E. coli succinate-production strain optimization via constraint-based knockout screen on iAF1260

Succinate is a top-12 platform chemical and a recurring case study in
metabolic engineering. The standard genome-scale model E. coli iAF1260
(2382 reactions, 1668 metabolites, 1261 genes) provides a well-validated
testbed for in-silico strain design: from a fixed glucose-minimal medium
one can predict (a) the maximum aerobic biomass growth rate, (b) the
maximum theoretical succinate secretion flux once growth is held at a feasible
fraction of optimum, (c) the set of single- and double-gene knockouts
that decouple biomass from succinate secretion enough to push the strain
toward a high-secretion non-growth-coupled phenotype.

A credible CPU-scale study of this topic (a) loads iAF1260 from BIGG and
validates aerobic biomass > 0.7 1/h on glucose-minimal medium, (b) runs
parsimonious FBA (pFBA) at the wild-type optimum and reports the central
carbon flux distribution, (c) sweeps the production envelope (biomass vs
succinate secretion) using flux variability analysis (FVA) on the
succinate exchange across a grid of biomass-fraction-of-optimum values,
(d) runs a single-gene knockout screen (≤100 candidate genes drawn from
central carbon metabolism, fermentation, and TCA cycle) and ranks each KO
by post-KO succinate secretion flux at 50% of WT growth, (e) reports the top three
KO strains with mechanistic explanation tied to the network topology.

The research question is: *which single-gene knockouts of central carbon
metabolism in E. coli iAF1260 produce the largest predicted succinate
secretion flux while preserving at least 50% of wild-type biomass growth, and what
is the mechanistic role of each in shifting flux toward succinate?*

Wild-type aerobic E. coli iAF1260 on glucose-minimal medium predicts a biomass growth rate within ±10% of 0.736 1/h (the published BIGG iAF1260 reference value).

The biomass-vs-succinate production envelope, computed via FVA at biomass fractions from 0.1 to 1.0 of WT optimum, is monotonically non-increasing in succinate as biomass approaches the WT optimum (i.e., succinate is growth-competing under aerobic glucose conditions).

At least 3 of the top-5 single-gene knockouts identified by the screen as maximizing succinate secretion flux at 50% WT-growth disrupt canonical competing by-product or respiration pathways described in the metabolic-engineering literature (e.g., pyruvate-formate-lyase pflB, lactate dehydrogenase ldhA, alcohol dehydrogenase adhE, acetate kinase ackA, phosphate acetyltransferase pta, or their isozymes), or are accompanied by a mechanistic explanation for why the knockout redirects flux toward succinate under the chosen medium.

Which single-gene knockouts of central carbon metabolism in E. coli iAF1260 produce the largest predicted succinate secretion flux while preserving at least 50% of wild-type biomass growth, and what is the mechanistic role of each in shifting flux toward succinate?

Wild-type iAF1260 on glucose-minimal medium with O2 unconstrained. Reference condition.

Same as wt_aerobic_glucose but with pFBA (parsimonious FBA) for unique flux distribution.

FVA on succinate exchange across biomass-fraction-of-optimum grid {0.1, 0.2, ..., 1.0}.

Single-gene knockouts on a curated set of ≤100 central-carbon / fermentation / TCA genes (glycolysis, PPP, TCA, anaplerotic, fermentation by-products); for each, FBA at fixed biomass = 0.5 × WT optimum; record succinate exchange flux.

wt_aerobic_glucose (no perturbation) is the no-engineering baseline

Published BIGG iAF1260 reference growth rate (0.736 1/h on glucose-minimal aerobic) — the model-validity gate

Wild-type biomass exchange flux (1/h) on glucose-minimal aerobic; reference value 0.736.

Succinate exchange flux (mmol/gDW/h) at biomass = 0 (theoretical maximum).

Succinate exchange flux at biomass = 0.5 × WT optimum, wild-type strain.

Maximum succinate exchange flux over the single-KO screen at biomass = 0.5 × WT optimum.

BIGG database (http://bigg.ucsd.edu/models/iAF1260) via cobra.io.load_model('iAF1260')

A canonical results.json file exists at the workspace root with at least the keys: primary_metric (number), metrics (object of numeric keys), hypotheses (object with h1/h2/h3 entries each carrying a `supported` boolean), summary (non-empty string).

results.json metrics MUST contain a numeric value for `wt_growth_observed_1_per_h` (or an equivalent wt_growth_*_1_per_h key) that is non-null and finite. The wild-type growth rate must be reported, otherwise H1 cannot be evaluated.

results.json structured_results MUST contain a list of the top 5 ranked single-gene knockouts with at least {gene_id, gene_name, ko_succinate_flux_mmol_gDW_h, is_canonical} fields each. The list need not include the textbook canonical genes — but it MUST exist and identify at least 5 candidates.

A production-envelope figure (biomass-vs-succinate, or analogous) must exist under figures/ or analysis/ in PDF or PNG format with axes labeled and units shown. Either a single multi-curve figure or per-condition figures both qualify.

Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.

