Matrix Taxonomy

The Papers.md matrix pairs an AI/ML method (row) with a cellular-agriculture research area (column). This page is the canonical definition of every row and column: what it covers, what falls out of scope, and — for categories that are easy to confuse — how to tell it apart from its neighbours. It serves two audiences: readers orienting themselves in the field, and the automated classification audit, which decides a paper’s placement from the paper’s own methods text measured against these definitions.

A guiding rule for every category: a placement records what a paper actually does, demonstrated in its methods — not what it could be applied to, and not what its title gestures at.

Research areas (columns)

Media Optimization

Designing and optimizing the culture medium — basal formulations, serum-free replacements, growth factors, and supplement concentrations — to improve cell growth, cost, or product quality. In scope: media-composition search, formulation design, and the experimental-design loops that drive it. Out of scope: operating or scaling the bioreactor (→ Bioprocess & Scale-Up) and the cells’ own biology (→ Cellular Engineering).

Cellular Engineering

Understanding and engineering the cells themselves — gene expression and regulation, cell-state and perturbation modeling, differentiation, cell-line characterization, and single-cell analysis used to interpret or modify cells. In scope: scRNA-seq analysis, perturbation-response prediction, gene regulatory modeling, cell-type annotation, and genetic/sequence design. Out of scope: what feeds the cells (→ Media Optimization) and the reactor they grow in (→ Bioprocess & Scale-Up).

Bioprocess & Scale-Up

Running and scaling the bioprocess — bioreactor design, process monitoring and control, fermentation strategy, and the transport phenomena (mixing, mass transfer, fluid dynamics) that govern scale-up. In scope: reactor and impeller design, CFD/mixing simulation, fed-batch and feeding-strategy control, real-time process monitoring, precision-fermentation process design (microbial-host production of media proteins and growth factors, an in-scope cellular-agriculture route), and the chemical/biological-engineering know-how cultivated-meat manufacturing depends on at volume — including methods demonstrated on non-biological reactor physics that transfer directly to bioreactor scale-up. Out of scope: the medium recipe itself (→ Media Optimization) and intracellular biology (→ Cellular Engineering). (Renamed from “Bioprocess control” to make the reactor/scale-up engineering scope explicit.)

Scaffolding

Designing and predicting edible, food-grade scaffolds and structural biomaterials that give cultured tissue its three-dimensional form and texture. In scope: scaffold geometry and architecture, mechanical-property prediction, tissue moulds, and food-grade biomaterial selection. Out of scope: the soluble medium (→ Media Optimization), sensory outcomes of the finished product (→ Sensory Prediction), and non-edible biomedical scaffolds (e.g. orthopedic or bone tissue-engineering constructs) whose methodology may transfer but whose application is not cellular agriculture; a general method demonstrated only on such a scaffold belongs in AI Tooling / Methodology, not here.

Sensory Prediction

Predicting sensory, organoleptic, and quality attributes of the product — flavor, odor, taste, aroma, texture, color, and freshness/spoilage. In scope: odor/taste/flavor prediction from chemical structure or spectra, and meat-quality/freshness classification. Out of scope: purely nutritional composition with no sensory target.

AI Tooling / Methodology

General-purpose AI methods, agents, tools, and frameworks that are applicable to cellular agriculture but are not yet tied to a specific applied cell-ag result. This is the home for a method described in general form — a biomedical agent, an agent framework, a knowledge-graph protocol — that could be applied to a cell-ag problem. In scope: general agents/tools/frameworks and methodology papers. Out of scope: a tool applied to a concrete cell-ag problem, which belongs in that problem’s column (e.g. an agent used for media design → Media Optimization).

AI Evaluation & Benchmarking

Benchmarks, evaluation frameworks, and measurement methods for AI and agent performance on biological / cell-ag-adjacent tasks. In scope: benchmark datasets bundled with protocols and metrics, evaluation methodologies, and verification frameworks. Out of scope: a model that merely reports benchmark numbers (it belongs in its own method × area), a dataset with no evaluation protocol (→ the Datasets/ directory), and a leaderboard service (→ Databases.md).

AI/ML methods (rows)

Bayesian Optimization

Sequential, surrogate-model-based optimization: a probabilistic model (usually a Gaussian process) plus an acquisition function chooses the next experiment to run, sample-efficiently. In cell-ag, the workhorse for media and process optimization under a tight experimental budget. Distinct from Bayesian inference (posterior or flux estimation that quantifies uncertainty without acquisition-driven experiment selection) — that is not this row.

