How Scientists Map a Bacterium's Energy Grid
Forget fossil fuels; imagine microbes generating electricity while cleaning up toxic waste. This isn't science fiction – it's the remarkable reality of bacteria like Shewanella oneidensis MR-1.
Dubbed "electric bacteria," Shewanella possesses a unique talent: it can "breathe" metals and minerals instead of oxygen, shuttling electrons directly onto solid surfaces like rust or even electrodes.
Understanding how this tiny powerhouse manages its internal energy flow – its metabolism – under changing conditions is key to unlocking its potential for bioenergy, bioremediation, and beyond.
Think of a bacterium like Shewanella as a complex factory. Raw materials (nutrients) come in. A vast network of conveyor belts and machines (enzymes and metabolic reactions) process these materials. The outputs are new cellular components (for growth) and waste products.
Bacteria don't live in unchanging labs. In the real world – say, in polluted groundwater or a bioelectrochemical device – nutrient levels fluctuate, electron acceptors (like oxygen or iron) appear and disappear, and pH shifts. Metabolism is inherently dynamic.
Traditional FBA gives a single, optimal "snapshot" for one specific set of conditions. But what happens over time as conditions change? Running a full dynamic simulation involving every reaction is computationally monstrous.
This is where the Static Optimization Approach (SOA) offers a clever workaround by approximating a dynamic process by stitching together a series of optimized "static" metabolic states.
SOA approximates a dynamic process by stitching together a series of optimized "static" metabolic states, each reflecting the environment at the moment the interval begins.
A pivotal study demonstrated the power of FBA-SOA for Shewanella oneidensis MR-1 . Let's break down a typical experimental simulation approach:
| Component | Concentration | Purpose |
|---|---|---|
| Lactate | 20 mM | Primary carbon and energy source |
| Oxygen (O₂) | 8 mM | Initial electron acceptor |
| Fumarate | 5 mM | Secondary electron acceptor |
| Fe(III) | 10 mM | Tertiary electron acceptor |
| Time Interval (hrs) | Dominant Electron Acceptor | Growth Rate (1/hr) |
|---|---|---|
| 0 - 4 | Oxygen (O₂) | High (~0.35) |
| 4 - 12 | Fumarate | Medium (~0.15) |
| 12 - 24 | Iron (Fe(III)) | Low (~0.08) |
| >24 | N/A | ~0.0 |
| Substrate | Aerobic Phase | Fumarate Phase | Fe(III) Phase |
|---|---|---|---|
| Lactate (mM/hr) | 4.5 | 1.8 | 0.9 |
| O₂ (mM/hr) | 2.0 | 0.0 | 0.0 |
| Fumarate (mM/hr) | 0.1 | 0.5 | 0.0 |
| Fe(III) (mM/hr) | 0.0 | 0.05 | 0.3 |
This FBA-SOA simulation successfully recapitulates the experimentally observed behavior of Shewanella in dynamic environments. It demonstrates that the core metabolic network, when optimized for growth under changing external constraints, inherently possesses the capability to "decide" which electron acceptor to use based on availability and energetic yield.
The FBA-SOA approach to studying Shewanella's dynamic metabolism is more than just a computational exercise. It provides a powerful virtual testbed:
Predict how to optimize conditions for faster pollutant cleanup or higher power output in microbial fuel cells.
Identify key metabolic bottlenecks or promising gene knockout targets to enhance specific functions.
Model how Shewanella influences geochemical cycles in sediments and groundwater.
Test hypotheses about metabolic capabilities quickly and cheaply before lab experiments.
By combining the comprehensive blueprint of the genome-scale model with the practical efficiency of the Static Optimization Approach, scientists are mapping the intricate electron highways within Shewanella oneidensis MR-1.