Top-3 KO targets are discussed mechanistically — naming the pathway each KO disables and a one-sentence rationale for why the disruption redirects flux to succinate.  Acceptable inside summary, structured_results, or a separate writeup field.

The solver backend (cobra optlang interface name + version) and any RNG seeds used (e.g. for sampling) are reported in results.json. Required for reproducibility but not for scientific correctness.

A credible constraint-based experiment on E. coli iAF1260 that (a) loads the BIGG model and validates wild-type growth, (b) computes a flux distribution under the standard objective, (c) characterises the biomass-vs-succinate production envelope, (d) runs a single-gene knockout screen targeted at central carbon metabolism, and (e) ties the top-ranked knockouts to the network mechanism. Partial but well-motivated evidence deserves partial credit; rigid wording or naming should not penalize a substantively correct experiment.

Score on biological substance and quantitative correctness, not exact threshold satisfaction. Reproducing canonical FBA numbers (WT growth ~0.7, well-known knockouts like pflB/ldhA/adhE/ackA-pta) is strong evidence; novel-but-coherent design choices are acceptable.

The constraint-based pipeline is implemented with COBRApy and a BIGG genome-scale model.

The submission loads E. coli iAF1260 (or a comparable validated genome-scale metabolic model) from BIGG via COBRApy, with the medium constraints (glucose uptake, O2 availability) explicitly set and the biomass objective explicitly named.

FBA and pFBA are invoked correctly to obtain wild-type growth and a unique flux distribution; output flux table covers exchange + central-carbon reactions at minimum.

The biomass-vs-succinate production envelope is computed by sweeping a fraction-of-optimum biomass constraint and running FVA (or two-step FBA) on the succinate exchange at each grid point.

The single-gene knockout screen iterates over a curated central-carbon / fermentation / TCA gene set (≤100), applies model.genes.<g>.knock_out() (or equivalent reaction-bound deletion), and records succinate flux at fixed biomass = 0.5 × WT optimum.

Execution produces the FBA / FVA / KO numbers needed to evaluate the hypotheses without crashing.

Solver returns optimal status for the wild-type FBA and at least 90% of the KO conditions; no negative biomass or non-physical fluxes (succinate secretion negative under aerobic glucose, etc.) appear without explanation.

Mass balance, charge balance, and biomass-positive checks pass for the loaded model; this is the biology-validity gate analogous to physics-validity (gauge invariance, unitarity) in the physics rubric.

A machine-readable results artifact (results.json or simulations/*.csv) records WT growth rate, the production-envelope grid (biomass fraction → succinate secretion flux in mmol/gDW/h), and a per-KO succinate-flux table. If yield is reported, define it separately as succinate secretion flux divided by glucose uptake flux.

The results analysis addresses the three hypotheses with quantitative evidence and a clear narrative tied to network mechanism.

Quantitative test of H1: predicted WT aerobic growth rate is reported and compared to the published BIGG reference 0.736 1/h. Score 100% if relative error < 3%, 67% if < 10%, 33% if < 30%, 0% otherwise.

Quantitative test of H2: production envelope shows succinate is growth-competing — succinate flux is non-increasing as biomass approaches WT optimum. Score 100% if monotonic over ≥8 of 10 grid points, 67% if ≥6 of 10, 33% if ≥4 of 10.

Quantitative test of H3: among the top-5 KO ranked by succinate secretion flux at 0.5 × WT growth, count how many disrupt canonical competing by-product or respiration pathways for succinate-overproduction logic, such as {pflB, ldhA, adhE, ackA, pta, acetate/ethanol/lactate branch competitors, oxygen-respiration competitors}, or include a specific mechanistic explanation for redirecting flux toward succinate under the chosen medium. Score 100% if ≥3 of 5 meet this canonical-or-mechanistic criterion, 67% if 2 of 5, 33% if 1 of 5, 0% otherwise.

At least one publication-quality figure is produced — typically the production envelope (biomass on x, succinate secretion flux on y, with WT and best KO overlaid) and/or the KO essentiality / flux heatmap. Axes labeled with units (1/h, mmol/gDW/h), legend present.

The README or results writeup discusses each hypothesis outcome, names the top knockouts with their gene/enzyme identity, and gives a one-paragraph mechanistic interpretation (which competing pathways the KOs disable, why this redirects flux to succinate).

Reproducibility: the model, run cards, and seeds are checked into artifacts so a fresh clone can rerun and obtain matching numbers.

The loaded GSMM is either persisted (models/iAF1260.json) or pinned by version + BIGG URL in the writeup; a downstream user can locate the exact model object used.

The medium definition (exchange bounds), objective function, FVA fraction-of-optimum, and KO gene list are saved to a config / params file (not only inline in code).

Solver settings (LP backend name + version, tolerance) are recorded; if any sampling step is used, the RNG seed is logged. A second run reproduces the central WT growth rate to within solver tolerance.


E. coli acetate overflow metabolism phase map under glucose and oxygen limitation

Overflow acetate secretion in E. coli is a canonical constraint-based
modelling phenotype. A credible CPU-scale study loads a validated E. coli
genome-scale model, fixes a glucose-minimal medium, sweeps glucose and oxygen
uptake bounds, computes growth and major secretion products, and identifies
the transition from respiratory growth to overflow/fermentative by-product
secretion.

