---
id: wiki-2026-0508-algorithmic-biology
title: Algorithmic Biology
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [알고리즘 생물학, computational biology, bioinformatics, algorithmic life]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: conceptual
tags: [biology, bioinformatics, computational-biology, alphafold, sequence-alignment, cellular-automata, ml-bio]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python / R / C++
  framework: BioPython / AlphaFold / Rosetta / Biopython
---

# Algorithmic Biology

## 📌 한 줄 통찰
> **"생명 = 매 우주 의 가장 복잡한 algorithm"**. 매 DNA / RNA / protein 의 computable model. AlphaFold 의 protein folding 의 50 year problem 의 solve. 매 신약 개발 / disease research / synthetic biology 의 가속화.

## 📖 핵심

### 매 sub-domain
1. **Sequence alignment**: 매 DNA / RNA / protein 의 evolutionary relation.
2. **Protein folding**: 매 amino acid → 3D structure 의 예측.
3. **Genome assembly**: 매 short read 의 puzzle 의 reconstruct.
4. **Phylogenetics**: 매 species 의 evolutionary tree.
5. **Systems biology**: 매 gene regulatory network / metabolic pathway.
6. **Cellular automata**: 매 simple rule → 매 complex pattern (Conway's Life).
7. **Synthetic biology**: 매 genetic circuit 의 design.

### 매 ML 응용
- **AlphaFold (DeepMind)**: 매 protein structure 의 atomic-accuracy 예측 (CASP14 win).
- **ESMFold (Meta)**: 매 large protein language model.
- **RoseTTAFold (Baker)**: 매 multi-track architecture.
- **AlphaMissense**: 매 missense variant 의 pathogenic 예측.
- **Geneformer / scGPT**: 매 single-cell transcriptomics 의 foundation model.

### 매 algorithm 기초

#### Sequence alignment
- **Needleman-Wunsch** (global): 매 dynamic programming.
- **Smith-Waterman** (local): 매 local match.
- **BLAST** (heuristic): 매 fast database search.

#### Phylogenetics
- **UPGMA / Neighbor-joining**: 매 distance-based.
- **Maximum likelihood / Bayesian**: 매 model-based.

#### Folding
- **Energy minimization**: 매 force field (Amber, Charmm).
- **Molecular dynamics**: 매 atomic simulation.
- **Deep learning**: 매 sequence → structure (AlphaFold).

### 매 data 의 challenge
- 매 noise (sequencing error, batch effect).
- 매 high dimensionality (10K+ gene).
- 매 small sample (rare disease).
- 매 ground truth 없음 (in vivo 어려움).
- 매 ethics (germline editing).

→ 매 PINN (Physics-informed Neural Networks) 의 prior 의 inject.

## 💻 패턴

### Sequence alignment (BioPython)
```python
from Bio import pairwise2
from Bio.pairwise2 import format_alignment

alignments = pairwise2.align.globalxx('ACGTACGT', 'ACGTGCGT')
print(format_alignment(*alignments[0]))
# ACGTACGT
# |||| |||
# ACGT-GCGT
```

### AlphaFold inference
```python
# 매 ColabFold (open-source AlphaFold2)
from colabfold.batch import run

run(
    queries=[('my_protein', 'MKTAYIAKQRQISFVKSHFSRQ...', None)],
    result_dir='./results',
    use_templates=False,
    num_recycles=3,
)
# Output: PDB file + confidence (pLDDT).
```

### Genome assembly (de Bruijn graph)
```python
def build_de_bruijn(reads, k):
    graph = {}
    for read in reads:
        for i in range(len(read) - k + 1):
            kmer = read[i:i+k]
            prefix, suffix = kmer[:-1], kmer[1:]
            graph.setdefault(prefix, []).append(suffix)
    return graph
```

### Cellular automata (Conway's Life)
```python
import numpy as np
def step(grid):
    neighbors = sum(np.roll(grid, (i, j), (0, 1))
                    for i in (-1, 0, 1) for j in (-1, 0, 1)
                    if (i, j) != (0, 0))
    return ((grid & (neighbors == 2)) | (neighbors == 3)).astype(int)
```

### Single-cell analysis (scanpy)
```python
import scanpy as sc

adata = sc.read_h5ad('data.h5ad')
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)
sc.pl.umap(adata, color='leiden')
```

## 🤔 결정 기준
| 문제 | Tool |
|---|---|
| Protein structure | AlphaFold / RoseTTAFold |
| Sequence search | BLAST |
| Genome assembly | SPAdes / Canu |
| RNA-seq | DESeq2 / edgeR |
| Single-cell | scanpy / Seurat |
| Phylogenetics | RAxML / BEAST |
| Synthetic biology | SBOL / Cello |

**기본값**: BioPython + scanpy + AlphaFold (Colab) 의 entry stack.

## 🔗 Graph
- 부모: [[Bioinformatics]] · [[Computational-Biology]] · [[Systems-Biology]]
- 변형: [[AlphaFold]]
- Adjacent: [[Computational-Neuroscience-RL|Computational-Neuroscience]] · [[Cellular-Automata]] · [[Physics-Informed-Neural-Networks]]

## 🤖 LLM 활용
**언제**: 매 biological data 의 ML 적용. 매 protein / sequence / genome analysis. 매 drug discovery pipeline.
**언제 X**: 매 clinical diagnosis (FDA-approved tool only). 매 wet lab experiment 의 substitute.

## ❌ 안티패턴
- **Data leakage**: 매 train / test 의 sequence similarity → 매 fake performance.
- **No biological prior**: 매 ML 의 black box 의 wet lab 의 reject.
- **Single dataset overfitting**: 매 cross-population 의 generalize X.
- **Ignoring batch effect**: 매 batch 가 confound.
- **No reproducibility**: 매 seed / version 의 lock.

## 🧪 검증 / 중복
- Verified (concept-level).
- 신뢰도 B (rapidly evolving field).
- Related: [[Bioinformatics]] · [[AlphaFold]] · [[Synthetic-Biology]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — sub-domain + ML 응용 + algorithm + code |
