Cell death and life in cancer: mathematical modeling of cell fate decisions

Tumor development is characterized by a compromised balance between cell life and death decision mechanisms, which are tighly regulated in normal cells. Understanding this process provides insights for developing new treatments for fighting with cancer. We present a study of a mathematical model describing cellular choice between survival and two alternative cell death modalities: apoptosis and necrosis. The model is implemented in discrete modeling formalism and allows to predict probabilities of having a particular cellular phenotype in response to engagement of cell death receptors. Using an original parameter sensitivity analysis developed for discrete dynamic systems, we determine the critical parameters affecting cellular fate decision variables that appear to be critical in the cellular fate decision and discuss how they are exploited by existing cancer therapies

Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

International audienceABSTRACT: Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time. BACKGROUND: There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature. RESULTS: Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions. CONCLUSIONS: Applications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations

NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps

Molecular biology knowledge can be systematically represented in a computer-readable form as a comprehensive map of molecular interactions. There exist a number of maps of molecular interactions containing detailed description of various cell mechanisms. It is difficult to explore these large maps, to comment their content and to maintain them. Though there exist several tools addressing these problems individually, the scientific community still lacks an environment that combines these three capabilities together. NaviCell is a web-based environment for exploiting large maps of molecular interactions, created in CellDesigner, allowing their easy exploration, curation and maintenance. NaviCell combines three features: (1) efficient map browsing based on Google Maps engine; (2) semantic zooming for viewing different levels of details or of abstraction of the map and (3) integrated web-based blog for collecting the community feedback. NaviCell can be easily used by experts in the field of molecular biology for studying molecular entities of their interest in the context of signaling pathways and cross-talks between pathways within a global signaling network. NaviCell allows both exploration of detailed molecular mechanisms represented on the map and a more abstract view of the map up to a top-level modular representation. NaviCell facilitates curation, maintenance and updating the comprehensive maps of molecular interactions in an interactive fashion due to an imbedded blogging system. NaviCell provides an easy way to explore large-scale maps of molecular interactions, thanks to the Google Maps and WordPress interfaces, already familiar to many users. Semantic zooming used for navigating geographical maps is adopted for molecular maps in NaviCell, making any level of visualization meaningful to the user. In addition, NaviCell provides a framework for community-based map curation.Comment: 20 pages, 5 figures, submitte

Coupling the Cell cycle and the Circadian Cycle

Cancer treatments based on the administration of medicines at different times of the day have been shown to be more efficient against malign cells and less damaging towards healthy ones. These results might be related to the recent discovery of links between the circadian clock, (controlled by the light/dark cycle of a day), and the cell cycle. However, if many models have been developed to describe both of these cycles, to our knowledge none has described a real interaction between them. We will try to establish a relation at a molecular level and we will then study the conditions of entrainment in period of these cycles with the modeling environment BIOCHAM. In other words, we will search how and where in the parameter space of our model the two cycles get synchronized. This technical report will insist on the conditions of entrainment of the cell cycle by the circadian cycle via a common protein kinase WEE1

BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge

International audienceBIOCHAM (the BIOCHemical Abstract Machine) is a software environment for modeling biochemical systems. It is based on two aspects: (1) the analysis and simulation of boolean, kinetic and stoch-astic models and (2) the formalization of biological properties in temporal logic. BIOCHAM provides tools and languages for describing protein networks with a simple and straightforward syntax, and for integrating biological properties into the model. It then becomes possible to analyze, query, verify and maintain the model with respect to those properties. For kinetic models, BIOCHAM can search for appropriate parameter values in order to reproduce a specific behavior observed in experiments and formalized in temporal logic. Coupled with other methods such as bifurcation diagrams, this search assists the modeler/biologist in the modeling process

Modelling molecular networks: relationships between different formalisms and levels of details

This document is the deliverable 1.3 of French ANR CALAMAR. It presents a study of different formalisms used for modelling and analyzing large molecular regulation networks, their formal links, in terms of mutual encodings and of abstractions, and the corresponding levels of detail captured

A Machine Learning approach to Biochemical Reaction Rules Discovery

Beyond numerical simulation, the possibility of performing symbolic computation on bio-molecular interaction networks opens the way to the design of new automated reasoning tools for biologists/modelers. The Biochemical Abstract machine BIOCHAM provides a precise semantics to biomolecular interaction maps as concurrent transition systems. Based on this formal semantics, BIOCHAM offers a compositional rule-based language for modeling biochemical systems, and an original query language based on temporal logic for expressing biological queries about reachability, checkpoints, oscillations or stability. Turning the temporal logic query language into a specification language for expressing the observed behavior of the system (in wild-life and mutated organisms) makes it possible to use machine learning techniques for completing or correcting biological models semi-automatically. Machine learning from temporal logic formulae is quite new however, both from the machine learning perspective and from the Systems Biology perspective. In this paper, we report on the machine learning system of BIOCHAM which allows to discover, on the one hand, interaction rules from a partial model with constraints on the system behavior expressed in temporal logic, and on the other hand, kinetic parameter values from a temporal logic specification with constraints on numerical concentrations

Apprentissage de règles de réactions biochimiques à partir de propriétés en logique temporelle

Avec le développement de langages formels pour modéliser les systèmes d' interactions biomoléculaires, la possibilité d'effectuer des calculs symboliques au delà des simulations numér iques ouvre la voie à la conception de nouveaux outils de raisonnement automatique destinés au biologiste modélisateur. La machine abstraite biochimique BIOCHAM est un environnement logiciel qui offre un langage simple de règles pour modéliser les interactions biomoléculaires et un langage original fondé sur la logique temporelle pour formaliser les propriétés biologiques du système. En s'appuyant sur ces deux langages formels, il devient possible d'utiliser des techniques d'apprentissage automatique pour inférer de nouvelles règles de réaction moléculaire à partir de propriétés temporelles observées. Dans ce contexte, le but est de corriger ou compléter les modèles BIOCHAM semi-automatiquement. Dans cet article, nous décrivons le système d'apprentissage automatique de BIOCHAM, qui permet, d'une part, de trouver de nouvelles règles d'interaction à partir d' un modèle partiel et de contraintes exprimées en logique temporelle, et d'autre part, d'estimer les valeurs de paramètres cinétiques à partir de propriétés formalisées en logique temporelle avec contraintes numériques sur les concentrations ou leurs dérivées