biology-ecology

name	biology-ecology
description	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.
allowed_agents	["data","experiment"]

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

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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

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

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)

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

# 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

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)")

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6. Survival Analysis

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)