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Synthetic Biology, Regeneration & Longevity

  • Exit capability

    Engineer and interrogate living systems using mechanistic models, systems biology, safe design, causal experiments, tissue constraints, and aging frameworks.

  • Frontier targets

    Gene circuits; artificial organs; regenerative medicine; longevity; cultured food; engineered ecosystems.

  • Choose this track if

    Best if you want biology to become a programmable engineering substrate.

Mechanisms

Week 1: Cellular engineering substrate

Reading/source: OpenStax Biology 2e: membranes, metabolism, signaling, gene expression; MIT Systems Biology intro

Know: See cells as coupled transport, reaction, information and control systems rather than a vocabulary list.

Reconstruct: Regenerate membrane transport, central dogma, ATP/redox and feedback motifs from blank page.

Do: Build a coarse cell-state model coupling nutrient, ATP, stress and gene-expression response.

Context: MIT Systems Biology explicitly connects genetic circuits, cellular decisions and evolutionary dynamics.

Defend: Which state variables are mechanistic and which are convenient summaries?

Gate: Pass if mass/energy/information flows are not mixed conceptually.

Source: source

Week 2: Gene regulation and network motifs

Reading/source: MIT Systems Biology: input functions, autoregulation, feedforward motifs

Know: Understand promoter/input functions, Hill responses, positive/negative feedback and motifs.

Reconstruct: Derive Hill-function limits and fixed points for autoregulation.

Do: Simulate inducible gene expression and feedforward filtering under noisy input.

Context: Network motifs are reusable dynamic structures, not merely biological names.

Defend: What molecular assumptions are hidden inside a Hill function?

Gate: Pass if predicted response changes correctly when cooperativity/leak changes.

Source: source

Week 3: Bistability, oscillation and cell decisions

Reading/source: MIT Systems Biology: toggle switches, oscillators, cellular decisions

Know: Analyze multi-stability, hysteresis, oscillators and state transitions.

Reconstruct: Derive nullcline/fixed-point conditions for a toggle-like circuit.

Do: Simulate a toggle/oscillator and map parameter regions for robust behavior.

Context: Synthetic biology often engineers dynamics, not static expression.

Defend: Why can a circuit work at one parameter point and fail evolutionarily/biologically?

Gate: Pass if robustness region is quantified, not a cherry-picked trace.

Source: source

Week 4: Stochastic gene expression

Reading/source: MIT Systems Biology stochastic expression/master equation/Gillespie

Know: Know when molecule counts/noise invalidate deterministic ODE averages.

Reconstruct: Reconstruct chemical master equation concept and Gillespie event-selection logic.

Do: Compare deterministic and stochastic models at high/low copy number.

Context: Noise can be signal, failure, or selectable phenotype.

Defend: When does averaging erase the behavior you are engineering?

Gate: Pass if model choice is justified by scale/copy number.

Source: source

Design

Week 5: Engineering biology: abstraction and specifications

Reading/source: MIT 20.020 Biological Engineering Design

Know: Translate desired biological function into requirements, modules, interfaces, measurements and failure modes.

Reconstruct: Write design-build-test-learn loop and a functional specification independent of DNA sequence.

Do: Design a purely computational/non-clinical synthetic system with sensor -> logic -> output and explicit controls.

Context: MIT's project-based course emphasizes synthesis, standards, abstraction, safety/security/ethics.

Defend: What does it mean for a biological part to have a stable interface?

Gate: Pass if specification includes environment/context assumptions.

Source: source

Week 6: Measurement and controls in biology

Reading/source: Wave-1 metrology/causal + systems biology

Know: Design positive, negative, process and calibration controls; distinguish function from proxy.

Reconstruct: Regenerate measurement model: true state -> assay -> noise/bias -> inference.

Do: Create synthetic assay data with batch effects and estimate signal under blinded control layout.

Context: Biological conclusions often fail through measurement/context, not equations.

