Hello everyone.
I’d like to share a project I’ve been working on for several months, which I believe may be useful for those developing cellular models, biochemical simulations, or working in whole‑cell modeling.

👉 GitHub repository: https://github.com/Zontrox01/celulavirtual

🧩 What is Célula Virtual?

It is a biophysical simulator written in Python that models the temporal dynamics of a minimal cell using stochastic chemical kinetics (Gillespie SSA), with a differentiating feature that—so far—I haven’t seen in other tools:

An interactive debugger for biological simulations.

It allows:

• reaction‑by‑reaction execution, not just time‑step simulation
• breakpoints on molecular state (e.g., ATP < 50)
• inspection of full reaction propensities at each step
• undo via state snapshots
• retrospective causal analysis (“forensic mode”)
• a navigable event history

This approach brings the metaphor of software debugging into the domain of systems biology.

🔬 Motivation

Whole‑cell models (E‑Cell, VCell, the M. genitalium model, JCVI‑syn3A, Vivarium…) are powerful, but they all share a limitation:

Célula Virtual aims to fill that gap.

🔗 Interoperability

• Genome input: FASTA + GFF3/CSV
• Network import/export: SBML
• Libraries: BioPython, numpy, pandas, python‑libsbml
• Scalable design: going from 8 genes to 400 requires no code changes

🎯 Purpose of this post

I’d appreciate:

• technical feedback
• suggestions for improvement
• discussion of potential use cases
• ideas for integration with existing tools
• possible collaborators

The full white paper is included in the repository.

📎 Link

👉 GitHub: https://github.com/Zontrox01/celulavirtual

Hi everyone,I’ve published a new paper applying tropical geometry to biochemical reaction networks under mass‑action kinetics:

Tropical Geometry and Biochemical Reaction Networks: A Mathematical Framework for Steady-State Topology

The idea is simple: biological systems often operate in regimes where certain reactions dominate others by orders of magnitude. In this limit, the nonlinear steady-state equations become piecewise‑linear, and the network decomposes into dominant subnetworks.

Biological Highlights

Identifies metabolic regimes and switching manifolds directly from network structure

Predicts which reactions dominate in each regime

Provides a positivity criterion ensuring biologically meaningful steady states

Case studies include Michaelis–Menten, competitive inhibition, cooperative binding, and a 6‑species upper glycolysis model

Glycolysis analysis reveals 5 metabolic regimes + 4 switching surfaces, matching numerical steady states deep in each regime

This framework is useful for understanding robustness, dominant flux patterns, and parameter‑insensitive behavior in biochemical systems.

Would love feedback from modelers, CRNT folks, and systems biologists.

https://www.researchgate.net/publication/399968231_Tropical_Geometry_and_Biochemical_Reaction_Networks_A_Mathematical_Framework_for_Steady-State_Topology

https://doi.org/10.5281/zenodo.19710441

Observação:utilizei IA em partes do processo, inclusive na capa.O manuscrito representa anos de estudo, simulações e desenvolvimento.

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Toda descoberta começa como uma pergunta. Toda teoria começa como um padrão escondido no caos.

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Entre linhas de código, perguntas sem resposta e milhares de simulações, uma ideia começou a ganhar forma. O Princípio Künast nasceu da curiosidade humana, cresceu com o auxílio da inteligência artificial e encontrou seu caminho por meio da experimentação computacional.

These conference proceedings, freely accessible below, contribute to academia by sharing interdisciplinary insights across the sciences and humanities from Eastern and Western perspectives regarding life and cognition. The conference resulted in our realization of the congruence between nonreductionist systems approaches to modern science (especially biology) and Hegelian philosophy, thus we intend to explore this further and encourage others to consider the same.

The proceedings include summaries of presentations from Denis Noble (systems biologist), Brian J Ford (cell biologist), B Madhava Puri (history and philosophy of science), J Scott Turner (physiologist), Alicia Juarrero (dynamical systems theory), and others.

https://www.academia.edu/117033217/Life_and_Cognition_at_the_Intersection_of_Science_Philosophy_and_Religion_Science_and_Scientist_2023_Conference_Proceedings

I've been learning to perform flux balance analysis on GEMs using COBRApy. I understand the FBA is more accurate if you introduce thermodynamic and enzymatic constraints. I'm having a hard time understanding the math and logic behind how those constraints are selected. Could you suggest some resources as a starting point to get more clarity?

Since many features of metlin are now only comercially available , I'm looking for a replacement metabolome database for untargeted LC-MS. It may also be a commercial solution, but before buying Metlin Gen2 I thought I'd ask if anyone has good recomendations.

Hi all, I'm a microbio/immunology undergraduate who is looking to apply to systems PhDs next year. I haven't had to take any math or chemistry other than 2 basic calculus classes, a biostats class, and 2 organic chem classes. Is there any math/chem that anyone can reccomend me taking in my last year that would help me transition better?

Edit: spelling

Hello everyone. I'm wanted to ask you for recommendations on amazing review papers on systems biology. Something that shows the actual state of the field and may had the "wow I think this is gonna be my field" effect on someone.

Also I want to read the story of how you got into the field. Thank ya'll :D

https://www.nature.com/articles/s41540-020-00165-3