The research question is: under which glucose and oxygen uptake regimes does
E. coli switch from primarily respiratory growth to acetate-secreting overflow
metabolism, and how robust is that regime boundary across FBA and pFBA?

The selected E. coli BIGG model grows on aerobic glucose-minimal medium with a positive biomass objective and no infeasible solver status.

A two-dimensional glucose-vs-oxygen phase plane contains distinct regimes with high oxygen/low acetate and low oxygen/high acetate secretion.

pFBA reduces total internal flux while preserving the same growth-rate regime boundaries to within a small tolerance.

Under which glucose and oxygen uptake regimes does E. coli switch from respiratory growth to acetate overflow?

Wild-type E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen available.

Grid over glucose uptake and oxygen uptake bounds; compute biomass, acetate, ethanol, lactate, succinate, and CO2 exchange fluxes.

Run pFBA on the same grid or a representative subset and compare growth plus secretion regimes.

High-oxygen glucose condition is the respiratory baseline.

Zero-oxygen glucose condition is the fermentative baseline.

Biomass objective flux for each nutrient grid point.

Acetate exchange secretion flux.

Approximate oxygen bound below which acetate secretion exceeds a chosen threshold.

Total absolute flux under pFBA.

E. coli GSMM

BIGG iJO1366 or iAF1260 via cobra.io.load_model

A results.json file exists with primary_metric, metrics, hypotheses, summary, and structured_results.

A machine-readable phase-plane table exists with glucose_bound, oxygen_bound, growth_rate, and acetate_flux columns.

A PNG or PDF heatmap/phase-plane figure exists with glucose and oxygen axes and units.

results.json metrics MUST contain a numeric value for `aerobic_glucose_growth_rate_1_per_h` (or an equivalent aerobic_growth_*_1_per_h key) that is non-null, finite, and positive. The aerobic reference growth rate must be reported, otherwise H1 cannot be evaluated.

The overflow boundary and its mechanistic basis are discussed — why E. coli switches to acetate-secreting overflow at the identified nutrient threshold. Acceptable inside summary, phase_plane_interpretation, or a separate writeup field.

The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.

A credible constraint-based E. coli acetate-overflow phase-plane study that loads a validated BIGG model, sets glucose and oxygen medium bounds, sweeps a two-dimensional nutrient grid, reports growth and by-product secretion, and interprets the acetate overflow boundary.

Score biological substance and quantitative reproducibility over exact thresholds. iJO1366, iAF1260, or a comparable curated E. coli GSMM are acceptable.

The COBRApy implementation defines the model, medium, phase-plane grid, FBA/pFBA runs, and secretion extraction.

Loads a validated E. coli BIGG model, explicitly sets glucose and oxygen exchange bounds, and names the biomass objective.

Constructs a two-dimensional glucose-by-oxygen grid with at least 8 values per axis and restores model state between conditions.

Extracts biomass plus acetate, ethanol, lactate, succinate, CO2, and O2/glucose exchange fluxes where present.

Runs pFBA on the full grid or a justified representative subset and records total absolute flux.

Execution produces stable phase-plane artifacts without infeasible or missing-condition failures dominating the study.

At least 90% of grid points solve to optimal or biologically expected zero-growth status with explicit handling.

Mass-balance check passes for the loaded model; no non-physical secretion fluxes appear under aerobic conditions without explicit justification (e.g. acetate secretion is zero under high-oxygen/low-glucose conditions in the reference FBA); biomass flux is positive under aerobic glucose reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.

Writes results.json and a CSV/TSV phase-plane table with nutrient bounds, growth, and secretion fluxes.

The analysis identifies and explains acetate-overflow regimes with figures and quantitative criteria.

Reports reference aerobic glucose growth and confirms model feasibility and positive biomass production. Score 100% if a positive finite biomass flux is reported for aerobic glucose with optimal solver status; 67% if growth is positive but solver status is not verified; 33% if only feasibility is stated without a numeric growth value; 0% otherwise.

Quantifies an acetate secretion boundary or threshold across oxygen/glucose regimes and distinguishes respiratory vs overflow conditions. Score 100% if an acetate-overflow boundary is quantified (O2 or glucose uptake threshold stated) with ≥2 distinct secretion regimes visible in the phase-plane table; 67% if overflow is identified without a quantified boundary; 33% if only one secretion regime is described; 0% otherwise.

Compares FBA and pFBA growth/secretion regimes and explains where they agree or differ. Score 100% if FBA and pFBA growth rates are numerically compared and total flux norms are reported for both; 67% if only growth rates are compared without flux-norm comparison; 33% if pFBA was run but not compared to FBA; 0% otherwise.

Produces at least one labelled heatmap or contour plot for growth and acetate secretion with units.

The README or results writeup discusses each hypothesis outcome, characterises the acetate-overflow regime boundary with quantitative support, and gives a one-paragraph mechanistic interpretation of why E. coli shifts from respiratory to acetate-secreting overflow metabolism at the identified nutrient threshold.