Defend: Which control distinguishes your mechanism from the strongest alternative?

Gate: Pass if every major conclusion maps to a discriminating measurement.

Source: source

Week 7: Sequence/function inference and uncertainty

Reading/source: Biology/genetics core + public sequence/function examples

Know: Treat sequence-to-function prediction as uncertain causal/mechanistic inference.

Reconstruct: Reconstruct coding/regulatory sequence roles and genotype->phenotype uncertainty chain.

Do: Analyze a public benign sequence dataset or synthetic data; compare motif-based vs learned predictor and OOD split.

Context: Prediction can guide design but does not establish biological function.

Defend: What does your predictor fail to know about cellular context?

Gate: Pass if claims are phrased as predictions until experimental evidence exists.

Source: source

Week 8: Safe synthetic biology project architecture

Reading/source: MIT 20.020 human practice + Wave-2 safety

Know: Integrate containment, reversibility, monitoring, ownership and affected-stakeholder questions into design.

Reconstruct: Construct hazard/control structure and evolutionary escape tree for a hypothetical benign engineered organism.

Do: Redesign Week-5 project to include non-proliferating/simulation-only containment assumptions and monitoring.

Context: Human practice belongs inside the design loop rather than at the end.

Defend: What success condition creates a new risk or governance problem?

Gate: Pass if safety constraints cannot be optimized away by the functional objective.

Source: source

Evolution

Week 9: Population genetics and selection

Reading/source: Biology 2e + MIT systems evolution sessions

Know: Model mutation, selection, drift, recombination and finite-population uncertainty.

Reconstruct: Derive Hardy-Weinberg and simple mutation-selection recurrence.

Do: Simulate engineered trait retention under fitness cost and population bottlenecks.

Context: Every replicating biological design enters an evolutionary optimization loop.

Defend: Who is optimizing what: engineer, cell, population, environment?

Gate: Pass if design includes expected evolutionary response.

Source: source

Reading/source: Systems/evolution concepts; computational design only

Know: Understand iterative variation-selection as a search algorithm with assay-defined objective.

Reconstruct: Formalize genotype -> phenotype -> assay -> selection loop and selection-bias risks.

Do: Run in silico directed-evolution optimization on toy fitness landscape; compare local/global search.

Context: Directed evolution can optimize what the assay rewards, including unwanted proxies.

Defend: How can an assay be Goodharted by biology?

Gate: Pass if you produce an adversarial genotype/phenotype that fools the assay.

Source: source

Week 11: Eco-evolutionary interactions

Reading/source: MIT Systems Biology population interactions/games/ecology

Know: Model cooperation, competition, predator-prey/resource feedback and spatial effects.

Reconstruct: Derive two-strategy replicator or Lotka-Volterra fixed points.

Do: Build artificial ecosystem simulation and perturb species/resources.

Context: Engineered organisms enter existing ecological games.

Defend: Why can a locally beneficial trait destabilize ecosystem function?

Gate: Pass if invasion and recovery scenarios are tested.

Source: source

Replication

Week 12: Systems-biology reproduction

Reading/source: MIT problem sets/lectures or an open gene-network paper

Know: Reproduce a canonical switch/oscillator/noise/evolution result.

Reconstruct: Reconstruct equations and expected qualitative phase behavior before code.

Do: Replicate one result with parameter sweep and uncertainty/sensitivity analysis.

Context: Reproduction before novelty.

Defend: Which qualitative conclusion is robust to parameter uncertainty?

Gate: Replication Gate: clean notebook + equation/source provenance + negative case.

Source: source

Tissue

Week 13: Transport and tissue-scale constraints

Reading/source: Cell biology + Wave-2 diffusion/fluids/materials

Know: Understand oxygen/nutrient transport, extracellular matrix, mechanics and vascularization constraints.

Reconstruct: Derive diffusion-consumption length scale and surface-area/volume scaling.

