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

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

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

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

The fingerprint of the summer 2018 drought in Europe on ground-based atmospheric CO2 measurements

During the summer of 2018, a widespread drought developed over Northern and Central Europe. The increase in temperature and the reduction of soil moisture have influenced carbon dioxide (CO2) exchange between the atmosphere and terrestrial ecosystems in various ways, such as a reduction of photosynthesis, changes in ecosystem respiration, or allowing more frequent fires. In this study, we characterize the resulting perturbation of the atmospheric CO2 seasonal cycles. 2018 has a good coverage of European regions affected by drought, allowing the investigation of how ecosystem flux anomalies impacted spatial CO2 gradients between stations. This density of stations is unprecedented compared to previous drought events in 2003 and 2015, particularly thanks to the deployment of the Integrated Carbon Observation System (ICOS) network of atmospheric greenhouse gas monitoring stations in recent years. Seasonal CO2 cycles from 48 European stations were available for 2017 and 2018.The UK sites were funded by the UK Department of Business, Energy and Industrial Strategy (formerly the Department of Energy and Climate Change) through contracts TRN1028/06/2015 and TRN1537/06/2018. The stations at the ClimaDat Network in Spain have received funding from the ‘la Caixa’ Foundation, under agreement 2010-002624

Sección geológica del Pacífico al Atlántico a través de Costa Rica

A geological profile through Costa Rica, from the Osa Peninsula, on the pacific coast, to the Punta Cahuita on the atlantic coast, permits to rebuild the geological history of the two main basins, in which acumulated more than 5000 meters of tertiary sediments, forming one regressive megasecuence: the Térraba basin on the SW flank of the Talamanca mountain range and the Talamanca basin on the NE flank of the same mountain range. Each basin shows a distinct compressive tectonic style: the Térraba basin presents scales and inverse fauts, with subsidence of the backward part of the basin, while the Talamanca basin presents a gravitative land slide to the sedimentary cover, with important overlaps on the foot of the mountain range, in the coastal plain.La paesentación de un perfil geográfico a través de Costa Rica, desde la Península de Osa, en la costa pacífica, hasta La Punta Cahuita, en la costa atlántica, permite reconstuir la historia geológica de las dos cuencas principales, en las, cuales se acumularon más de 5000 metros de sedimentos terciarios, formando una mega secuencia regresiva: la cuenca de Térraba al SW de la Codillera de Talamanca y la cuenca de Talamanca al NE de la misma cordillera. Cada cuenca enseña un estilo tectónico de compresión distinto: la cuenca de Térraba presenta escamas y fallas frontales inversas, con hundimiento de la parte de atrás, mientras que la cuenca de Talamanca presenta un despegamiento gravitativo de la cobertura sedimentaria con sobrecorrimientos importantes al pie de la cordillera, en la llanura costanera

The 2018 version of the gene table of monogenic neuromuscular disorders (nuclear genome)

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Terciario del sur del Valle Central: sección estratigáfica del Cerro Caraigres, provincia de San José, Costa Rica

The outcrops of Caraigres Mountain, allow to buid one continuous measured section of Pacacua Formation and of the shallow water limestones of Parrita Unit, from the Late Eocene, equivalent of the El Cajon Unit the Brito Formation. The Pacacua Formation, Oligocene and Miocene in age, consists of a volcanic-sedimentary series, represented by volcanic onglomerates, volcanic sandstones (wackes), volcanic tuffs and black silts.The shallow limestones of the Parrita Unit, with macroforamifera and transported bioclasts, with a thickness of 150 m, are typical of a facies of opened lagoons.The big thickness, more than 400 m, of the Pacacua Formation from a shallow environment indicates a continuous subsidence of the Central Valley during the Oligocene and the Miocene, with the formation of horst y graben, limitated to the Nort and to the South by main faults, along which occurred volcanic eruptions, which materials were transported by rivers to the subsident basin, where the were sedimented as deltas. The Pacacua Formation is the main sedimentary series of the Valley Central. The Coris, San Migel y Turrucares Formations ought to be considered as members of this Formation.  Los afloramientos del Cerro Caraigres, asl sur de San José, permiten construir una sección media continua de la Formación Pacacua y de las calizas someras de la Unidad Parrita, del Eoceno Superior, equivalente de la Unidad El Cajón de la Formación Brito. La Formación Pacacua de edad Oligoceno y Mioceno, consiste en una serie volcano-sedimentaria, compuesta de conglomerados volcánicos, areniscas volcánicas (wacas), tobas retrabajadas y lutitas negras. Las calizas someras de la Unidad Parrita, con macroforaminíferos y bioclastos transportados, de 150 m de espesor, son típicas de una facies de lagunas abiertas. El espesor de más de 4000 m de la Formación Pacacua de ambiente somero implica una subsidencia constante del Valle Central durante el Oligoceno y el Mioceno, con la formación de estructuras, horts-graben de dirección NW-SE, limitados al Ny al S por fallas principales, a lo largo de las cuales hubo erupciones de materiales volcánicos intermitentes, transportados por ríos hasta la cuenca de hudimiento, donde se depositaban en forma de deltas.La Formación Pacacua es la serie sedimentaria principal del Valle Central. Las formaciones Coris, San Miguel y Turrucares deberían ser consideradas como miembros de dicha Formación, indicado cambios laterales y locales de facies durante el Mioceno.

The 2020 version of the gene table of neuromuscular disorders (nuclear genome)

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