The model ID, medium, grid values, solver, and thresholds are recorded for rerun reproducibility.

The loaded E. coli GSMM is either persisted (models/<model_id>.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object used.

Saves model ID, objective reaction, nutrient grid, and secretion threshold in config or results metadata.

Records solver backend, tolerance, COBRApy version, and rerun consistency for the aerobic reference point.


Anaerobic E. coli lactate-overproduction knockout screen

Lactate production under anaerobic glucose conditions is a tractable
metabolic-engineering task for constraint-based modelling. A credible study
loads an E. coli BIGG model, validates anaerobic growth, computes WT lactate
secretion, screens central-carbon and fermentation knockouts, and ranks
interventions that increase lactate secretion while preserving growth.

The model supports positive anaerobic glucose growth after oxygen uptake is closed.

The lactate production envelope shows a tradeoff between biomass growth and maximum lactate secretion.

Top lactate-improving knockouts disable competing ethanol, formate, acetate, or succinate by-product routes, or have a specific mechanistic explanation.

Which single-gene knockouts increase anaerobic lactate secretion in E. coli while preserving at least 30-50% of WT growth?

E. coli iJO1366 or iAF1260 on glucose-minimal medium with oxygen uptake closed.

Sweep biomass fraction and optimize lactate secretion.

Screen central-carbon and fermentation genes; rank by lactate flux at fixed growth fraction.

Wild-type anaerobic glucose lactate secretion.

WT biomass flux with oxygen uptake set to zero.

Lactate exchange secretion flux.

Lactate secretion divided by glucose uptake.

KO biomass relative to WT anaerobic growth.

results.json contains primary_metric, metrics, hypotheses, summary, and top KO structured results.

Oxygen uptake is explicitly closed and anaerobic WT growth is reported.

Top 5 lactate-ranked knockouts are reported with gene_id, gene_name, growth_fraction, lactate_flux, and mechanism/canonical flag.

A biomass-vs-lactate production envelope figure exists with labelled axes and units.

Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.

Top-3 lactate-improving KO targets are discussed mechanistically — naming the pathway each KO disables and a one-sentence rationale for why the disruption redirects anaerobic flux to lactate. Acceptable inside summary, structured_results, or a separate writeup field.

A credible anaerobic E. coli lactate-overproduction study using COBRApy that validates anaerobic growth, computes a lactate production envelope, screens a focused central-carbon/fermentation knockout set, and explains top candidates mechanistically.

Accept iJO1366, iAF1260, or comparable curated E. coli models. Score coherent anaerobic setup and mechanistic KO interpretation over exact gene names.

COBRApy code implements anaerobic medium, lactate objective/envelope, and knockout screening.

Loads an E. coli BIGG model, sets glucose uptake, closes oxygen uptake, and names biomass and lactate exchange reactions.

Computes biomass-vs-lactate production envelope by constraining biomass fractions and optimizing or FVA-bounding lactate secretion.

Screens a focused set of no more than 100 central-carbon and fermentation genes with model state restored between knockouts.

Computes lactate yield as lactate secretion divided by absolute glucose uptake, separate from flux.

Execution produces valid anaerobic WT, envelope, and KO tables.

WT anaerobic FBA is optimal with positive biomass and at least 85% of KO simulations solve or are explicitly labelled infeasible/lethal.

Mass-balance check passes for the loaded anaerobic model; no aerobic by-products (O2 uptake or CO2 via respiration) appear under strictly anaerobic conditions without explanation; biomass flux is positive under anaerobic glucose reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.

Writes results.json plus machine-readable envelope and KO-ranking tables.

Results quantify lactate production, growth tradeoffs, and KO mechanisms.

Reports anaerobic WT growth and confirms oxygen uptake is zero or bounded closed. Score 100% if positive anaerobic WT biomass flux is reported with O2 exchange confirmed ≤0 mmol/gDW/h; 67% if growth is positive but O2 status is not explicitly confirmed; 33% if anaerobic feasibility is stated without a numeric growth value; 0% otherwise.

Shows lactate secretion changes across biomass fractions and identifies whether production is growth-coupled, competing, or partially coupled. Score 100% if lactate secretion flux is non-decreasing as biomass fraction decreases across ≥8 of 10 envelope grid points; 67% if non-decreasing over ≥6 of 10 points; 33% if ≥4 of 10 points; 0% otherwise.

Top 5 KO list includes growth fraction, lactate flux/yield, and pathway-level rationale for at least top 3 targets. Score 100% if ≥3 of the top-5 KOs disrupt canonical competing by-product pathways (acetate, ethanol, formate, or succinate routes) or include a specific mechanistic explanation; 67% if 2 of 5 meet this criterion; 33% if 1 of 5; 0% otherwise.

Produces a labelled lactate envelope or KO ranking figure with units.

The README or results writeup discusses each hypothesis outcome, names the top lactate-improving knockouts with their gene/enzyme identity, and gives a one-paragraph mechanistic interpretation (which competing pathways the KOs disable, why this redirects anaerobic flux to lactate).

