refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-nutritional-biochemistry
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title: Nutritional Biochemistry
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [영양 생화학, Nutrient Biochemistry, Metabolic Nutrition]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [biochemistry, nutrition, metabolism, biology, health]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: domain-knowledge
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framework: biochemistry
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---
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# Nutritional Biochemistry
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## 매 한 줄
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> **"매 nutrient 는 metabolic substrate + cofactor + signal"**. Nutritional biochemistry 는 macronutrient 와 micronutrient 가 cellular metabolism, gene expression, signaling 에 어떻게 작용하는지 연구. 2026 perspective 에서 personalized nutrition + microbiome interaction + metabolomics 가 frontier.
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## 매 핵심
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### 매 macronutrient pathways
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- **Carbohydrate**: glycolysis → pyruvate → acetyl-CoA → TCA → ETC. 4 kcal/g.
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- **Lipid**: β-oxidation → acetyl-CoA → TCA. 9 kcal/g. Membrane lipids, eicosanoids.
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- **Protein**: amino acid → transamination → urea / gluconeogenesis. 4 kcal/g.
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### 매 micronutrient roles
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- **B-vitamins**: coenzymes (NAD, FAD, CoA, THF, PLP).
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- **Fat-soluble (ADEK)**: signaling (retinoic acid, calcitriol).
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- **Minerals**: cofactors (Mg-ATP, Zn-fingers, Fe-heme), electrolytes.
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### 매 응용
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1. Sports nutrition / supplement design.
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2. Disease management (diabetes, NAFLD).
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3. Personalized diet (genotype + microbiome).
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4. Public health policy.
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## 💻 패턴
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### Basal metabolic rate (Mifflin-St Jeor)
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```python
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def bmr(weight_kg, height_cm, age_y, sex="M"):
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base = 10*weight_kg + 6.25*height_cm - 5*age_y
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return base + 5 if sex == "M" else base - 161
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def tdee(bmr_val, activity="moderate"):
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factors = {"sedentary": 1.2, "light": 1.375,
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"moderate": 1.55, "active": 1.725}
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return bmr_val * factors[activity]
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```
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### Macro split optimization
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```python
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def macro_split(tdee, goal="maintain", body_kg=70):
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protein_g = body_kg * (1.6 if goal == "cut" else 1.2)
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p_cal = protein_g * 4
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fat_cal = tdee * 0.25
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carb_cal = tdee - p_cal - fat_cal
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return {"protein_g": protein_g,
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"fat_g": fat_cal / 9,
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"carb_g": carb_cal / 4}
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```
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### Glycemic load
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```python
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def glycemic_load(food: dict) -> float:
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return food["gi"] * food["carb_g"] / 100
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# Low <10, medium 11-19, high 20+
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```
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### TCA cycle ATP yield
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```python
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def atp_per_glucose():
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glycolysis = 2
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nadh_glyc = 2 * 2.5
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pyruvate_to_acetyl = 2 * 2.5
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tca_per_acetyl = (3*2.5) + 1.5 + 1
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tca_total = 2 * tca_per_acetyl
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return glycolysis + nadh_glyc + pyruvate_to_acetyl + tca_total
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# ≈ 30 ATP / glucose (modern stoichiometry)
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```
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### Nitrogen balance
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```python
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def n_balance(protein_intake_g, urea_n_excreted_g, fecal_skin_g=4):
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n_in = protein_intake_g / 6.25
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n_out = urea_n_excreted_g + fecal_skin_g
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return n_in - n_out
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```
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### Vitamin D activation cascade
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```python
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def vit_d_activation():
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return [
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"7-dehydrocholesterol",
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"cholecalciferol (D3, skin UVB)",
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"25-OH-D3 (liver, CYP2R1)",
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"1,25-(OH)2-D3 (kidney, CYP27B1, calcitriol)",
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"VDR-RXR transcription factor",
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]
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Weight loss | Caloric deficit + protein-priority |
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| Performance | Periodized carb + creatine |
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| T2D management | Low GL + Mediterranean |
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| Deficiency screen | Serum 25-OH-D, B12, ferritin, Mg |
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**기본값**: TDEE 기반 + protein floor + micronutrient screen.
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## 🔗 Graph
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## 🤖 LLM 활용
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**언제**: meal plan template, nutrient interaction summary, label decoding.
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**언제 X**: clinical diagnosis / Rx — RD / MD 필수.
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## ❌ 안티패턴
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- **Calorie-only thinking**: hormone / micronutrient 무시.
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- **Single-nutrient hype**: antioxidant / superfood 일반화 — context-free.
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- **Ignoring bioavailability**: total intake ≠ absorbed.
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- **Population stat → individual**: personal genetics / microbiome 무시.
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## 🧪 검증 / 중복
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- Verified (Lehninger 8e, Modern Nutrition in Health and Disease 11e, USDA DRI 2024).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — BMR/macro/TCA/Vit-D 패턴 |
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