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Overview

This documentation provides a comprehensive guide to my project, focusing on Flux Balance Analysis (FBA) in the context of systems biology. The document is structured into three main sections: Introduction, FBA and Linear Approximation. Each section is designed to offer both a theoretical understanding and practical insights into the methodologies and applications of FBA as well as the purpose of the project and its impact.

1. Introduction​

1.1 Overview​

The Introduction section begins with an overview, setting the stage for what readers can expect from the documentation. It outlines the purpose, scope, and target audience of the document, providing a foundation for the detailed content that follows.

1.2 Goals with this Project​

Here, we define the objectives and aspirations of this project. This part serves to align readers with the project's vision, detailing the expected outcomes and the impact we aim to achieve in the field of computational biology.

2 What is Flux Balance Analysis?​

2.1 Overview​

The mathematical and theoretical foundations of Flux Balance Analysis are set here.

2.2 Static FBA​

Static Flux Balance Analysis is introduced, explaining its fundamental principles, how it operates, and its relevance in studying metabolic networks in biological systems.

2.3 Dynamic FBA​

This subsection delves into Dynamic Flux Balance Analysis, highlighting the distinctions from Static FBA, its applications, and the added complexities it addresses in modeling biological systems.

3. Linear Approximation​

3.1 Methods​

In the Linear Approximation section, we explore various approaches to the topic. This part is critical for understanding the reasoning behind the usage of specific methods over others.

3.1.1 Static Segmentation​

We discuss the process and significance of Static Segmentation in linear approximation, particularly its implementation.

3.1.2 Adaptive Static Segmentation​

This part builds upon Static Segmentation, exploring how Adaptive Static Segmentation differs and the additional benefits it offers.

3.1.3 Dynamic Bounds Fitting​

Dynamic Bounds Fitting is examined, explaining its approach, application, and how it contributes to the accuracy and efficiency.

3.1.4 Bayesian Optimization​

Bayesian Optimization is introduced, providing insights into its methodology, advantages, and specific scenarios where it proves most effective.

3.2 Discussion about the Approaches​

The final section offers a critical analysis and comparison of the methods outlined in Section 2.1. We discuss the practical applications, strengths, and limitations of each method, guiding readers in understanding the best practices and scenarios for their implementation.

This documentation is designed to be both informative and practical, offering detailed insights while guiding readers through the complexities of Flux Balance Analysis in computational biology.