Text as tautology: an exploration in inference, transitivity, and logical compression

Rhetorical structure theory (RST) and relational propositions have been shown useful in analyzing texts as expressions in propositional logic. Because these expressions are systematically derived, they may be expected to model discursive reasoning as articulated in the text. If this is the case, it would follow that logical operations performed on the expressions would be reflected in the texts. In this paper the logic of relational propositions is used to demonstrate the applicability of transitive inference to discourse. Starting with a selection of RST analyses from the research literature, analyses of the logic of relational propositions are performed to identify their corresponding logical expressions and within each expression to identify the inference path implicit within the text. By eliminating intermediary relational propositions, transitivity is then used to progressively compress the expression. The resulting compressions are applied to the corresponding texts and their compressed RST analyses. The application of transitive inference to logical expressions results in abridged texts that are intuitively coherent and logically compatible with their originals. This indicates an underlying isomorphism between the inferential structure of logical expressions and discursive coherence, and it confirms that these expressions function as logical models of the text. Potential areas for application include knowledge representation, logic and argumentation, and RST validation.

Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles. We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model’s ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets. This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis.

APPLICATION OF ARTIFICIAL INTELLIGENCE METHODS IN 3D SIMULATORS BASED ON FUNCTIONAL AND LOGICAL MODELS

The article presents an approach to the use of artificial intelligence methods in the development of imitation professional simulators. The analysis of the use of artificial intelligence tools for the development of 3D applications is carried out, typical components and their functions are considered, the drawbacks that limit the use of these methods in professional simulators are revealed. The proposed approach is based on adapting these standard tools and supplementing them with functional-logical models that perform control functions. As an example, the implementation of the approach in the Unity cross-platform development environment is proposed. The results of the work have been tested in the development of simulators in the field of labor protection.

Shugart, M. y Taagepera, R. (2017). Votes from seats. Logical models of electoral systems. Nueva York: Cambridge University Press

La literatura sobre sistemas electorales es voluminosa, pero pocos nombres resaltan tanto en ella como los de Matthew Shugart y Rein Taagepera. En "Votes from Seats" se vislumbra la continuidad y – en parte – la culminación de décadas de estudio de los sistemas electorales por estos autores. Expanden el camino iniciado en sus obras anteriores (v.g., Taagepera y Shugart, 1989 y 1993), pero también corrigen premisas que ahora consideran equivocadas y afinan predicciones que antes no pasaban de ser sospechas, como abiertamente declaran. En el tema concreto de los sistemas electorales, su funcionamiento y sus consecuencias, el libro está en la frontera del conocimiento. Aunque sintetiza conceptos y teorías que ya conformaban el paradigma de los estudios electorales – y que no resultarán novedosos para las personas conocedoras de la literatura – , Shugart y Taagepera llevan las hipótesis a un nuevo estadio de prueba empírica, ofreciendo una mirada más robusta a los mecanismos que unen a los sistemas electorales con los sistemas de partidos

Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signalling pathways, facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles.We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviours identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model’s ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets.This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis.Author summaryWe constructed a logical model to study, from a dynamical perspective, the differences between melanomas and colorectal cancers that share the same BRAF mutations but exhibit different sensitivities to anti-BRAF treatments. The model was built from the literature and completed from existing pathway databases. The model encompasses the key proteins of the MAPK pathway and was made specific to each cancer cell line (100 melanoma and colorectal cell lines from public database) using available omics data, including mutations and RNAseq data. It can simulate the effect of drugs and show high correlation with experimental results. Moreover, the structure of the network confirms both the importance of the reactivation of the MAPK pathway through CRAF and the involvement of PI3K/AKT pathway in the mechanisms of resistance to BRAF inhibition.The study shows that, because of the low number of samples, the mechanistic approach that we propose provides different insights than powerful standard machine learning methodologies would, showing the complementarity between the two approaches. An important aspect to mention is that the mechanistic approach presented here does not rely on training datasets but directly interprets and maps data on the model to simulate drug responses.

