We’re excited about the potential of gIOC to deepen our understanding of the living world and welcome your thoughts and feedback.

👉 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗽𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗵𝗲𝗿𝗲: authors.elsevier.com/sd/article/S...

Our paper introduces 𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗜𝗻𝘃𝗲𝗿𝘀𝗲 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 (𝗴𝗜𝗢𝗖), a data-driven framework to reverse-engineer these fundamental principles directly from observations.

Nature is filled with optimal designs, from efficient metabolic pathways to the hexagonal structure of honeycombs. But how can we uncover the hidden rules (the objectives and constraints)?

⚙️ We cover recent advances and open challenges in:
- Model structure selection
- Identifiability
- Parameter estimation
- Uncertainty quantification
- Model reliability

If you are interested in how mathematical models help unravel biological complexity, we hope you will find it useful!

We tackle key challenges in ODE-based models: identifiability, instability, underfitting, & overfitting.

Our integrated approach offers a roadmap to more reliable & predictive microbiome models.

🔬 Check it at: doi.org/10.1101/2025... #Microbiome #Ecology #Modeling #MicrobialCommunities

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