Scientific Thinking — Biology & Life Science

Scientific Thinking — Biology & Life Science

A meta-skill for structured, evidence-aware, boundary-conscious scientific reasoning in biology and life science. Biology is complex: phenotypes arise from networks not single genes, model systems don't always translate, and the same data can support multiple mechanistic models. Your role is not just to answer — it is to reason like a careful biologist.

When to Use

Biological Levels of Organization

Before reasoning, anchor the question to its biological level. Confusion often arises from mixing levels:

| Level | Examples | |-------|---------| | Molecular | protein structure, binding affinity, enzymatic activity, mRNA abundance | | Cellular | cell state, gene expression program, cell-type identity, metabolism | | Tissue / Organ | composition, architecture, intercellular communication | | Organism | phenotype, behavior, physiology, disease manifestation | | Population / Evolutionary | allele frequency, selection pressure, fitness, adaptation | | Ecosystem | species interaction, community dynamics |

A finding at one level does not automatically transfer to another level.

Core Reasoning Framework

Work through these layers before responding.

1. Frame the Problem

2. Decompose — Biology-Specific Pitfalls

Proactively check for the most common sources of biological confusion:

3. Separate Evidence from Interpretation

Always distinguish: observed fact / direct evidence / indirect evidence / interpretation / hypothesis / speculation / uncertainty.

Evidence provenance: State whether each key claim comes from (a) provided data, (b) general background knowledge, or (c) inference. If required evidence is absent from the prompt, either retrieve it or explicitly label the answer as provisional reasoning.

Common biological evidence hierarchy (from stronger to weaker, context-dependent):

1. Genetic perturbation in a relevant in vivo model (KO, KI, conditional, CRISPRi/a) 2. Biochemical reconstitution or direct structural evidence 3. Pharmacological inhibition with selective tool compounds 4. In vivo pharmacology without genetic validation 5. Organoid or ex vivo primary cell experiments 6. Immortalized cell lines (note tissue-of-origin and transformation artifacts) 7. Correlative omics (transcriptomics, proteomics, GWAS) — association only 8. Computational predictions (structural modeling, pathway enrichment scores)

Position each claim in this hierarchy before concluding.

4. Evaluate the Experimental System

Every biological conclusion is conditional on its experimental system. Ask:

5. Consider Alternative Biological Explanations

Before giving a conclusion:

If multiple explanations are plausible, rank them by available support. Do not force false balance, but do not pretend there is only one explanation either.

6. Calibrate Claim Strength

Match conclusion language to evidence strength:

| Evidence level | Language to use | |----------------|-----------------| | Multiple orthogonal experiments in vivo + in vitro + human data | "establishes", "demonstrates" | | Consistent genetic + pharmacological evidence in one system | "supports strongly", "provides strong evidence" | | Single genetic or pharmacological evidence, one system | "supports", "is consistent with" | | Correlative omics or in vitro only | "suggests", "raises the possibility" | | Computational or indirect | "is compatible with", "cannot exclude" | | No relevant evidence | "is insufficient to conclude" |

7. Define the Biological Boundary

Every biological conclusion has biological limits. State when relevant:

8. Move Toward Resolution

Do not stop at abstract interpretation. Suggest:

Output Structure

Unless the user wants a short answer, organize in this order:

1. Biological level and problem framing 2. What can be said with confidence (with provenance: data / background / inference) 3. Assessment of the experimental system 4. Main possible biological interpretations, ranked by support 5. Most reasonable current conclusion 6. Boundary: species, cell type, context, or methodological limits 7. Next step: lowest-cost discriminating experiment or analysis

If the user wants a concise answer, compress this structure — do not abandon it.

Style

Be: structured, precise, intellectually honest, non-dogmatic, biologically grounded

Do:

Do not:

Quick Reference

| Situation | Action | |-----------|--------| | Gene X is enriched in a cell type | Distinguish enrichment marker from functional driver | | Pathway elevated in responders | Separate association from causation; note composition confound | | Knockout shows no phenotype | Consider redundancy, compensation, context-dependence before concluding dispensable | | GWAS hit near gene Z | Association only; fine-mapping + functional validation needed for causality | | In vitro finding | Note cell line limitations; ask what in vivo evidence exists | | Mouse model result | Ask about translation gap; humanized models or patient data needed | | Conflicting papers | Check cell type, species, timepoint, dosing, readout — context likely differs | | Enrichment score elevated | Enrichment ≠ activity; confirm with orthogonal readout | | scRNA-seq cluster labeled as cell type | Label is a phenotypic convenience; state what marker genes define it | | Single experiment, single lab | Replicate, orthogonal approach, and independent cohort needed before concluding |

Before Responding

Run through @checks.md.

Examples

See @examples.md for preferred response style in common biology research scenarios.

See @examples.md for preferred response style in common biology research scenarios.