Semi-automatic model revision of Boolean regulatory networks: confronting time-series observations with (a)synchronous dynamics

MotivationComplex cellular processes can be represented by biological regulatory networks. Computational models of such networks have successfully allowed the reprodution of known behaviour and to have a better understanding of the associated cellular processes. However, the construction of these models is still mainly a manual task, and therefore prone to error. Additionally, as new data is acquired, existing models must be revised. Here, we propose a model revision approach of Boolean logical models capable of repairing inconsistent models confronted with time-series observations. Moreover, we account for both synchronous and asynchronous dynamics.ResultsThe proposed tool is tested on five well known biological models. Different time-series observations are generated, consistent with these models. Then, the models are corrupted with different random changes. The proposed tool is able to repair the majority of the corrupted models, considering the generated time-series observations. Moreover, all the optimal solutions to repair the models are produced.Contact{[email protected],[email protected]}

ІНТЕЛЕКТУАЛЬНІ СИСТЕМИ: ПРИНЦИПИ ЕВРИСТИЧНОЇ САМООРГАНІЗАЦІЇ ПРОЦЕСІВ СМИСЛОВОГО МИСЛЕННЯ ТА СМИСЛОВОЇ ДІЯЛЬНОСТІ

The purpose of this study is to solve the following problems. The first task concerns the determination of the form of correspondence of factors that model the technological activity of the “process” and “resource” with factors that are subjected to simultaneous processing according to the central regularity of the integrative activity of the brain. The second task concerns the determination of possible forms of relationships for technological activity factors “process” and “resource” with concepts that characterize thinking processes, namely: “reflection”, “data”, “information”, “knowledge”, “meaning”, “thinking”, “intelligence”, “semantic thinking”, “understanding”. From the above analysis of the problems of representation, processing, and acquisition of knowledge, it follows that the main problem is the mismatch of the laws of formal logic with logic, which is realized in the thinking processes of living beings. Human intelligence in the theory of artificial intelligence is perceived as an auxiliary tool. In the theory of artificial intelligence, the model of an artificial neuron copies its structure but does not reproduce the processes that are realized in it. From a philosophical point of view, the basic concepts that reveal the content of thinking processes are the concepts of “reason” and “mind”. Moreover, the main property of the mind is its dialectics, which is manifested through the concept of "measure". The content of the concept of “measure” is defined in the form of a dialectical unity of the concepts of “general” (qualitative definition) “single” (quantitative definition). The methodological basis for the construction of all logical models is the methodology of a holistic approach based on which a logical model of holistic semantic activity is formed. In this model, the content of "duality" of the content of the concept of "activity" is disclosed. This ensured the definition of the principle of organizing the intellectual system into an organized whole in the form of the dialectical unity of certain tasks, as well as the principle of self-organization of its activities in the form of a mechanism to ensure compliance with the results of solving these problems. Based on the hypothesis about the equivalence of the technological activity of the natural intellectual system using the “process” and “resource” factors and the process of semantic thinking based on the central regularity of the integrative activity of the brain, a logical model for structuring excitations on the theory of functional systems has been developed. This model, along with the logical model of semantic activity (process), serves as the basis for the formation of logical models of levels 0 - 4. The principle of heuristic self-organization in the form of a fourth heuristic, namely, dialectical self-organization for the concepts of “general” “single”, is a fundamental principle of heuristic self-organization of pairs of factors for logical models. The architectures of these logical models are formed using two pairs of factors defined for each model. The fifth principle of heuristic self-organization follows from this: the architecture of logical models of semantic thinking and semantic activity are formed using two pairs of factors defined for each logical model, the elements of each of which are connected by cause-effect relationships, which in meaning correspond to pairs of process and resource factors, and correspond to the architecture of the Cartesian coordinate system.

Setting the basis of best practices and standards for curation and annotation of logical models in biology—highlights of the [BC]2 2019 CoLoMoTo/SysMod Workshop

The fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled ‘Annotation and curation of computational models in biology’, organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements.