Mathematical models for siderophore production and siderophore mediated antagonism in bacterial populations under iron limitation
Bibliographic record
Abstract
Although iron is the most abundant transition metal on earth, its solubility is very low and therefore its bioavailability is poor. Many microorganisms have developed iron chelating systems and often produce siderophores which are iron chelators secreted by the cell. In particular, Pseudomonads are prominent producers of a siderophore called pyoverdine that has a high iron binding capability. The pseudomonas-pyoverdine system is modeled through a system of nonlinear differential equations that explicitly takes into account the transient adaptation (lag phase) of the average physiological state of the population to the environmental conditions. Various lag phase models are considered and a parameter identification study is conducted. The theoretical properties of the model, the model behavior at small and large times, and local stability are analyzed. A new competition model based on iron chelation is also developed. A population of a chelator microorganism (e.g., Pseudomonas) and a non-chelator (e.g., a pathogen) is considered. The chelator is assumed to produce an iron-chelating siderophore which binds ferric ions and makes them available only to the chelator and not to the competing microorganism. A qualitative analysis of the model for the batch case (no inflow or outflow) is carried out and the global behavior of the model variables is studied. For the chemostat case, the equilibrium points are derived and their local stability is studied. The principle of competitive exclusion that bases survivability of the competing species on their break even points is found not to apply. An optimal finite-time control strategy is proposed that aims at manipulating the microbial community by preemptive colonization to displace the pathogenic bacteria through competition for iron. Pontryagin's Minimum Principle is used to characterize the optimal control and the optimality system composed of state and adjoint differential equations is numerically solved. Existence results are established and various simulations are carried out to illustrate the technique. It is found that through the optimal feeding strategy, it is possible to move from an existing non-desired equilibrium whereby the pathogen outcompetes Pseudomonas to a more desirable equilibrium where the pathogen gets extinct.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".