Soft Set Theory for Decision Making in Computational Biology under Incomplete Information.

Gustavo Santos-Garcia,Beatriz Santos-Buitrago,Carolyn Talcott,Adrian Riesco,Merrill Knapp,Jose Carlos R Alcantud

doi:10.1109/access.2019.2896947

Gustavo Santos-Garcia, Beatriz Santos-Buitrago + Show 4 more

Open Access

https://doi.org/10.1109/access.2019.2896947

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Abstract

The study of biological systems is complex and of great importance. There exist numerous approaches to signal transduction processes, including symbolic modeling of cellular adaptation. The use of formal methods for computational systems biology eases the analysis of cellular models and the establishment of the causes and consequences of certain cellular situations associated to diseases. In this paper, we define an application of logic modeling with rewriting logic and soft set theory. Our approach to decision making with soft sets offers a novel strategy that complements standard strategies. We implement a metalevel strategy to control and guide the rewriting process of the Maude rewriting engine. In particular, we adapt mathematical methods to capture imprecision, vagueness, and uncertainty in the available data. Using this new strategy, we propose an extension in the biological symbolic models of Pathway Logic. Our ultimate aim is to automatically determine the rules that are most appropriate and adjusted to reality in dynamic systems using decision making with incomplete soft sets.

Highlights

Systems biology is an emergent field that facilitates understanding biological systems by describing their structure, dynamics, and control methods
We propose a novel metalevel rewriting-logic approach to guide the executions of rewrite rules in biological dynamic systems using decision making with incomplete soft sets
INCOMPLETE SOFT SET STRATEGIES IN PATHWAY LOGIC We present the integration between the incomplete soft set strategy outlined in the previous section and the Pathway Logic framework

Summary

INTRODUCTION

Systems biology is an emergent field that facilitates understanding biological systems by describing their structure, dynamics, and control methods. Of this introduction, we define the purpose of our work and its significance, and review the current state-of-art in the related research fields This interdisciplinary work covers three very different areas: soft set theory under incomplete information, rewriting logic, and computational biology and Pathway Logic. There are numerous approaches to obtain ‘‘decision values’’ In this sense, Alcantud and Santos-García [18] define a new criterion of choice values extraction for incomplete soft sets based on a combinatorial. While the equational part is used to define the static nature of a system (e.g. data structures), allowing the user to define sorts, constructors, and functions on these sorts, rules stand for state transitions Following these ideas, the rewriting logic approach has been successfully applied to many models of concurrency [20], [21].

COMPUTATIONAL BIOLOGY AND PATHWAY LOGIC

MODELING IN PATHWAY LOGIC

INCOMPLETE SOFT SET STRATEGIES FOR DECISION MAKING

Result

DISCUSSION AND CONCLUSIONS