Deep Learning

Multi-layer neural networks not better described by a more specific row — deep MLPs, generic feed-forward and recurrent nets, and autoencoders/transformers used as plain predictors. A catch-all: a paper whose core architecture is a CNN, GNN, GAN/VAE, or a pretrained foundation model belongs in that row instead. Not shallow models (e.g. RBF networks, single-hidden-layer MLPs used only as a baseline) and not non-neural methods (chemometrics/PLS, network propagation) that are sometimes loosely called “deep learning”.

GNN

Graph neural networks — learned, message-passing models over graph-structured data (GCN, GAT, GIN, graph autoencoders). In cell-ag: cell–cell similarity graphs, molecular graphs, and gene-interaction networks. Distinct from classical graph algorithms, random-walk network propagation, and non-learned graph embeddings (e.g. metapath2vec, node2vec), which are not trained GNNs.

CNN

Convolutional neural networks for grid- or image-structured data. In cell-ag: microscopy and product imaging, hyperspectral/spectral data, and 3-D scaffold tomography. The placement requires genuine spatial convolution on image-like input — a dense regression surrogate merely labelled “CNN” is better placed under Deep Learning.

GAN / VAE

Deep generative models — generative adversarial networks and variational autoencoders — used for data synthesis, augmentation, or generative dimensionality reduction. In cell-ag: synthetic regulatory- sequence design and scRNA-seq embedding.

Genetic Algorithms

Population-based evolutionary search — genetic algorithms, evolutionary strategies, NSGA, differential evolution. In cell-ag: optimizing media, process parameters, and tissue/scaffold designs over large combinatorial spaces.

SVM

Support vector machines and support vector regression — kernel methods for classification and regression on modest, well-featurized datasets. Common in sensory and media/process modeling.

Ensemble Learning

Tree ensembles and model averaging — random forests, gradient boosting / XGBoost, AdaBoost, and stacked ensembles. The default strong baseline for tabular prediction across sensory, media, scaffold, and process problems.

K-Nearest Neighbors

Instance-based prediction from nearest-neighbor similarity, for small-data classification and regression. Usually appears alongside other classical models in comparative studies.

Linear & Regularized Models

Classical linear predictors and their regularized variants — ordinary and penalized linear/logistic regression (LASSO, ridge, elastic net) and linear additive scoring models (e.g. propensity-score scoring cards). Valued for interpretability and small-data stability: the fitted coefficients name which features drive the prediction, and L1 penalties perform explicit feature selection. In cell-ag: morphology- and sequence-feature regression for cell-quality and taste prediction where the feature set is modest and interpretability matters. Distinct from the SVM row (no kernel or margin), Ensemble Learning (no trees, bagging, or boosting), and Chemometrics (not restricted to spectral latent-variable projection).

Chemometrics

Multivariate statistics for extracting quantitative information from chemical measurements — overwhelmingly spectra. The workhorses are Partial Least Squares (PLS) regression, PLS-DA (discriminant analysis), PCA, and OPLS. In cell-ag this is the backbone of spectroscopy-based Process Analytical Technology — NIR/Raman monitoring of glucose, lactate, and biomass in a bioreactor, and hyperspectral quality/freshness prediction. Distinct from the kernel (SVM), tree-ensemble, and neural-net rows: it is latent-variable linear projection, not learning by kernels, trees, or backpropagation.

Active Learning

Iterative design-of-experiments in which the model selects which experiments to run next to label, typically wrapped around a surrogate model, closing a predict→experiment→retrain loop. In cell-ag: media and strain optimization campaigns. Adjacent to Bayesian Optimization (a specific acquisition- driven instance); this row is the broader iterative-selection framing.

Reinforcement Learning

Learning policies from reward signals — classical RL, policy-gradient methods, and LLM post-training (GRPO, RLHF). In cell-ag: reward-driven post-training of models and virtual-cell control.

Foundation Models: Next-Token Prediction

Large, pretrained, transferable models trained with an autoregressive next-token objective (GPT-style), applied to biology — e.g. generative transcriptome and cell-atlas models. The defining test is that the model is a pretrained foundation model, not a task-specific network.

Foundation Models: Masked Language Modeling

Pretrained, transferable foundation models trained with a masked (BERT-style) objective — the single-cell foundation models such as scBERT, Geneformer, and scFoundation. Defined by masked self-supervised pretraining at scale plus downstream transfer.

Foundation Models: LM + Biological Priors

Foundation models that combine a language-model backbone with explicit biological priors or structured knowledge (e.g. protein-language-model representations integrated with curated biology). The model must itself be such a foundation model — a task-specific network that merely consumes pretrained embeddings as input features does not belong here (it is Deep Learning / GNN).

