A Graphical User Interface for a Method to Infer Kinetics and Network Architecture (MIKANA)
نویسندگان
چکیده
One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).
منابع مشابه
A New Single-Display Intelligent Adaptive Interface for Controlling a Group of UAVs
The increasing use of unmanned aerial vehicles (UAVs) or drones in different civil and military operations has attracted attention of many researchers and science communities. One of the most notable challenges in this field is supervising and controlling a group or a team of UAVs by a single user. Thereupon, we proposed a new intelligent adaptive interface (IAI) to overcome to this challenge. ...
متن کاملتحلیل میزان درک کاربران از نمادهای تصویری محیط رابط گرافیکی نرمافزار سیمرغ
Purpose: This research is devoted to study the icons in graphical user interface of Simorgh library software and analyze the users’ understanding of and interaction with this software in Birjand University. Methodology: The methodology of this research is of survey type and it is an applied study. To measure the responders’ understanding of icons in different pages of search section in Simorgh...
متن کاملThe GINA Interface Builder
As part of the GINA project at GMD, an interface builder has been implemented that can be used to interactively develop graphical user interfaces based on the OSF/Motif toolkit. The interface builder extensively uses direct-manipulation techniques to facilitate explorative learning and to accelerate the definition process. It provides special support for the dynamic layout capabilities of Motif...
متن کاملCausal Graphical Models in Systems Genetics: a Unified Framework for Joint Inference of Causal Network and Genetic Architecture for Correlated Phenotypes1
Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenoty...
متن کاملCausal Graphical Models in System Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes
Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenoty...
متن کامل