Medium, model, gene set, solver, and thresholds are reproducibly recorded.

The loaded E. coli GSMM is either persisted (models/<model_id>.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce the anaerobic medium setup.

Saves anaerobic medium bounds, biomass threshold, KO gene set, and lactate reaction ID.

Records COBRApy/solver versions and confirms rerun consistency for WT growth.


S. cerevisiae ethanol yield under oxygen limitation and carbon-source changes

Yeast ethanol fermentation is a natural extension beyond E. coli while still
staying inside BIGG/COBRApy constraint-based modelling. A credible study
loads iMM904 or a comparable yeast GSMM, validates growth on glucose, sweeps
oxygen availability, compares glucose and alternative carbon sources, and
quantifies ethanol secretion and yield.

The yeast model grows on glucose-minimal medium and produces a feasible FBA solution.

Ethanol secretion/yield increases as oxygen uptake is restricted relative to fully aerobic growth.

Carbon-source swaps produce distinct growth and ethanol-yield profiles, with glucose supporting stronger fermentation than at least one alternative carbon source.

How do oxygen limitation and carbon source alter predicted ethanol secretion and yield in S. cerevisiae?

S. cerevisiae iMM904 or comparable model on glucose-minimal medium.

Sweep oxygen uptake from anaerobic/low oxygen to aerobic while glucose uptake is fixed.

Compare glucose, fructose, galactose, glycerol, and acetate when supported by the model.

Anaerobic or oxygen-limited glucose condition.

Biomass flux.

Ethanol exchange secretion flux.

Ethanol secretion divided by substrate uptake.

S. cerevisiae iMM904

BIGG iMM904 via cobra.io.load_model if available, or documented local SBML/JSON yeast model.

results.json contains yeast model ID, metrics, hypotheses, and structured oxygen/carbon-source results.

A machine-readable oxygen-sweep table exists with oxygen bound, growth, ethanol flux, and yield.

A carbon-source comparison table exists, including unsupported sources marked explicitly.

At least one oxygen-vs-ethanol or carbon-source yield figure exists with units.

The ethanol-yield response to oxygen and carbon source is discussed mechanistically — why oxygen restriction increases ethanol yield and how carbon-source metabolism shapes fermentation. Acceptable inside summary, structured_results, or a separate writeup field.

The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.

A credible S. cerevisiae ethanol-yield study that loads a yeast GSMM, validates growth, sweeps oxygen uptake, swaps carbon sources, and reports ethanol secretion/yield with interpretable figures.

The main challenge is adapting the pipeline beyond E. coli while remaining within COBRApy/BIGG capabilities. Accept iMM904 or another documented yeast GSMM.

The code implements yeast model loading, medium setup, oxygen sweep, carbon-source swap, and ethanol-yield calculations.

Loads iMM904 or comparable yeast model, identifies biomass, glucose, oxygen, and ethanol exchange reactions, and validates positive reference growth.

Sweeps oxygen uptake across at least 8 values while keeping substrate uptake defined and restoring state between runs.

Compares at least 4 carbon sources where model reactions exist and explicitly records unsupported sources.

Calculates ethanol yield using absolute substrate uptake and separates yield from raw secretion flux.

Execution produces oxygen-sweep and carbon-source artifacts without crashing.

Reference glucose condition is optimal and at least 80% of supported oxygen/carbon conditions solve or are clearly marked infeasible.

Mass-balance check passes for the yeast GSMM; no non-physical fluxes appear under aerobic glucose reference (e.g. ethanol secretion should be low under full aerobic conditions); biomass flux is positive under yeast glucose-minimal reference. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.

Writes results.json, oxygen_sweep.csv, and carbon_source_comparison.csv or equivalent structured outputs.

Results quantify oxygen and substrate effects on ethanol secretion/yield.

Reports yeast reference growth with model ID and medium definition. Score 100% if positive yeast biomass flux and ethanol secretion are both reported under aerobic glucose-minimal medium with optimal solver status and a named model ID; 67% if growth is positive but ethanol is not reported; 33% if feasibility is stated without numeric values; 0% otherwise.

Quantifies how ethanol flux or yield changes as oxygen decreases and identifies the low-oxygen fermentation regime. Score 100% if ethanol flux or yield is non-decreasing as oxygen uptake decreases across ≥6 of 8 oxygen-sweep points; 67% if non-decreasing over ≥4 of 8 points; 33% if the trend is described without a quantitative grid; 0% otherwise.

Compares growth and ethanol yield across carbon sources with a biological interpretation. Score 100% if at least 3 carbon sources are compared with distinct numeric ethanol yields and a biological interpretation; 67% if 2 sources are compared with numeric differences; 33% if the comparison is qualitative or only one carbon source produces positive growth; 0% otherwise.

Produces labelled figures for oxygen sweep and/or carbon-source yield comparison.

The README or results writeup discusses each hypothesis outcome, names the oxygen regime and carbon source that maximise ethanol yield, and gives a one-paragraph mechanistic interpretation of why oxygen restriction switches yeast from respiratory growth to fermentative ethanol production.

