414 research outputs found
Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming
A fundamental question in systems biology is the construction and training to
data of mathematical models. Logic formalisms have become very popular to model
signaling networks because their simplicity allows us to model large systems
encompassing hundreds of proteins. An approach to train (Boolean) logic models
to high-throughput phospho-proteomics data was recently introduced and solved
using optimization heuristics based on stochastic methods. Here we demonstrate
how this problem can be solved using Answer Set Programming (ASP), a
declarative problem solving paradigm, in which a problem is encoded as a
logical program such that its answer sets represent solutions to the problem.
ASP has significant improvements over heuristic methods in terms of efficiency
and scalability, it guarantees global optimality of solutions as well as
provides a complete set of solutions. We illustrate the application of ASP with
in silico cases based on realistic networks and data
AI-driven Hypernetwork of Organic Chemistry: Network Statistics and Applications in Reaction Classification
Rapid discovery of new reactions and molecules in recent years has been
facilitated by the advancements in high throughput screening, accessibility to
a much more complex chemical design space, and the development of accurate
molecular modeling frameworks. A holistic study of the growing chemistry
literature is, therefore, required that focuses on understanding the recent
trends and extrapolating them into possible future trajectories. To this end,
several network theory-based studies have been reported that use a directed
graph representation of chemical reactions. Here, we perform a study based on
representing chemical reactions as hypergraphs where the hyperedges represent
chemical reactions and nodes represent the participating molecules. We use a
standard reactions dataset to construct a hypernetwork and report its
statistics such as degree distributions, average path length, assortativity or
degree correlations, PageRank centrality, and graph-based clusters (or
communities). We also compute each statistic for an equivalent directed graph
representation of reactions to draw parallels and highlight differences between
the two. To demonstrate the AI applicability of hypergraph reaction
representation, we generate dense hypergraph embeddings and use them in the
reaction classification problem. We conclude that the hypernetwork
representation is flexible, preserves reaction context, and uncovers hidden
insights that are otherwise not apparent in a traditional directed graph
representation of chemical reactions
Transformations on hypergraph families
We present a new general theory of function-based hypergraph transformations
on finite families of finite hypergraphs. A function-based hypergraph
transformation formalises the action of structurally modifying hypergraphs from
a family in a consistent manner. The mathematical form of the transformations
facilitates their analysis and incorporation into larger mathematical
structures, and concurs with the function-based nature of modelling in the
physical world. Since quotients of hypergraphs afford their simplification and
comparison, we also discuss the notion of a quotient hypergraph transformation
induced by an equivalence relation on the vertex set of a hypergraph family.
Finally, we demonstrate function-based hypergraph transformations with two
fundamental classes of examples involving the addition or deletion of
hyperedges or hypergraphs
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