Hierarchical study of Guyton Circulatory Model

National audienceThis article presents an initial study of the Guyton Circulatory Model using BioRica. This model consists of 18 connected modules, each of which caracterise a separate physiological subsystem. We have focused the present analysis in the Renin- Angiotensin-Aldosterone System (RAAS). The use of BioRica allowed us to build an hierarchical model for this system by means of directly mapping modules to BioRica nodes. The results of each node were validated by comparison with published results

Modeling Stochastic Switched Systems with BioRica

National audienceModeling physycal and biological dynamic systems needs to combine different types of models in a non-ambiguous way. We present an approach to integrate continuous, discrete, stochastic, deterministic and non-deterministic elements by using Transition Systems theory, reuse, composition of models, and the framework BioRica. The systems are described by interacting con- tinuous and discrete models, and in addition continuous models are decomposed into two compo- nents: controlled and controller model. We define Stochastic Switched Systems whose continuous dynamics is modeled by differential equations and its discrete dynamics by transition systems, al- lowing stochastic and non-deterministic behaviours. We illustrated the use of our approach with examples of intrinsically and approximated hybrid systems. Our approach allows us to give a first step to integrate and to extend models of complex systems, such as cell differentiation

Genome-wide identification of new Wnt/β-catenin target genes in the human genome using CART method

<p>Abstract</p> <p>Background</p> <p>The importance of <it>in silico </it>predictions for understanding cellular processes is now widely accepted, and a variety of algorithms useful for studying different biological features have been designed. In particular, the prediction of <it>cis </it>regulatory modules in non-coding human genome regions represents a major challenge for understanding gene regulation in several diseases. Recently, studies of the Wnt signaling pathway revealed a connection with neurodegenerative diseases such as Alzheimer's. In this article, we construct a classification tool that uses the transcription factor binding site motifs composition of some gene promoters to identify new Wnt/β-catenin pathway target genes potentially involved in brain diseases.</p> <p>Results</p> <p>In this study, we propose 89 new Wnt/β-catenin pathway target genes predicted <it>in silico </it>by using a method based on multiple Classification and Regression Tree (CART) analysis. We used as decision variables the presence of transcription factor binding site motifs in the upstream region of each gene. This prediction was validated by RT-qPCR in a sample of 9 genes. As expected, LEF1, a member of the T-cell factor/lymphoid enhancer-binding factor family (TCF/LEF1), was relevant for the classification algorithm and, remarkably, other factors related directly or indirectly to the inflammatory response and amyloidogenic processes also appeared to be relevant for the classification. Among the 89 new Wnt/β-catenin pathway targets, we found a group expressed in brain tissue that could be involved in diverse responses to neurodegenerative diseases, like Alzheimer's disease (AD). These genes represent new candidates to protect cells against amyloid β toxicity, in agreement with the proposed neuroprotective role of the Wnt signaling pathway.</p> <p>Conclusions</p> <p>Our multiple CART strategy proved to be an effective tool to identify new Wnt/β-catenin pathway targets based on the study of their regulatory regions in the human genome. In particular, several of these genes represent a new group of transcriptional dependent targets of the canonical Wnt pathway. The functions of these genes indicate that they are involved in pathophysiology related to Alzheimer's disease or other brain disorders.</p

Genome-wide annotation of human Wnt target genes using CART: toward treatments of degenerative diseases

International audienceThe Wnt/beta signaling pathway has an important role in neuroprotection and bone formation. The activation of this pathway induces the accumulation of beta-catenin in the nucleus of the cell, which interacts with a TCF/LEF transcription factor to activate the expression of the so called Wnt target genes. Some proteins implied in neuroprotection such as CamK4, and other ones related to bone formation, e.g. Runx2, are Wnt targets. Modeling the stimulatory effects of the Wnt pathway on neuroprotection functions and bone formation allow us to analyze in silico the physiological responses to treatments of degenerative disorders such as Alzheimer's disease and osteoporosis. To obtain in-silico new Wnt target genes and insights about regulation, we built an statistical method based on CART. It is trained by the information about the transcription factor binding site motifs in the nearby regions of known Wnt target genes. Between the new Wnt target, we found genes that protect cells against amyloid beta toxicity: e.g. calcium/calmodulin-dependent protein kinase IV (CamK4), for which there exist strong evidences for up-regulation in response to both Wnt ligands and lithium