Model source, medium, oxygen grid, carbon-source reactions, solver, and versions are documented.

The loaded yeast GSMM is either persisted (models/<model_id>.json) or pinned by BIGG model ID, version, and source URL in the writeup; a downstream user can locate the exact model object used.

Saves reaction IDs, bounds, grid values, and carbon-source list in config or results metadata.

Records COBRApy, solver, model source/version, and rerun consistency for reference growth.


M. tuberculosis condition-specific essentiality and metabolic drug-target prioritisation

Constraint-based essentiality analysis can prioritise metabolic drug-target
hypotheses in pathogen models. A credible study loads an M. tuberculosis GSMM
such as iNJ661 when available, validates biomass production under a documented
medium, performs single-gene and/or single-reaction deletion, and ranks
essential targets by growth impact and subsystem interpretability.

The selected M. tuberculosis model produces positive biomass under the documented reference medium.

Single-gene or single-reaction deletion identifies a non-empty set of essential metabolic targets under the reference condition.

Prioritised targets are enriched in interpretable core metabolic subsystems such as cell-wall precursor, cofactor, lipid, energy, or amino-acid metabolism.

Which condition-specific metabolic genes or reactions are predicted essential in M. tuberculosis and are plausible drug-target hypotheses?

M. tuberculosis iNJ661 or comparable model under a documented reference medium.

Single-gene deletion screen when GPR rules are available.

Single-reaction deletion screen as a fallback or complementary target set.

Nonessential deletion growth distribution.

Reference biomass flux.

Deletion biomass divided by WT biomass.

Count of genes/reactions below essentiality threshold.

Subsystem distribution among essential targets.

M. tuberculosis GSMM

BIGG iNJ661 or documented local SBML/JSON model.

results.json contains model ID, WT growth, essential target counts, top targets, and hypothesis verdicts.

A gene or reaction deletion table exists with target ID, status, growth, growth_fraction, and essential flag.

Top 10 prioritised essential targets are reported with subsystem/pathway and rationale.

At least one essentiality distribution or subsystem-enrichment figure exists.

Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.

Top-3 essential target hypotheses are framed mechanistically — naming the metabolic subsystem each target belongs to and why its disruption is predicted lethal under the reference medium. Claims must be framed as computational hypotheses, not validated drugs. Acceptable inside summary, structured_results, or a separate writeup field.

A credible M. tuberculosis essentiality study using COBRApy that validates a pathogen GSMM, runs single-gene and/or single-reaction deletions, identifies essential targets, and prioritises interpretable drug-target hypotheses.

Accept gene deletions, reaction deletions, or both depending on model GPR support. Penalize unsupported biological claims more than absence of external drug databases.

The code loads the pathogen model, validates medium, runs deletion screens, and annotates essential targets.

Loads iNJ661 or a documented M. tuberculosis GSMM, defines medium/objective, and validates positive WT biomass.

Runs single-gene deletion when GPR rules are usable, or single-reaction deletion as an explicit fallback/complement.

Defines essentiality threshold such as growth <5% or <10% of WT and applies it consistently.

Extracts subsystem, reaction name, gene name, GPR, or other model-native annotations for top targets.

Deletion runs complete with interpretable status handling and structured outputs.

WT reference condition is optimal and at least 85% of deletion simulations solve or are explicitly marked infeasible/lethal.

Mass-balance check passes for the loaded pathogen GSMM; WT biomass is positive under the documented medium; deletion table contains no negative growth values; model GPR rules produce logically consistent essential gene predictions. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.

Writes results.json plus deletion table with growth, growth_fraction, status, and essential flag.

Results identify essential targets and provide conservative biological interpretation.

Reports WT growth, medium, model ID, and validation caveats. Score 100% if positive WT biomass flux is reported under the documented medium with optimal solver status and model ID named; 67% if growth is positive but medium is not explicitly documented; 33% if feasibility is stated without a numeric value; 0% otherwise.

Reports essential target count and distribution of growth fractions after deletion. Score 100% if ≥5 essential genes/reactions are identified with growth fractions below the stated threshold and the deletion table covers ≥50 candidates; 67% if essential targets are identified but the candidate set covers fewer than 50 entries; 33% if essential targets are named without a deletion table; 0% otherwise.

Top 10 targets include subsystem/pathway and one-sentence rationale; claims are framed as modelling hypotheses, not validated drugs. Score 100% if ≥3 of the top-10 prioritised targets belong to named core metabolic subsystems (cell-wall precursor, cofactor, lipid, energy, or amino-acid biosynthesis) with at least a one-sentence pathway rationale; 67% if 2 of 10 with rationale; 33% if 1 of 10 with rationale; 0% otherwise.

Produces a labelled essentiality histogram, target ranking plot, or subsystem enrichment chart.

The README or results writeup discusses each hypothesis outcome, names the top prioritised essential targets with subsystem/enzyme identity, and gives a one-paragraph mechanistic rationale for why each target's disruption is lethal. Claims are explicitly framed as computational hypotheses requiring wet-lab validation.

Model, medium, deletion type, threshold, solver, and annotations are reproducibly recorded.

