Python Belgrade #66

  • Location: CDT Hub

  • Date: 2026-03-27 18:00

Talks:

Integrative In Silico Analysis of Total RNA Networks in Parkinson’s Disease —

  • Abstract:

Understanding the role of total RNA molecules in Parkinson’s disease remains an evolving area of research. This lecture presents insights from an in silico gene expression study using a preclinical model to examine how total RNA interactions contribute to disease-related molecular changes. By integrating publicly available datasets and preliminary findings, signaling networks associated with Parkinson’s disease were identified, revealing altered gene expression patterns and interconnected pathways. The analytical framework combines statistical and bioinformatic approaches, including differential expression, multivariate analysis, and pathway enrichment. In addition to established R-based workflows, the pipeline is applicable to Python, enabling reproducible data processing, statistical modeling, and visualization of transcriptomic data. Overall, the findings highlight the regulatory importance of total RNA molecules and their potential as biomarkers for monitoring disease progression, while demonstrating how flexible computational tools can advance understanding of Parkinson’s disease mechanisms and support future translational research.

Rendering as Code with PyMOL —

  • Abstract:

We begin by establishing that molecular systems are hierarchical data structures rather than static models. We will explore how mapping atomic coordinates to queryable Python objects enables deterministic structural manipulation via idempotent selectors, forming the foundation of a programmable visual scene. The discussion then shifts to the transition from ephemeral GUI states to robust system infrastructure, where we treat selections as SQL-like queries and renders as versioned artifacts. By wrapping hooks into diffable blueprints, we will demonstrate how to bridge declarative definitions and imperative execution, ensuring CI/CD readiness and the use of reproducible, stored state. To conclude, we examine how scripted scenography replaces memory-based styling to treat visualization as a deterministic performance of structural data. We will conclude by showing how formalizing these protocols enables reproducible, shareable molecular narratives, allowing researchers to focus on data analysis while ensuring publication-quality assets remain derived directly from the underlying data. References: - PyMOL - De novo design of protein nanoparticles with integrated functional motifs - Structural and functional properties of SARS-CoV-2 spike protein: Potential antivirus drug development for COVID-19 - Structure of the dopamine D2 receptor in complex with the antipsychotic drug spiperone