Do: Model viable thickness of engineered tissue under consumption and perfusion scenarios.

Context: Artificial organs fail if tissue-scale transport is ignored.

Defend: What parameter change buys the most viable thickness?

Gate: Pass if transport bound constrains design geometry.

Source: source

Week 14: Stem cells, differentiation and regeneration

Reading/source: Biology/developmental mechanisms + literature review

Know: Reason about cell state, niche, differentiation, self-renewal and regeneration as control/trajectory problems.

Reconstruct: Draw state-transition graph with self-renewal/differentiation and failure modes.

Do: Build stochastic cell-population model under differentiation cues and proliferation limits.

Context: Regeneration requires controlled state transitions, not simply more growth.

Defend: How do you distinguish regeneration from dysplasia/overgrowth?

Gate: Pass if safety/quality endpoints include unwanted cell states.

Source: source

Week 15: Artificial organs as controlled systems

Reading/source: Physiology core + transport/materials/control

Know: Backchain organ function into transport, sensing, actuation, biochemical and regulatory functions.

Reconstruct: Write multi-scale requirements for kidney/liver/pancreas/heart-like function without prescribing clinical intervention.

Do: Choose one organ and build computational block diagram + mass/energy/control model.

Context: Replacement is harder than matching average throughput because organs regulate dynamically.

Defend: Which homeostatic function is easiest to overlook?

Gate: Pass if design includes dynamic disturbances and compensatory physiology.

Source: source

Aging

Week 16: Hallmarks of aging framework

Reading/source: López-Otín et al. 2023 Hallmarks of Aging: An Expanding Universe

Know: Understand the 12-hallmark framework and its criteria: age association, aggravation, amelioration.

Reconstruct: Reconstruct hallmarks and causal-evidence criteria without notes.

Do: Build causal graph linking hallmarks, biomarkers, interventions and clinical outcomes; mark evidence types.

Context: The 2023 update expands the framework to twelve interconnected hallmarks.

Defend: Is a hallmark a cause, mechanism class, biomarker, or organizing framework?

Gate: Pass if you refuse causal claims that only have association evidence.

Source: source

Week 17: Aging frameworks under criticism

Reading/source: 2024 open review on hallmarks as conceptual framework + comparative/user-guide literature

Know: Treat hallmarks as a useful but contestable ontology, not a settled causal decomposition.

Reconstruct: List competing organizations and boundary failures of hallmark taxonomy.

Do: Take 20 aging papers/claims and classify evidence by intervention, species, tissue, outcome and replication.

Context: Recent reviews explicitly discuss strengths, influence and emerging hallmarks.

Defend: What observation would make two hallmarks collapse into one mechanism or split further?

Gate: Pass if ontology uncertainty changes research priority.

Source: source

Week 18: Biomarkers, surrogate endpoints and causal traps

Reading/source: Wave-1 causal inference + aging literature

Know: Distinguish biological-age predictors, mechanisms, surrogate endpoints and clinical benefit.

Reconstruct: Regenerate surrogate-paradox/mediation caution and target-trial design.

Do: Simulate an intervention that improves a biomarker while harming latent health outcome.

Context: Longevity research is especially vulnerable to long horizons and proxy optimization.

Defend: What evidence validates a biomarker as a decision surrogate?

Gate: Pass if benefit claims name endpoint and horizon rather than 'age reversal'.

Source: source

Research

Week 19: Failure/uncertainty map for a biological target

Reading/source: All prior work

Know: Rank mechanism uncertainty, measurement, delivery, heterogeneity, evolution, manufacturing and safety.

Reconstruct: Construct causal dependency graph and value-of-information estimate.

Do: Choose gene circuit/tissue/aging target; rank next experiments computationally.

Context: Good biotech research selects experiments that disambiguate mechanisms.

Defend: What uncertainty, if resolved, changes the program decision most?