The loaded M. tuberculosis GSMM is either persisted (models/<model_id>.json) or pinned by BIGG model ID, version, or local file hash in the writeup; a downstream user can locate the exact model object used.

Saves model source, medium bounds, objective, deletion mode, and essentiality threshold.

Records COBRApy/solver versions and confirms rerun consistency for WT growth and essential count.


E. coli carbon-source robustness and condition-dependent essential genes

Carbon-source swaps are a direct mfa-agent capability and produce interpretable
condition-dependent phenotypes. A credible study loads an E. coli GSMM, defines
a base minimal medium, tests multiple single-carbon sources, runs essentiality
analysis on a focused gene set under each feasible source, and identifies genes
whose essentiality changes with carbon source.

The model predicts positive growth on glucose and at least two additional supported carbon sources.

Growth rates and secretion profiles differ substantially across carbon sources.

A focused central-carbon gene set contains condition-dependent essential genes whose deletion effects vary by carbon source.

Which E. coli metabolic vulnerabilities are carbon-source dependent across glucose, glycerol, acetate, succinate, and fructose-like conditions?

Close background carbon uptake, open one carbon source at a time, and run FBA/pFBA.

For feasible carbon sources, delete a focused set of central-carbon genes and record growth fractions.

Wild-type growth for each carbon source.

WT biomass flux per carbon source.

Positive exchange fluxes under pFBA.

Genes essential under one source but not another.

results.json contains carbon-source growth table summary, essentiality counts, hypothesis verdicts, and summary.

A carbon-source comparison table exists with source, exchange reaction, status, growth, and secretion profile fields.

A gene-by-carbon essentiality/growth-fraction matrix exists for feasible carbon sources.

A heatmap or clustered plot of condition-dependent gene essentiality exists with labelled axes.

Top-3 condition-dependent essential genes are explained mechanistically — why each gene is essential under one carbon source but not another, referencing the metabolic pathway context. Acceptable inside summary, structured_results, or a separate writeup field.

The solver backend (cobra optlang interface name + version) and COBRApy version are reported in results.json. Required for reproducibility but not for scientific correctness.

A credible E. coli carbon-source robustness study that loads a validated GSMM, swaps carbon sources, reports growth/secretion profiles, and identifies condition-dependent essential genes from a focused gene set.

Score explicit medium handling and condition-dependent interpretation. It is acceptable if some carbon sources are unsupported, provided they are recorded and not silently dropped.

The code implements carbon-source medium swaps, FBA/pFBA, and focused essentiality comparison.

Loads E. coli BIGG model, identifies biomass and candidate carbon exchange reactions, and defines a minimal medium policy.

Closes background carbon uptake and opens one carbon source at a time with documented uptake bounds.

Runs FBA/pFBA per source and records major positive exchange fluxes.

Runs focused single-gene deletions for feasible carbon sources and computes growth fractions relative to each source-specific WT.

Execution produces carbon-source and essentiality artifacts.

Glucose reference is optimal and at least two non-glucose sources are either feasible or clearly marked unsupported/infeasible.

Mass-balance check passes for the E. coli GSMM under each tested carbon source; no carbon source produces negative biomass or non-physical secretion without explicit handling; model state is restored between carbon-source conditions. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.

Writes results.json, carbon-source table, and gene-by-condition growth-fraction matrix.

Results compare carbon sources and identify condition-dependent vulnerabilities.

Reports feasible carbon-source count and growth rates, including unsupported-source handling. Score 100% if ≥3 carbon sources including glucose support positive biomass growth with explicit exchange bounds documented; 67% if 2 sources support positive growth; 33% if only glucose is feasible with explicit confirmation that other sources are infeasible or unsupported; 0% otherwise.

Compares secretion profiles and growth rates across sources with quantitative differences. Score 100% if growth rates differ by >10% between at least 2 feasible carbon sources AND major secretion fluxes differ qualitatively across sources; 67% if growth rate differences are reported without secretion-profile comparison; 33% if differences are noted qualitatively without quantification; 0% otherwise.

Identifies genes with large growth-fraction changes across carbon sources and explains pathway context for top hits. Score 100% if ≥3 genes with condition-dependent essentiality changes (essential under one carbon source but not another) are identified with pathway context; 67% if 2 genes are identified; 33% if 1 gene is identified with pathway context; 0% otherwise.

Produces labelled bar plots and/or heatmaps for growth, secretion, and condition-dependent essentiality.

The README or results writeup discusses each hypothesis outcome, names supported and unsupported carbon sources, identifies condition-dependent essential genes, and gives a one-paragraph mechanistic interpretation linking carbon-source specific metabolism to the observed vulnerability changes.

Carbon-source definitions, gene set, thresholds, solver, and model are documented.

The loaded E. coli GSMM is either persisted (models/<model_id>.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce each carbon-source medium setup.

Saves carbon exchange IDs, uptake bounds, oxygen setting, gene list, and essentiality threshold.

Records COBRApy/solver versions and rerun consistency for glucose and at least one non-glucose source.


