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

Learning Transition Rules from Temporal Logic Properties

Most of the work on temporal representation issues in Machine Learning deals with the problem of learning/mining temporal patterns from a large set of temporal data. In this paper we investigate the somewhat different problem of learning the behavioral rules of a system from its observed temporal properties formalized in temporal logic. Our interest in this problem arose from Systems Biology and the development of machine learning techniques for learning biochemical reaction rules and kinetic parameters in the Biochemical Abstract Machine BIOCHAM. Our contribution is twofold. First, in the general setting of Kripke structures and concurrent transition systems, we define positive and negative CTL formulae and propose a theory revision algorithm for learning transition rules from a CTL specification. Second, in the setting of hybrid systems which add a continuous dynamics described by differential equations, we show how a similar algorithm can be built to learn parameter values from a constraint LTL specification. In the context of BIOCHAM, which is used as a running example in this paper, we report evaluation results showing the usefulness of this approach and encouraging performance figures

Machine Learning Bio-molecular Interactions from Temporal Logic Properties

With the advent of formal languages for modeling bio-molecu\-lar interaction systems, the design of automated reasoning tools to assist the biologist becomes possible. The biochemical abstract machine BIOCHAM software environment offers a rule-based language to model bio-molecular interactions and an original temporal logic based language to formalize the biological properties of the system. Building on these two formal languages, machine learning techniques can be used to infer new molecular interaction rules from temporal properties. In this context, the aim is to semi-automatically correct or complete models from observed biological properties of the system. 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 present an ad-hoc enumerative method for structural learning from temporal properties and report on the evaluation of this method on formal biological models of the literature

Langages formels dans la machine abstraite biochimique BIOCHAM

International audienceLe développement de langages formels pour modéliser les systèmes biologiques 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 des interactions biomoléculaires, et un lan-gage puissant fondé sur la logique temporelle pour formaliser les propriétés biologiques du sys-tème. 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, estimer les valeurs des paramètres cinétiques, et corriger ou compléter les modèles semi-automatiquement. Dans cet article, nous décrivons les langages implantés dans BIOCHAM et illustrons l'utilisation du système d'apprentissage automatique sur un modèle simple du contrôle du cycle cellulaire. ABSTRACT. With the advent of formal languages for modeling biological systems, the design of automated reasoning tools to assist the biologist becomes possible. The biochemical abstract machine BIOCHAM software environment offers a rule-based language to model bio-molecular interactions and a powerful temporal logic based language to formalize the biological properties of the system. Building on these two formal languages, machine learning techniques can be used to infer new molecular interaction rules from temporal properties, or to estimate kinetic parameter values, in order to semi-automatically correct or complete models from observed biological properties of the system. In this article we describe the formal languages of BIOCHAM and illustrate, on a simple cell cycle control model, the use of the machine learning system

A Process Calculus for Molecular Interaction Maps

We present the MIM calculus, a modeling formalism with a strong biological basis, which provides biologically-meaningful operators for representing the interaction capabilities of molecular species. The operators of the calculus are inspired by the reaction symbols used in Molecular Interaction Maps (MIMs), a diagrammatic notation used by biologists. Models of the calculus can be easily derived from MIM diagrams, for which an unambiguous and executable interpretation is thus obtained. We give a formal definition of the syntax and semantics of the MIM calculus, and we study properties of the formalism. A case study is also presented to show the use of the calculus for modeling biomolecular networks.Comment: 15 pages; 8 figures; To be published on EPTCS, proceedings of MeCBIC 200

A Minimal OO Calculus for Modelling Biological Systems

In this paper we present a minimal object oriented core calculus for modelling the biological notion of type that arises from biological ontologies in formalisms based on term rewriting. This calculus implements encapsulation, method invocation, subtyping and a simple formof overriding inheritance, and it is applicable to models designed in the most popular term-rewriting formalisms. The classes implemented in a formalism can be used in several models, like programming libraries.Comment: In Proceedings CompMod 2011, arXiv:1109.104

Modelling and querying interaction networks in the biochemical abstract machine biocham

Recent progress in high-throughput data-production technologies pushes research toward systems biology, focusing on the global interaction between the components of biomolecular processes. In this article we present a formal modelling environment fo

The biochemical abstract machine BIOCHAM

Abstract. In this article we present the Biochemical Abstract Machine BIOCHAM and advocate its use as a formal modeling environment for networks biology. Biocham provides a precise semantics to biomolecular interaction maps. Based on this formal semantics, the Biocham system offers automated reasoning tools for querying the temporal properties of the system under all its possible behaviors. We present the main features of Biocham, provide details on a simple example of the MAPK signaling cascade and prove some results on the equivalence of models w.r.t. their temporal properties. 1 Introduction In networks biology, the complexity of the systems at hand (metabolic net-works, extracellular and intracellular networks, networks of gene regulation) clearly shows the necessity of software tools for reasoning globally about bio-logical systems [1]. Several formalisms have been proposed in recent years for modeling biochemical processes either qualitatively [2-4] or quantitatively [5-9].State-of-the-art tools integrate a graphical user interface and a simulator, yet few formal tools are available for reasoning about these processes and provingproperties about them. Our focus in Biocham has been on the design of a biochemical rule language and a query language of the model in temporal logic,that are intended to be used by biologists. Biocham has been designed in the framework of the ARC CPBIO on "ProcessCalculi and Biology of Molecular Networks " [10] which aims at pushing forward a declarative and compositional approach to modeling languages in SystemsBiology. Biocham is a language and a programming environment for modeling biochemical systems, making simulations, and checking temporal properties. Itis composed of