Foundation Models: Cell-State & Perturbation Prediction

Pretrained, transferable foundation models whose target is predicting cell state and perturbation responses across contexts. The discriminator is pretraining-and-transfer: a single-task supervised predictor (for example a GNN trained end-to-end on one perturbation dataset) is not a foundation model, even though it predicts perturbations — it belongs in its architecture’s row.

Foundation Models (other modalities)

Pretrained foundation models for modalities beyond single-cell transcriptomics not covered by the other foundation-model rows — multimodal omics, spectra, and small-molecule/natural-product models.

Scientific Literature & Discovery Agents

LLM agents for literature search, synthesis, and (semi-)autonomous discovery — retrieval-augmented question answering over papers and idea→experiment→manuscript pipelines. Among the agent rows, this one is defined by its focus on knowledge synthesis and the discovery workflow.

General-Purpose Biomedical Agents

LLM agents built to be broadly applicable across biomedical tasks without task-specific tuning, and that say so. Distinguished from Domain-Specific Biomedical Agents by breadth of intended use.

Chemistry / Synthesis Agents

LLM agents specialized for chemistry — synthesis planning, molecular reasoning, and autonomous chemical experimentation.

Domain-Specific Biomedical Agents

LLM agents purpose-built for one specific biomedical task or domain — NGS/RNA-seq analysis, spatial biology, a single named assay or modality. Distinguished from General-Purpose Biomedical Agents by narrow, dedicated focus.

Robot Scientists & Lab Automation

Systems that couple AI to physical laboratory automation for autonomous experimentation, closing the loop from hypothesis to robotically executed experiment. The discriminator is physical/automated wet-lab execution, not an in-silico agent alone.

Benchmarks & Evaluation Frameworks

Papers whose primary contribution is a benchmark or evaluation framework — datasets paired with tasks, protocols, and metrics, or a method for scoring/verifying AI and agent performance. Not a model that is merely evaluated on a benchmark (→ its own method × area), a dataset shipped without an evaluation protocol (→ Datasets/), or a correspondence/commentary about an evaluation (→ Reviews & Perspectives).

Agent Infrastructure (Frameworks, KGs, Protocols)

The substrate that agents run on — agent frameworks, knowledge graphs, tool/communication protocols (e.g. MCP), and orchestration ecosystems. The discriminator is that the contribution is plumbing/infrastructure, not an agent that itself solves a downstream task.

Subject themes (topic tags)

A separate axis from the matrix: subject-matter themes that group any content type (papers, software, databases, datasets) for cross-content discovery. Unlike research areas — which are problems an AI method is applied to — themes are what the material is about, so they fit databases and datasets that never enter the matrix (per the Databases-vs-ResearchAreas distinction). Themes are a fixed backbone; finer tags live under them and are minted only when several items cluster. A theme may link to a matrix research area where the two align.

Media & Growth Factors

Culture-medium composition and optimization: serum-free/serum-reduced formulations, growth factors and cytokines, recombinant medium proteins, and design-of-experiments for medium screening. Links to the Media Optimization research area.

Cell Lines & Engineering

The cells themselves and their manipulation: cell-line establishment and characterization, genetic engineering (CRISPR, perturbation), differentiation and myogenesis, stemness/senescence, and single-cell atlases. Links to Cellular Engineering.

Bioprocess & Scale-Up

Growing cells at scale: bioreactor design and CFD, perfusion and fed-batch, soft sensors and process control, and scale-up engineering. Links to Bioprocess & Scale-Up.

Scaffolding & Biomaterials

The structural substrate for tissue: scaffolds, biomaterials and hydrogels, 3D bioprinting, and the texture/architecture of cultured tissue. Links to Scaffolding.

Sensory & Flavor

The eating quality of the product and the analytics behind it: flavor and taste prediction, aroma, and the mass-spectrometry / chemometrics / metabolomics infrastructure used to measure them. Links to Sensory Prediction.

Metabolism & Modeling

Metabolic knowledge and models: genome-scale metabolic models (GEMs), flux analysis, pathway and strain design, and metabolic-network resources. A cross-cutting subject with no single matrix column.

Food Safety

The safety assessment of the proteins a cultured product introduces: allergenicity and immunogenicity prediction, IgE-epitope mapping, and the allergen reference databases that screening pipelines compare against. A cross-cutting subject with no single matrix column; the allergenicity fine tag groups the in-silico screening tools, allergen databases, and their methods papers.

AI Methods & Tooling

General-purpose AI capability and infrastructure applied across cell-ag: agents and foundation models, benchmarks and evaluation, frameworks, ontologies, and lookup databases/tooling. Links to AI Tooling / Methodology.