Modeling and simulation of Hybrid Systems and Cell factory applications

Les Fonctions biologiques sont le résultat de l'interaction de beaucoup de processus, avec differents objectives, complexités, niveaux d'hiérarchie, et changements de conditions que modi ent le comportement de systèmes. Nous utilisons des équations diferenciales ou dynamiques plus générales, et Stochastic Systèmes de Transition pour décrire la dynamique de changements des modèles. La composition, réconciliation et reutilisation des modèles nous permettent d'obtenir des descriptions de systèmes biologiques complètes et compatibles et leur combiner. Notre spéci cation de Systèmes Hybrides avec BioRica assures l'intégrité de modèles, et implement notre approche. Nous appliquons notre approche pour décrire in-silico deux systèmes: la dynamique de la fermentation du vin, et des décisions cellulaires associées à la formation de tissu d'os.The main aim of this thesis is to develop an approach that allows us to describe biological systems with theoretical sustenance and good results in practice. Biological functions are the result of the interaction of many processes, that connect different hierarchy levels going from macroscopic to microscopic level. Each process works in different way, with its own goal, complexity and hierarchy level. In addition, it is common to observe that changes in the conditions, such as nutrients or environment, modify the behavior of the systems. So, to describe the behavior of a biological system over time, it is convenient to combine different types of models: continuous models for gradual changes, discrete models for instantaneous changes, deterministic models for completely predictable behaviors, and stochastic or non- deterministic models to describe behaviors with imprecise or incomplete information. In this thesis we use the theory of Composition and Hybrid Systems as basis, and the BioRica framework as tool to model biological systems and analyze their emergent properties in silico. With respect to Hybrid Systems, we considered continuous models given by sets of differential equations or more general dynamics. We used Stochastic Transition Systems to describe the dynamics of model changes, allowing coe fficient switches that control the parameters of the continuous model, and strong switches that choose different models. Composition, reconciliation and reusing of models allow us to build complete and consistent descriptions of complex biological systems by combining them. Compositions of hybrid systems are hybrid systems, and the re nement of a model forming part of a composed system results in a re nement of the composed system. To implement our approach ideas we complemented the theory of our approach with the improving of the BioRica framework. We contributed to do that giving a BioRica speci cation of Hybrid Systems that assures integrity of models, allowing composition, reconciliation, and reuse of models with SBML speci cation. We applied our approach to describe two systems: wine fermentation kinetics, and cell fate decisions leading to bone and fat formation. In the case of wine fermentation, we reused known models that describe the responses of yeasts cells to different temperatures, quantities of resources and toxins, and we reconciled these models choosing the model with best adjustment to experimental data depending on the initial conditions and fermentation variable. The resulting model can be applied to avoid process problems as stuck and sluggish fermentations. With respect to cell fate decisions the idea is very ambitious. By using accurate models to predict the bone and fat formation in response to activation of pathways such as the Wnt pathway, and changes of conditions affecting these functions such as increments in Homocysteine, one can analyze the responses to treatments for osteoporosis and other bone mass disorders. We think that here we are giving a first step to obtain in silico evaluations of medical treatments before testing them in vivo or in vitro