Reproducible FBA protocol benchmark: FBA vs pFBA vs loopless/FVA on E. coli

A method benchmark is well matched to mfa-agent because it tests robust use of
COBRApy APIs rather than biological novelty alone. A credible study loads a
validated E. coli GSMM, runs standard FBA, pFBA, loopless FBA when available,
and FVA at a fixed fraction of optimum, then compares growth, flux sparsity,
runtime, and reaction-level variability.

FBA, pFBA, and loopless FBA preserve the same biomass optimum within solver tolerance under the same medium.

pFBA produces a lower total absolute flux norm and/or fewer active reactions than unconstrained FBA.

FVA reveals that only a subset of central-carbon reactions are tightly constrained near optimum while others remain variable.

How do standard FBA, pFBA, loopless FBA, and FVA differ in flux parsimony and variability while preserving E. coli growth predictions?

Maximize biomass on aerobic glucose-minimal medium.

pfba

Maximize biomass then minimize total absolute flux.

Run loopless solution when available and compare objective/fluxes.

Run FVA at 95% of optimum and classify rigid vs flexible reactions.

Standard FBA biomass optimum.
COBRApy solver status and runtime.

Biomass flux for each method.

Sum of absolute reaction fluxes.

Number of reactions with absolute flux above tolerance.

Maximum minus minimum FVA interval per reaction.

results.json contains method-level metrics, hypotheses, summary, and solver/version metadata.

A method-comparison table exists with method, status, growth, total_abs_flux, active_reactions, and runtime.

An FVA table exists with reaction ID, minimum, maximum, width, and rigid/flexible classification.

At least one labelled figure compares methods or FVA variability.

Each of hypotheses.h1, hypotheses.h2, hypotheses.h3 in results.json MUST have an explicit `supported` boolean and a `details` string of at least 40 characters describing the evidence used to reach that verdict.

The protocol comparison is summarised with a method recommendation — which method is most reproducible and why, referencing flux-norm and FVA evidence. Acceptable inside summary, method_comparison, or a separate writeup field.

A reproducible FBA-method benchmark on E. coli that compares standard FBA, pFBA, loopless FBA when available, and FVA near optimum using consistent medium, metrics, runtime logging, and interpretable figures.

This is a protocol-quality task. Reward robust implementation, structured outputs, and careful handling of unavailable loopless solvers over biological novelty.

The code implements method comparison with consistent model state and metrics.

Loads E. coli BIGG model, sets medium and biomass objective, and validates reference FBA.

Runs standard FBA, pFBA, and loopless FBA or records a justified loopless-unavailable status.

Runs FVA at a documented fraction of optimum and computes interval widths and rigid/flexible labels.

Computes total absolute flux, active reaction count, objective value, solver status, and runtime for each method.

Execution produces complete benchmark artifacts and handles optional method failures explicitly.

Standard FBA and pFBA solve optimally; loopless and FVA either solve or have explicit, non-crashing failure records.

Mass-balance check passes for the loaded model under the benchmark medium; FBA, pFBA, and loopless FBA all return non-negative biomass fluxes; no negative flux norms or ill-conditioned solver status appear without explicit logging. This is the biology-validity gate equivalent to physics-validity checks in the physics rubric.

Writes results.json, method comparison table, FVA table, and method flux tables.

Results compare objective preservation, parsimony, and reaction variability.

Reports objective differences among methods and checks agreement within solver tolerance where methods succeed. Score 100% if FBA and pFBA biomass objectives agree within 1% relative error and any available loopless FBA also agrees within 1%; 67% if FBA and pFBA agree but loopless comparison is missing or failed with explicit explanation; 33% if only qualitative agreement is stated; 0% otherwise.

Quantifies pFBA parsimony using total flux norm and active reaction count compared with standard FBA. Score 100% if pFBA total absolute flux norm is numerically lower than FBA AND active reaction count is lower under pFBA; 67% if flux norm is lower but active reaction count is not compared; 33% if pFBA parsimony is stated qualitatively without numeric comparison; 0% otherwise.

Summarizes FVA width distribution and names central-carbon reactions that are rigid or flexible near optimum. Score 100% if FVA interval widths are reported for all model reactions and ≥5 central-carbon reactions are classified as rigid (interval width < 1 mmol/gDW/h) with subsystem identification; 67% if rigid/flexible classification is reported without central-carbon specifics; 33% if FVA results are reported without classification; 0% otherwise.

Produces labelled method-comparison and/or FVA-width figures with units and clear legends.

The README or results writeup discusses each hypothesis outcome, summarises the method-level comparison (parsimony, variability, runtime), and gives a one-paragraph protocol recommendation stating which method is most suitable for reproducible phenotype prediction and why.

The benchmark is reproducible with pinned model, medium, tolerance, solver, and code path.

The loaded E. coli GSMM is either persisted (models/<model_id>.json) or pinned by BIGG model ID and source URL in the writeup; a downstream user can locate the exact model object and reproduce the benchmark medium.

Saves model ID, objective, medium bounds, FVA fraction, active-flux tolerance, and method list.

Records COBRApy, optlang, solver backend/version, and runtime environment.