22 papers · ranked by Valyu relevance
Jonas Béal, Arnau Montagud, Pauline Traynard, Emmanuel Barillot + 1 more
'Laurence Calzone'] Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumors and their therapeutic…
Luna Xingyu Li, Boris Aguilar, John H Gennari, Guangrong Qin
Gene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision…
Regina Samaga, Steffen Klamt
A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is…
Kirsten Thobe, Christina Kuznia, Christine Sers, Heike Siebert
Systems biology studies the structure and dynamics of biological systems using mathematical approaches. Bottom-up approaches create models from prior knowledge but usually cannot cope with uncertainty, whereas top-down approaches infer models directly from data using statistical methods but mostly neglect valuable…
Wassim Abou-Jaoudé, Pauline Traynard, Pedro T. Monteiro, Julio Saez-Rodriguez + 3 more
'Julio Saez-Rodriguez' 'Tomáš Helikar' 'Denis Thieffry' 'Claudine Chaouiya'] The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models.…
Wassim Abou-Jaoudé, Pedro T. Monteiro, Aurélien Naldi, Maximilien Grandclaudon + 3 more
Computational modeling constitutes a crucial step towards the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant…
Ahmed Abdelmonem Hemedan, Reinhard Schneider, Marek Ostaszewski
Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson’s disease (PD), one of the most prevalent neurodegenerative disorders. Our…
Aurélien Naldi
Here we introduce bioLQM, a new Java software toolkit for the conversion, modification, and analysis of Logical Qualitative Models of biological regulatory networks, aiming to foster the development of novel complementary tools by providing core modelling operations. Based on the definition of multi-valued logical…
Kirsten Thobe, Christine Sers, Heike Siebert
Background The mammalian target of rapamycin (mTOR) is a regulator of cell proliferation, cell growth and apoptosis working through two distinct complexes: mTORC1 and mTORC2. Although much is known about the activation and inactivation of mTORC1, the processes controlling mTORC2 remain poorly characterized.…
Katrin Kolczyk, Regina Samaga, Holger Conzelmann, Sebastian Mirschel + 1 more
'Carsten Conradi'] Background Signaling systems typically involve large, structured molecules each consisting of a large number of subunits called molecule domains. In modeling such systems these domains can be considered as the main players. In order to handle the resulting combinatorial complexity, rule-based…
Sabah Al‐Fedaghi
– Applying logic in the area of conceptual modeling has been investigated widely, yet there has been limited uptake of logic-based conceptual modeling in industry. According to some researchers, another formalization of such tools as EER or UML class diagrams in logic may only marginally contribute to the body of…
Céline Hernandez, Morgane Thomas-Chollier, Aurélien Naldi, Denis Thieffry
At the crossroad between biology and mathematical modelling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as our knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical…
Arnaud Poret, Claudio Monteiro Sousa, Jean-Pierre Boissel
Due to the scarcity of quantitative details about biological phenomena, quantitative modeling in systems biology can be compromised, especially at the subcellular scale. One way to get around this is qualitative modeling because it requires few to no quantitative information. One of the most popular qualitative…
Sabah Al‐Fedaghi
Modeling languages in software engineering (e.g., UML) evolved from software systems modeling where denotational and operational kinds of semantics are the traditional subjects of research and practice. According to some authors, although a large portion of the static semantics (e.g., UML) seems to have reached a…
Siyuan Wang, Zhongyu Wei, Meng Han, Zhihao Fan + 3 more
'Qi Zhang' 'Xuanjing Huang'] Logical reasoning over incomplete knowledge graphs to answer complex logical queries is a challenging task. With the emergence of new entities and relations in constantly evolving KGs, inductive logical reasoning over KGs has become a crucial problem. However, previous PLMs-based methods…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Authors not listed
The scarcity and expense of fatigue data limits optimal design of components and constrains companies to a few well qualified materials when safety-critical applications are concerned. This research investigates different strategies to improve extraction of structured information from unstructured scientific…
Authors not listed
In the real world, many reversal phenomena occur—for example, cases in which a statement once regarded as false is later recognized as true. Upside-Down Logic is a framework designed to formalize such reversal phenomena as a logical system. It inverts the truth and falsity of propositions through contextual…
Ruggero Pagnan
> Abstract. We introduce ologisms. They generate from ologs by extending their logical expressivity, from the possibility of considering constraints of equational nature only to the possibility of considering constraints of syllogistic nature, in addition. This is obtained by taking advantage of the peculiar features…
Sabah Al‐Fedaghi
Object orientation has become the predominant paradigm for conceptual modeling (e.g., UML), where the notions of class and object form the primitive building blocks of thought. Classes act as templates for objects that have attributes and methods (actions). The modeled systems are not even necessarily software systems…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…