Gate: Pass if next experiment is chosen by information value, not convenience.

Source: source

Week 20: Reproduce an open computational biology result

Reading/source: Choose systems-biology/aging public-data paper

Know: Practice reproduction using public data/simulation rather than unsafe wet-lab instruction.

Reconstruct: Reconstruct estimand/model/data preprocessing and validation split.

Do: Reproduce one central figure/claim; run sensitivity to batch/covariates/model choice.

Context: Computational reproduction is a strong safe research apprenticeship.

Defend: Which conclusion depends on preprocessing or cohort composition?

Gate: Extension Gate: reproduction + one plausible countermodel.

Source: source

Week 21: Mechanism-driven extension

Reading/source: Selected target

Know: Propose minimal extension that discriminates causal mechanisms.

Reconstruct: Write predicted outcomes under competing mechanisms.

Do: Run simulation/public-data analysis that produces different predictions between models.

Context: Useful novelty reduces mechanistic uncertainty.

Defend: What result would make you abandon your mechanism?

Gate: Pass if competing model could win.

Source: source

Translation

Week 22: Manufacturing, delivery and regulatory reality

Reading/source: Wave-2 manufacturing/safety + biologic/tissue product context

Know: Translate laboratory idea into quality, delivery, reproducibility, storage, monitoring and safety constraints.

Reconstruct: Build critical-quality-attribute/process-parameter map.

Do: Construct manufacturing/QC/deployment concept for benign research product/tissue model; no clinical protocol.

Context: Biological therapies are processes as much as molecules.

Defend: Which variance source grows with scale?

Gate: Pass if scale-up changes design requirements.

Source: source

Capstone

Week 23: Integrated biological engineering dossier

Reading/source: All track sources

Know: Link mechanism -> model -> measurement -> evolutionary/tissue context -> manufacturing -> safety.

Reconstruct: Regenerate complete causal/requirements graph.

Do: Capstone: computational synthetic biology, tissue/organ, or longevity research program with reproducible evidence and experimental design.

Context: No outcome claims beyond evidence tier.

Defend: Where is the biggest translation gap from model organism/cell to human/system?

Gate: Systems Gate: biology + causal/statistics + safety/translation reviewers.

Source: source

Week 24: Research defense and human-practice review

Reading/source: MIT 20.020 human practice + all work

Know: Defend value, uncertainty, affected parties, ownership, reversibility and stopping rules.

Reconstruct: Write claim ledger: source, evidence type, assumptions, uncertainty, next falsification.

Do: Final paper/notebooks + risk/governance appendix + 12-month safe research roadmap.

Context: Responsible biological engineering asks consequences of success before deployment.

Defend: What future capability would make your current containment/governance inadequate?

Gate: Capstone Gate: technical reviewer + bioethics/human-practice reviewer + safety reviewer.

Source: source

Research gates

G1 Replication

Required performance: Reproduce an open systems-biology/aging/public-data result without unsafe experimental instruction.

Minimum artifacts: Data/code provenance; model; causal assumptions; batch/OOD sensitivity; negative result.

Pass criterion: Association is not upgraded to mechanism.

G2 Extension

Required performance: Discriminate between competing biological mechanisms with simulation/public-data analysis.

Minimum artifacts: Causal graph; predicted outcomes; controls; sensitivity; evolutionary alternative.

Pass criterion: Must allow competing mechanism to win.

G3 System Closure

Required performance: Close mechanism->measurement->evolution/tissue/physiology->manufacturing/translation->safety.

Minimum artifacts: Requirements; evidence tiers; QC; containment/governance; translation gaps.

Pass criterion: No clinical efficacy claim beyond evidence.

G4 Research Defense

Required performance: Defend value, uncertainty, human practice, reversibility and safe next experiments.

Minimum artifacts: Paper/notebooks; claim ledger; human-practice review; 12-month safe roadmap.

Pass criterion: Must state how success changes risks/governance.

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