a bipartite Boolean model (bBM) with 3158 nodes. These attractors are directly interpretable in terms of genotype-to-phenotype relationships, allowing network validation equivalent to an in silico mutagenesis screen. Our approach contributes to the development of scalable analysis methods required for whole-cell modeling efforts.

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

Motivation: Cancer progression is a complex phenomenon that spans multiple scales from molecular to cellular and intercellular. Simulations can be used to perturb the underlying mechanisms of those systems and to generate hypotheses on novel therapies. We present a new version of PhysiBoSS, a multiscale modelling framework designed to cover multiple temporal and spatial scales, that improves its integration with PhysiCell, decoupling the cell agent simulations with the internal Boolean model in an easy-to-maintain computational framework. Results: PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS, conceived as an add-on that expands the PhysiCell agent-based functionalities with intracellular cell signalling using MaBoSS having a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 successfully reproduces simulations reported in the former PhysiBoSS and expands its functionalities such as using user-defined models and cells' specifications, having mechanistic submodels of substrate internalisation with ODEs and enabling the study of drug synergies. Availability and implementation: PhysiBoSS 2.0 is open-source and publicly available on GitHub (https://github.com/PhysiBoSS/PhysiBoSS) under the BSD 3-clause license. Additionally, a nanoHUB tool has been set up to ease the use of PhysiBoSS 2.0 (https://nanohub.org/tools/pba4tnf/).

Estimating the Parameters of a Stochastic Geometrical Model for Multiphase Flow Images Using Local Measures

This paper presents a new method for estimating the parameters of a stochastic geometric model for multiphase flow image processing using local measures. Local measures differ from global measures in that they are only based on a small part of a binary image and consequently provide different information of certain properties such as area and perimeter. Since local measures have been shown to be helpful in estimating the typical grain elongation ratio of a homogeneous Boolean model, the objective of this study was to use these local measures to statistically infer the parameters of a more complex non-Boolean model from a sample of observations. An optimization algorithm is used to minimize a cost function based on the likelihood of a probability densityof local measurements. The performance of the model is analysed using numerical experiments and real observations. The errors relative to real images of most of the properties of the model-generated images are less than 2%. The covariance and particle size distribution are also calculated and compared.

An ECHO of Cartilage: In Silico Prediction of Combinatorial Treatments to Switch Between Transient and Permanent Cartilage Phenotypes With Ex Vivo Validation

A fundamental question in cartilage biology is: what determines the switch between permanent cartilage found in the articular joints and transient hypertrophic cartilage that functions as a template for bone? This switch is observed both in a subset of OA patients that develop osteophytes, as well as in cell-based tissue engineering strategies for joint repair. A thorough understanding of the mechanisms regulating cell fate provides opportunities for treatment of cartilage disease and tissue engineering strategies. The objective of this study was to understand the mechanisms that regulate the switch between permanent and transient cartilage using a computational model of chondrocytes, ECHO. To investigate large signaling networks that regulate cell fate decisions, we developed the software tool ANIMO, Analysis of Networks with interactive Modeling. In ANIMO, we generated an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 proteins with 200 interactions. We called this model ECHO, for executable chondrocyte. Previously, we showed that ECHO could be used to characterize mechanisms of cell fate decisions. ECHO was first developed based on a Boolean model of growth plate. Here, we show how the growth plate Boolean model was translated to ANIMO and how we adapted the topology and parameters to generate an articular cartilage model. In ANIMO, many combinations of overactivation/knockout were tested that result in a switch between permanent cartilage (SOX9+) and transient, hypertrophic cartilage (RUNX2+). We used model checking to prioritize combination treatments for wet-lab validation. Three combinatorial treatments were chosen and tested on metatarsals from 1-day old rat pups that were treated for 6 days. We found that a combination of IGF1 with inhibition of ERK1/2 had a positive effect on cartilage formation and growth, whereas activation of DLX5 combined with inhibition of PKA had a negative effect on cartilage formation and growth and resulted in increased cartilage hypertrophy. We show that our model describes cartilage formation, and that model checking can aid in choosing and prioritizing combinatorial treatments that interfere with normal cartilage development. Here we show that combinatorial treatments induce changes in the zonal distribution of cartilage, indication possible switches in cell fate. This indicates that simulations in ECHO aid in describing pathologies in which switches between cell fates are observed, such as OA.

Satisfiability Checking of Strategy Logic with Simple Goals

In this paper, we introduce a new method of the satisfiability (SAT) checking for Simple-Goal Strategy Logic (SL[SG]), using symbolic Boolean model encoding and the SAT Modulo Monotonic Theories techniques, which was implemented into the tool SGSAT. To the best of our knowledge, this is the only tool solving the SAT problem for SL[SG]. Its applications include process synthesis, developing controllers as well as automatic planners in multi-agent scenarios.

Investigation of observability property of controlled binary dynamical systems: a logical approach

The property of observability of controlled binary dynamical systems is investigated. A formal definition of the property is given in the language of applied logic of predicates with bounded quantifiers of existence and universality. A Boolean model of the property is built in the form of a quantified Boolean formula accordingly to the Boolean constraints method developed by the authors. This formula satisfies both the logical specification of the property and the equations of the binary system dynamics. Aspects of the proposed approach implementation for the study of the observability property are considered. The technology of checking the feasibility of the property using an applied microservice package is demonstrated in several examples.

Local Measures Distribution for the Estimation of the Elongation Ratio of the Typical Grain in Homogeneous Boolean Models

We introduce a particular localization of the Minkowski functionals to characterize and discriminate different random spatial structures. The aim of this paper is to present a method estimating the typical grain elongation ratio in a homogeneous Boolean model. The use of this method is demonstrated on a range of Boolean models of rectangles featuring fixed and random elongation ratio. An optimization algorithm is performed to determine the elongation ratio which maximize the likelihood function of the probability density associated with the local perimeter measure. Therefore, the elongation ratio of the typical grain can be deduced.

A novel framework for delivering static search capabilities to large textual corpora directly on the Web domain: an implementation for Migne’s Patrologia Graeca

Purpose This study aims to provide a system capable of static searching on a large number of unstructured texts directly on the Web domain while keeping costs to a minimum. The proposed framework is applied to the unstructured texts of Migne’s Patrologia Graeca (PG) collection, setting PG as an implementation example of the method. Design/methodology/approach The unstructured texts of PG have automatically transformed to a read-only not only Structured Query Language (NoSQL) database with a structure identical to that of a representational state transfer access point interface. The transformation makes it possible to execute queries and retrieve ranked results based on a specialized application of the extended Boolean model. Findings Using a specifically built Web-browser-based search tool, the user can quickly locate ranked relevant fragments of texts with the ability to navigate back and forth. The user can search using the initial part of words and by ignoring the diacritics of the Greek language. The performance of the search system is comparatively examined when different versions of hypertext transfer protocol (Http) are used for various network latencies and different modes of network connections. Queries using Http-2 have by far the best performance, compared to any of Http-1.1 modes. Originality/value The system is not limited to the case study of PG and has a generic application in the field of humanities. The expandability of the system in terms of semantic enrichment is feasible by taking into account synonyms and topics if they are available. The system’s main advantage is that it is totally static which implies important features such as simplicity, efficiency, fast response, portability, security and scalability.

Drug target ranking for glioblastoma multiforme

Background Glioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer. Results We use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma. Conclusions Given the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients.