A Graphical User Interface for a Method to Infer Kinetics and Network Architecture (MIKANA)

Márcio A Mourão,Patrick E Mcsharry,Jeyaraman Srividhya,Santiago Schnell,Edmund J Crampin,Ying Xu

doi:10.1371/journal.pone.0027534

Abstract

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).

Highlights

The biological revolution unleashed by the use of high– throughput technologies provides a detailed picture of the protein interaction map of diverse organisms
In Method to Infer Kinetics and Network Architecture (MIKANA), the task of identifying biochemical pathways from time course data consists of, firstly, identifying the connectivity of the pathway and, secondly, determining and parameterizing the reaction mechanisms involved in each step of the pathway
The identification is made by selecting reactions from a dictionary of functions, which is built by assuming mass action kinetics in all chemically plausible steps between the chemical species measured in the whole reaction system

Summary

Introduction

The biological revolution unleashed by the use of high– throughput technologies provides a detailed picture of the protein interaction map of diverse organisms. The investigation of complex protein interaction maps requires the development of methodologies to understand the complex associations, localization and binding partners among thousands of proteins [1,2,3,4]. These methodologies provide an effective static picture of protein function in a biochemical pathway. During the last 40 years, computational tools have emerged to model biochemical pathways and to estimate kinetic parameters [12] These computational tools require detailed knowledge of the reaction mechanisms to estimate kinetic parameters or deduce specific mechanistic behaviors. We have extensively reviewed these and other approaches for reconstructing mechanisms of biochemical pathways in [6]

Methods

Results

Conclusion