diff --git a/content/02.introduction.md b/content/02.introduction.md
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+## Introduction
+
+- Importance of computation in biological research and scope.
+- Importance to reproducibility in computational biology.
+- Mentioning previous literature and some of their major takeaways and topics not fully covered in here (what kind of languages to learn, advanced text editors/IDEs,
+- The computational biology best-practices continuum: reproducibility at the personal level, sharing our research with others and over time.
+- Addressing the audience: computational biologists researchers – data analysts, workflow designers, software developers, mathematical modelers, etc.
+- Structure of this manuscript: mention Figure 1 and Figure 2.
+  - **Figure 1:** Funnel structure for better computational biology.
+  - **Figure 2:** Types of computational biology projects per level.
diff --git a/content/02.manuscript.md b/content/02.manuscript.md
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-## Introduction
-
-- Importance of computation in biological research and scope.
-- Importance to reproducibility in computational biology.
-- Mentioning previous literature and some of their major takeaways and topics not fully covered in here (what kind of languages to learn, advanced text editors/IDEs, 
-- The computational biology best-practices continuum: reproducibility at the personal level, sharing our research with others and over time.
-- Addressing the audience: computational biologists researchers – data analysts, workflow designers, software developers, mathematical modelers, etc.
-- Structure of this manuscript: mention Figure 1 and Figure 2.
-  - **Figure 1:** Funnel structure for better computational biology.
-  - **Figure 2:** Types of computational biology projects per level.
-
-## Level 1: Individual Research
-
-Goal: How should you manage your computational biology project?
-
-Topics:
-- Literate programming: R Markdown, Jupyter Notebooks
-- Version control: Git / GitHub (commits)
-- Software versioning and environment managers (Conda, python-env)
-- Modularize, snippets
-- Coding style: variable naming, linter, pep8, commenting
-- Programming practices: paradigms (object-oriented, procedural), assertions, pair-programming
-
-## Level 2: Collaboration
-
-Goal: How to allow your collaborators to reproduce and interact with your research/software?
-
-Topics:
-- Sharing notebooks: R Markdowns and Jupyter Notebooks (Binder, Google CoLab)
-- Sharing apps: Shiny apps, Dashboard.
-- GitHub (branching, pull requests)
-- Workflow automation: Snakemake (NextFlow, Make, Bash script)
-- Sharing data and metadata
-
-## Level 3: Community
-
-Goal: How to develop and maintain a computational biology project with community feedback over time?
-
-Topics:
-- GitHub releases and semantic versioning
-- Git Flow, GitHub Issues
-- Continuous integration and unit tests
-- Dependencies per user: pip-tools
-- Sharing software as Python packages, Conda/Bioconda or containers (Docker, Singularity)
-- Include license (MIT) and DOIs
-- Documentation: read the docs.
-
-Note: Maybe add software development methodologies (Agile, Scrum, Sprints).
-
-## All three levels in action
-
-- Example of computational biology project 1: RNA-seq analysis (workflow)
-- Example of computational biology project 2: Genome-scale metabolic model (systems biology project)
-- Example of computational biology project 3: Software development (computational tool)
diff --git a/content/03.personal-research.md b/content/03.personal-research.md
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+## Level 1: Personal Research
+
+Goal: How should you manage your computational biology project?
+
+Topics:
+- Literate programming: R Markdown, Jupyter Notebooks
+- Version control: Git / GitHub (commits)
+- Software versioning and environment managers (Conda, python-env)
+- Modularize, snippets
+- Coding style: variable naming, linter, pep8, commenting
+- Programming practices: paradigms (object-oriented, procedural), assertions, pair-programming
diff --git a/content/04.collaboration.md b/content/04.collaboration.md
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+## Level 2: Collaboration
+
+Goal: How to allow your collaborators to reproduce and interact with your research/software?
+
+Topics:
+- Sharing notebooks: R Markdowns and Jupyter Notebooks (Binder, Google CoLab)
+- Sharing apps: Shiny apps, Dashboard.
+- GitHub (branching, pull requests)
+- Workflow automation: Snakemake (NextFlow, Make, Bash script)
+- Sharing data and metadata
diff --git a/content/05.community.md b/content/05.community.md
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+## Level 3: Community
+
+Goal: How to develop and maintain a computational biology project with community feedback over time?
+
+Topics:
+- GitHub releases and semantic versioning
+- Git Flow, GitHub Issues
+- Continuous integration and unit tests
+- Dependencies per user: pip-tools
+- Sharing software as Python packages, Conda/Bioconda or containers (Docker, Singularity)
+- Include license (MIT) and DOIs
+- Documentation: read the docs.
diff --git a/content/06.case-studies.md b/content/06.case-studies.md
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+## Case studies
+
+- Example of computational biology project 1: RNA-seq analysis (workflow)
+- Example of computational biology project 2: Genome-scale metabolic model (systems biology project)
+- Example of computational biology project 3: Software development (computational tool)
diff --git a/content/07.conclusion.md b/content/07.conclusion.md
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+## Conclusion
+
+Pending
diff --git a/content/08.acknowledgments.md b/content/08.acknowledgments.md
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+## Acknowledgments
+
+Pending