# Biology Ecology > Experimental and ecological biology — experimental design with controls/replicates, biology-specific statistical tests, diversity indices, cell biology assays (IC50, ELISA, flow cytometry), imaging analysis, and survival analysis. Use when working with biological experimental data. - Skill: `leonardodalinky/biology-ecology` (Agent Skill) - Install (CLI): `npx skillmds@latest add leonardodalinky/biology-ecology` - Raw SKILL.md: https://api.skillmd.com/api/skills/leonardodalinky/biology-ecology/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: leonardodalinky (https://skillmd.com/u/leonardodalinky) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/leonardodalinky/biology-ecology --- # Biology and Ecology ## Overview This skill covers experimental biology and ecology workflows: from designing valid experiments with appropriate controls and replicates, to analyzing assay data, microscopy images, ecological surveys, and survival data. For genomics and sequencing data, also see the `bioinformatics-analysis` skill. ## When to Use This Skill - Analyzing in vitro or in vivo experimental data (cell lines, animal studies) - Processing ecology survey data (species counts, diversity indices) - Fitting dose-response curves (IC50, EC50) - Analyzing flow cytometry, ELISA, or imaging data - Survival analysis (Kaplan-Meier, Cox regression) - Choosing the right statistical test for biological data --- ## 1. Experimental Design for Biology ### Controls | Control type | Purpose | Example | |---|---|---| | **Negative control** | Establish background / baseline | Vehicle (DMSO, PBS), untreated cells | | **Positive control** | Confirm assay works | Known active compound, reference drug | | **Vehicle control** | Separate drug effect from solvent effect | DMSO at matched concentration | | **Isotype control** (flow) | Estimate non-specific antibody binding | Matched isotype antibody | **Rule**: Always include both negative AND positive controls in every experiment run. ### Replicates - **Biological replicates**: independent samples (different cell passages, different animals, different patients) → what you use for statistics - **Technical replicates**: same sample measured multiple times → estimate measurement variability only, do NOT treat as independent samples **Minimum n**: Aim for n ≥ 3 biological replicates per condition. For animal studies, use power analysis (see `experiment-design` skill) targeting power ≥ 0.80. ### Randomization and Blinding ```python import random # Randomize treatment assignment wells = list(range(96)) random.seed(42) random.shuffle(wells) treatment_wells = wells[:24] # randomly assigned control_wells = wells[24:48] # Document blinding: encode sample IDs before measurement sample_map = {f"Sample_{i}": f"Unknown_{i:03d}" for i in range(1, 25)} # Decode only after all measurements are complete ``` --- ## 2. Biology-Specific Statistical Tests ```python import scipy.stats as stats import pingouin as pg import numpy as np import pandas as pd # Two groups, continuous, normal distribution → Welch's t-test (unequal variance assumed) control = np.array([2.1, 2.4, 2.2, 2.5, 2.3]) treatment = np.array([3.5, 3.8, 3.2, 3.9, 3.6]) t_stat, p_val = stats.ttest_ind(control, treatment, equal_var=False) print(f"Welch's t-test: t={t_stat:.2f}, p={p_val:.4f}") # Effect size (Cohen's d) result = pg.ttest(treatment, control) print(result[["T", "p-val", "cohen-d", "power"]]) # Non-parametric alternative: Mann-Whitney U u_stat, p_mw = stats.mannwhitneyu(control, treatment, alternative="two-sided") print(f"Mann-Whitney U: p={p_mw:.4f}") # Multiple groups → one-way ANOVA + Tukey HSD post-hoc groups = {"Control": [2.1, 2.4, 2.2], "Drug_A": [3.5, 3.8, 3.2], "Drug_B": [4.1, 4.3, 4.0]} df_long = pd.DataFrame([{"group": g, "value": v} for g, vals in groups.items() for v in vals]) aov = pg.anova(dv="value", between="group", data=df_long) posthoc = pg.pairwise_tukey(dv="value", between="group", data=df_long) print(aov) print(posthoc[["A", "B", "diff", "p-tukey"]]) # Non-parametric: Kruskal-Wallis + Dunn's test kw_stat, kw_p = stats.kruskal(*[v for v in groups.values()]) print(f"Kruskal-Wallis: H={kw_stat:.2f}, p={kw_p:.4f}") dunn = pg.pairwise_tests(dv="value", between="group", data=df_long, parametric=False) # Paired comparison: paired t-test before = np.array([5.2, 4.8, 5.5, 4.9]) after = np.array([4.1, 3.9, 4.3, 3.8]) t_paired, p_paired = stats.ttest_rel(before, after) print(f"Paired t-test: t={t_paired:.2f}, p={p_paired:.4f}") ``` **Test selection quick guide**: | Data type | 2 independent groups | 2 paired | ≥3 independent | ≥3 paired | |---|---|---|---|---| | Normal | Welch's t-test | Paired t-test | ANOVA + Tukey | Repeated ANOVA | | Non-normal / small n | Mann-Whitney U | Wilcoxon signed-rank | Kruskal-Wallis + Dunn | Friedman | --- ## 3. Dose-Response Analysis (IC50 / EC50) ```python import numpy as np from scipy.optimize import curve_fit import matplotlib.pyplot as plt def four_pl(x, bottom, top, ec50, hill): """Four-parameter logistic (4PL) dose-response curve.""" return bottom + (top - bottom) / (1 + (ec50 / x) ** hill) # Concentration in nM, response as % inhibition conc = np.array([0.001, 0.01, 0.1, 1, 10, 100, 1000]) # nM response = np.array([2, 5, 15, 50, 85, 95, 98]) # % inhibition # Fit the curve try: popt, pcov = curve_fit( four_pl, conc, response, p0=[0, 100, 1.0, 1.0], # initial guesses bounds=([0, 50, 0, 0.1], [20, 110, 1e6, 10]), # realistic bounds maxfev=10000, ) bottom, top, ic50, hill = popt perr = np.sqrt(np.diag(pcov)) print(f"IC50 = {ic50:.3f} nM (95% CI: {ic50-2*perr[2]:.3f} – {ic50+2*perr[2]:.3f})") print(f"Hill slope = {hill:.2f}") print(f"Bottom = {bottom:.1f}%, Top = {top:.1f}%") except RuntimeError: print("Curve fitting failed — check data range covers full sigmoidal curve") # Plot x_fit = np.logspace(np.log10(conc.min()), np.log10(conc.max()), 200) plt.semilogx(conc, response, "o", label="Data") plt.semilogx(x_fit, four_pl(x_fit, *popt), "-", label=f"4PL fit (IC50={ic50:.2f} nM)") plt.axvline(ic50, ls="--", color="gray", alpha=0.5) plt.xlabel("Concentration (nM)") plt.ylabel("% Inhibition") plt.legend() ``` --- ## 4. ELISA Quantification ```python # Standard curve fitting and interpolation std_conc = np.array([0, 0.5, 1, 2, 5, 10, 20]) # ng/mL std_od = np.array([0.05, 0.12, 0.21, 0.38, 0.82, 1.45, 2.1]) # OD450 # Fit 4PL to standard curve popt_std, _ = curve_fit(four_pl, std_conc[1:], std_od[1:], p0=[0, 2.5, 5, 1.5], maxfev=10000) # Interpolate unknown samples unknown_od = np.array([0.45, 0.78, 1.12]) def interpolate_from_curve(od_values, popt): """Inverse 4PL: solve for concentration given OD.""" from scipy.optimize import brentq concentrations = [] for od in od_values: try: c = brentq(lambda x: four_pl(x, *popt) - od, 0.01, 1000) concentrations.append(c) except ValueError: concentrations.append(np.nan) return np.array(concentrations) conc_unknown = interpolate_from_curve(unknown_od, popt_std) print(f"Concentrations: {conc_unknown} ng/mL") ``` --- ## 5. Ecology: Diversity Indices ```python import numpy as np from scipy.stats import entropy as scipy_entropy # Community composition (species counts) community_A = np.array([50, 30, 10, 5, 3, 2]) community_B = np.array([15, 14, 13, 12, 11, 10]) def shannon_index(counts): props = counts / counts.sum() return scipy_entropy(props, base=np.e) def simpson_index(counts): n = counts.sum() return 1 - np.sum(counts * (counts - 1)) / (n * (n - 1)) def chao1(counts): """Chao1 species richness estimator.""" f1 = (counts == 1).sum() # singletons f2 = (counts == 2).sum() # doubletons return len(counts) + (f1**2) / (2 * max(f2, 1)) print(f"Community A — Shannon H': {shannon_index(community_A):.3f}, Simpson D: {simpson_index(community_A):.3f}") print(f"Community B — Shannon H': {shannon_index(community_B):.3f}, Simpson D: {simpson_index(community_B):.3f}") # Beta diversity: Bray-Curtis dissimilarity def bray_curtis(a, b): return np.sum(np.abs(a - b)) / np.sum(a + b) bc = bray_curtis(community_A, community_B) print(f"Bray-Curtis dissimilarity: {bc:.3f} (0=identical, 1=completely different)") ``` --- ## 6. Survival Analysis ```python from lifelines import KaplanMeierFitter, CoxPHFitter from lifelines.statistics import logrank_test import pandas as pd # Kaplan-Meier survival curves df = pd.DataFrame({ "time": [5, 12, 20, 28, 35, 8, 15, 22, 30, 40], "event": [1, 1, 0, 1, 0, 1, 0, 1, 1, 0], # 1=event, 0=censored "group": ["ctrl"]*5 + ["treat"]*5, }) kmf = KaplanMeierFitter() fig, ax = plt.subplots(figsize=(7, 5)) for group in ["ctrl", "treat"]: mask = df["group"] == group kmf.fit(df[mask]["time"], df[mask]["event"], label=group) kmf.plot_survival_function(ax=ax, ci_show=True) # Log-rank test between groups ctrl = df[df.group == "ctrl"] treat = df[df.group == "treat"] lr = logrank_test(ctrl["time"], treat["time"], ctrl["event"], treat["event"]) print(f"Log-rank p-value: {lr.p_value:.4f}") # Cox proportional hazards df["treated"] = (df["group"] == "treat").astype(int) cph = CoxPHFitter() cph.fit(df[["time", "event", "treated"]], duration_col="time", event_col="event") cph.print_summary() # HR < 1 means treatment reduces hazard (protective) # HR > 1 means treatment increases hazard ``` **Assumptions to check**: - **Proportional hazards assumption**: use Schoenfeld residuals (`cph.check_assumptions(df)`) - **Censoring**: must be non-informative (censored patients don't systematically differ from non-censored)