Modeling and simulation of Hybrid Systems and Cell factory applications

The main aim of this thesis is to develop an approach that allows us to describe biological systems with theoretical sustenance and good results in practice. Biological functions are the result of the interaction of many processes, that connect different hierarchy levels going from macroscopic to microscopic level. Each process works in different way, with its own goal, complexity and hierarchy level. In addition, it is common to observe that changes in the conditions, such as nutrients or environment, modify the behavior of the systems. So, to describe the behavior of a biological system over time, it is convenient to combine different types of models: continuous models for gradual changes, discrete models for instantaneous changes, deterministic models for completely predictable behaviors, and stochastic or non- deterministic models to describe behaviors with imprecise or incomplete information. In this thesis we use the theory of Composition and Hybrid Systems as basis, and the BioRica framework as tool to model biological systems and analyze their emergent properties in silico. With respect to Hybrid Systems, we considered continuous models given by sets of differential equations or more general dynamics. We used Stochastic Transition Systems to describe the dynamics of model changes, allowing coe fficient switches that control the parameters of the continuous model, and strong switches that choose different models. Composition, reconciliation and reusing of models allow us to build complete and consistent descriptions of complex biological systems by combining them. Compositions of hybrid systems are hybrid systems, and the re nement of a model forming part of a composed system results in a re nement of the composed system. To implement our approach ideas we complemented the theory of our approach with the improving of the BioRica framework. We contributed to do that giving a BioRica speci cation of Hybrid Systems that assures integrity of models, allowing composition, reconciliation, and reuse of models with SBML speci cation. We applied our approach to describe two systems: wine fermentation kinetics, and cell fate decisions leading to bone and fat formation. In the case of wine fermentation, we reused known models that describe the responses of yeasts cells to different temperatures, quantities of resources and toxins, and we reconciled these models choosing the model with best adjustment to experimental data depending on the initial conditions and fermentation variable. The resulting model can be applied to avoid process problems as stuck and sluggish fermentations. With respect to cell fate decisions the idea is very ambitious. By using accurate models to predict the bone and fat formation in response to activation of pathways such as the Wnt pathway, and changes of conditions affecting these functions such as increments in Homocysteine, one can analyze the responses to treatments for osteoporosis and other bone mass disorders. We think that here we are giving a first step to obtain in silico evaluations of medical treatments before testing them in vivo or in vitro.Les Fonctions biologiques sont le résultat de l'interaction de beaucoup de processus, avec differents objectives, complexités, niveaux d'hiérarchie, et changements de conditions que modi ent le comportement de systèmes. Nous utilisons des équations diferenciales ou dynamiques plus générales, et Stochastic Systèmes de Transition pour décrire la dynamique de changements des modèles. La composition, réconciliation et reutilisation des modèles nous permettent d'obtenir des descriptions de systèmes biologiques complètes et compatibles et leur combiner. Notre spéci cation de Systèmes Hybrides avec BioRica assures l'intégrité de modèles, et implement notre approche. Nous appliquons notre approche pour décrire in-silico deux systèmes: la dynamique de la fermentation du vin, et des décisions cellulaires associées à la formation de tissu d'os

Modeling and simulation of hybrid systems and cell factory applications

Les fonctions biologiques sont le résultat de l'interaction de beaucoup de processus, avec différents objectifs, complexités, niveaux de hiérarchie, et changements de conditions que modifient le comportement de systèmes. Nous utilisons des équations différentielles ou dynamiques plus générales, et systèmes stochastiques de transition pour décrire la dynamique de changements des modèles. La composition, réconciliation et réutilisation des modèles nous permettent d'obtenir des descriptions de systèmes biologiques complètes et compatibles et leur combiner. Notre spécification de systèmes hybrides avec BioRica assure l'intégrité de modèles, et implémente notre approche. Nous appliquons notre approche pour décrire in-silico deux systèmes: la dynamique de la fermentation du vin, et des décisions cellulaires associées à la formation de tissu d'os.The main aim of this thesis is to develop an approach that allows us to describe biological systems with theoretical sustenance and good results in practice. Biological functions are the result of the interaction of many processes, that connect different hierarchy levels going from macroscopic to microscopic level. Each process works in different way, with its own goal, complexity and hierarchy level. In addition, it is common to observe that changes in the conditions, such as nutrients or environment, modify the behavior of the systems. So, to describe the behavior of a biological system over time, it is convenient to combine different types of models: continuous models for gradual changes, discrete models for instantaneous changes, deterministic models for completely predictable behaviors, and stochastic or non- deterministic models to describe behaviors with imprecise or incomplete information. In this thesis we use the theory of Composition and Hybrid Systems as basis, and the BioRica framework as tool to model biological systems and analyze their emergent properties in silico.With respect to Hybrid Systems, we considered continuous models given by sets of differential equations or more general dynamics. We used Stochastic Transition Systems to describe the dynamics of model changes, allowing cofficient switches that control the parameters of the continuous model, and strong switches that choose different models. Composition, reconciliation and reusing of models allow us to build complete and consistent descriptions of complex biological systems by combining them. Compositions of hybrid systems are hybrid systems, and the refinement of a model forming part of a composed system results in a refinement of the composed system. To implement our approach ideas we complemented the theory of our approach with the improving of the BioRica framework. We contributed to do that giving a BioRica specification of Hybrid Systems that assures integrity of models, allowing composition, reconciliation, and reuse of models with SBML specification.We applied our approach to describe two systems: wine fermentation kinetics, and cell fate decisions leading to bone and fat formation. In the case of wine fermentation, we reused known models that describe the responses of yeasts cells to different temperatures, quantities of resources and toxins, and we reconciled these models choosing the model with best adjustment to experimental data depending on the initial conditions and fermentation variable. The resulting model can be applied to avoid process problems as stuck and sluggish fermentations. With respect to cell fate decisions the idea is very ambitious. By using accurate models to predict the bone and fat formation in response to activation of pathways such as the Wnt pathway, and changes of conditions affecting these functions such as increments in Homocysteine, one can analyze the responses to treatments for osteoporosis and other bone mass disorders. We think that here we are giving a first step to obtain in silico evaluations of medical treatments before testing them in vitro or in vivo

