Daniele de Martino - Biofisika Institute, Bilbao, Spain
Biography
Title: The metabolic goals of cells: lesson from single cell flux analysis
Abstract: Is cellular metabolism optimized, for instance, to maximize growth? This assumption is common in system-level approaches such as Flux Balance Analysis (FBA), which provides useful insights and is theoretically supported by competitive evolutionary dynamics. However, I will demonstrate that this hypothesis is not backed by flux data, even under ideal conditions in E. coli experiments. Moreover, it conflicts with the well-established observation of single-cell heterogeneity. By incorporating this into models using statistical methods like maximum entropy (MaxEnt), we can quantitatively explain the flux data. Extending this approach to full inverse modeling reveals that the linear objective function framework of optimization offers limited predictive power and comes at the expense of explainability. Heterogeneity, on the other hand, allows for intercellular exchange interactions, a crucial factor often overlooked in standard models. I will present recent experimental findings from single-cell flux analysis that demonstrate this point:
1) Tumor-stroma co-cultures: These systems either collectively maintain the medium's homeostasis or fail to do so, manifesting the Warburg effect. Statistical physics modeling of this data reveals that the Warburg threshold represents a formal phase transition, where the key driver of acidification is a lack of coordination, rather than local hypoxia or mitochondrial saturation.
2) Cyanobacterial metabolic ecology: In a simple flask, cyanobacterial cells exhibit complex metabolic behaviors, forming sub-clusters that alternate between optimal nitrogen and carbon yields. There are also acid exchanges, and extreme cells with metabolic rates double the average, as well as ‘fixer’ cells, which exhibit slow growth but maintain a high metabolic rate.
These findings highlight the limitations of traditional optimization approaches and the need to account for cellular interactions to really understand metabolic systems.
DDM et al (2018) Nat. comm., 9(1), 2988.
Muntoni et al (2022) Biophysical Journal 121.10: 1919-1930
Onesto et al (2023) ACS Nano, 17, 4, 3313–3323
Narayanankutty et al. (2024) arXiv:2405.13424