Implementing biological hybrid systems: Allowing composition and avoiding stiffness

International audienceThe theory of hybrid systems allows us to model biological functions with many interactive processes, describe complexity and hierarchy levels, and consider behavior law changes. However, we need to develop an implementation to simulate these models. The BioRica framework allows a non-ambiguous implementation, and, as shown here, the QSS method (Quantized States Systems) helps us to approach complex systems in a more efficient way. This method allows us to numerically solve stiff differential equations by separately choosing the temporal partition for each sub-model and variable, depending on how fast it changes over time. With that, one obtains more accurate solutions and decreases the number of computations compared to classic methods. Moreover, QSS does not need to store trajectories and interpolate when mode transitions occur between partition times. Herein, we exhibit a translation from BioRica to QSS models, which preserves the semantics. We implement QSS method with BioRica, and illustrate with applications in Biology, the Tyson model of cell cycle, and examples in Engineering

Implementing biological hybrid systems: Allowing composition and avoiding stiffness

International audienceThe theory of hybrid systems allows us to model biological functions with many interactive processes, describe complexity and hierarchy levels, and consider behavior law changes. However, we need to develop an implementation to simulate these models. The BioRica framework allows a non-ambiguous implementation, and, as shown here, the QSS method (Quantized States Systems) helps us to approach complex systems in a more efficient way. This method allows us to numerically solve stiff differential equations by separately choosing the temporal partition for each sub-model and variable, depending on how fast it changes over time. With that, one obtains more accurate solutions and decreases the number of computations compared to classic methods. Moreover, QSS does not need to store trajectories and interpolate when mode transitions occur between partition times. Herein, we exhibit a translation from BioRica to QSS models, which preserves the semantics. We implement QSS method with BioRica, and illustrate with applications in Biology, the Tyson model of cell cycle, and examples in Engineering

Implementing biological hybrid systems: Allowing composition and avoiding stiffness

International audienceThe theory of hybrid systems allows us to model biological functions with many interactive processes, describe complexity and hierarchy levels, and consider behavior law changes. However, we need to develop an implementation to simulate these models. The BioRica framework allows a non-ambiguous implementation, and, as shown here, the QSS method (Quantized States Systems) helps us to approach complex systems in a more efficient way. This method allows us to numerically solve stiff differential equations by separately choosing the temporal partition for each sub-model and variable, depending on how fast it changes over time. With that, one obtains more accurate solutions and decreases the number of computations compared to classic methods. Moreover, QSS does not need to store trajectories and interpolate when mode transitions occur between partition times. Herein, we exhibit a translation from BioRica to QSS models, which preserves the semantics. We implement QSS method with BioRica, and illustrate with applications in Biology, the Tyson model of cell cycle, and examples in Engineering