2nd/10_Wiki/Topic_Programming/AI_and_ML/Bioenergetics.md

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id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-bioenergetics	Bioenergetics	10_Wiki/Topics	verified	self	생체 에너지학 ATP mitochondria metabolism thermodynamics neuromorphic computing	none	B	0.85	conceptual	biology biochemistry atp metabolism mitochondria thermodynamics neuromorphic energy-efficiency		2026-05-10	pending	language applicable_to biology / biochemistry Neuromorphic Computing Energy-Efficient AI Drug Discovery

Bioenergetics

📌 한 줄 통찰

"매 생명 = 매 entropy 의 저항". 매 thermodynamics 2nd law (entropy ↑) 의 against — 매 energy 의 collect + transform → 매 order. 매 ATP 의 currency. 매 modern AI 의 inspiration: 매 neuromorphic computing 의 energy efficiency.

📖 핵심

매 ATP (생명 의 currency)

• Adenosine Triphosphate.
• 매 phosphate bond 의 cleave → 매 energy.
• 매 cell 의 매 활동 의 fuel.
• 매 인간 의 매 day 의 매 자기 무게 만큼 의 ATP turnover.

매 thermodynamics

• 2nd law: 매 entropy 의 ↑ in 의 closed system.
• Living system: 매 open system — 매 외부 의 free energy.
• Gibbs free energy (ΔG): 매 work 의 가능 amount.
• Coupling: 매 ΔG > 0 reaction 의 매 ΔG < 0 의 hydrolysis 의 drive.

매 metabolism

Catabolism (이화)

• 매 분해 → 매 energy.
• 매 glucose → 매 36 ATP (full oxidation).

Anabolism (동화)

• 매 build → 매 order (protein, DNA).
• 매 ATP 의 consume.

매 핵심 pathway

1. Glycolysis: 매 cytosol, 매 glucose → pyruvate, 매 2 ATP.
2. TCA / Krebs cycle: 매 mitochondria matrix.
3. Electron transport chain (ETC): 매 inner membrane.
4. Oxidative phosphorylation: 매 32 ATP.
5. Fermentation (anaerobic): 매 lactate / ethanol.

매 mitochondria

• 매 powerhouse.
• 매 own DNA (maternal).
• 매 chemiosmotic gradient (Mitchell 1961).
• 매 endosymbiotic origin.

매 efficiency

• 매 muscle: 25% (rest 가 heat).
• 매 photosynthesis: 1-3% (광합성).
• 매 brain: 매 20W 의 100T synapse.
• 매 GPU (LLM): 매 100s of W 의 inference.

→ 매 brain 의 efficiency 의 100,000× 의 vs current AI.

매 modern AI 의 응용

Neuromorphic computing

• 매 spike-based.
• 매 event-driven (sparse).
• 매 in-memory compute.
• 매 chip: Intel Loihi, IBM TrueNorth, BrainChip Akida.

Energy-efficient ML

• 매 quantization (INT8, INT4).
• 매 sparse activation.
• 매 mixture of experts (only activated subset).
• 매 distillation.

Biological inspiration

• 매 spike-timing-dependent plasticity (STDP).
• 매 reservoir computing.
• 매 differentiable physical system.

매 medical 응용

• Mitochondrial disease: 매 inherited.
• Cancer: 매 Warburg effect (glycolysis 의 prefer).
• Aging: 매 mitochondrial dysfunction.
• Diabetes: 매 metabolic dysregulation.
• Drug: 매 target metabolic enzyme.

매 evolutionary

• 매 archaea + bacteria 의 endosymbiosis (mitochondria).
• 매 eukaryote 의 큰 size 의 enable.
• 매 multicellular 의 prerequisite.

💻 패턴 (응용 — neuromorphic / energy-efficient ML)

Spiking Neural Network (SNN)

import torch
import torch.nn as nn

class LIFNeuron(nn.Module):
    """Leaky Integrate-and-Fire — biological-style."""
    def __init__(self, threshold=1.0, decay=0.9):
        super().__init__()
        self.threshold = threshold
        self.decay = decay
        self.v = 0
    
    def forward(self, x):
        self.v = self.decay * self.v + x
        spike = (self.v >= self.threshold).float()
        self.v = self.v * (1 - spike)  # 매 reset on spike
        return spike

# 매 input 의 most 의 zero (sparse) → 매 energy ↓

Energy-aware training

def carbon_aware_train(model, dataset, max_kwh):
    energy_used = 0
    for batch in dataset:
        loss = compute_loss(model, batch)
        loss.backward()
        optimizer.step()
        
        energy_used += measure_gpu_power_wh()
        if energy_used > max_kwh * 1000:
            log(f'Energy budget exhausted: {energy_used} Wh')
            break

Mixture of Experts (sparse activation)

class MoELayer(nn.Module):
    def __init__(self, n_experts=8, top_k=2):
        super().__init__()
        self.experts = nn.ModuleList([Expert() for _ in range(n_experts)])
        self.gate = nn.Linear(d_in, n_experts)
        self.top_k = top_k
    
    def forward(self, x):
        scores = self.gate(x).softmax(dim=-1)
        top_scores, top_idx = scores.topk(self.top_k, dim=-1)
        
        # 매 only top-k 의 active → 매 sparse computation
        out = sum(top_scores[..., i].unsqueeze(-1) * self.experts[idx](x)
                  for i, idx in enumerate(top_idx.unbind(-1)))
        return out

Quantization (INT8 inference)

import torch.quantization as tq

model.eval()
qmodel = tq.quantize_dynamic(model, {nn.Linear}, dtype=torch.qint8)
# 매 75% 의 size ↓, 매 2-4× faster, 매 energy ↓

Energy estimation

# 매 GPU energy of 1 token (LLM)
GPU_TDP_W = 700  # H100
TOKENS_PER_SEC = 1000
ENERGY_PER_TOKEN_J = GPU_TDP_W / TOKENS_PER_SEC  # 0.7 J

# 매 brain comparison
BRAIN_W = 20
BRAIN_TOKEN_EQUIV_PER_SEC = 5  # 매 reading speed
ENERGY_PER_BRAIN_TOKEN_J = BRAIN_W / BRAIN_TOKEN_EQUIV_PER_SEC  # 4 J

# 매 ratio
print(f'GPU 의 brain 의 {ENERGY_PER_BRAIN_TOKEN_J / ENERGY_PER_TOKEN_J:.1f}× efficient per joule')
# 매 surprising X — 매 GPU 가 매 numerical 의 fast 가, 매 brain 의 task 의 different.

Mitochondria simulation (toy)

def atp_yield(glucose, oxygen_present=True):
    """매 simplified glycolysis + TCA + ETC."""
    if oxygen_present:
        glycolysis = 2  # 매 net ATP
        tca = 2 * 1     # 매 GTP
        etc = 2 * 17    # 매 NADH/FADH2 → ATP
        return glycolysis + tca + etc  # 매 ~36
    return 2  # 매 fermentation 만

🤔 결정 기준 (응용 측)

상황	Approach
Edge inference	Quantization + SNN
Large model	MoE + sparse
Battery-powered	Neuromorphic chip
Datacenter	Standard GPU + efficient algorithm
Drug discovery	Metabolic pathway model

기본값: 매 sparsity + 매 quantization + 매 hardware 의 right tool.

🔗 Graph

• 부모: Thermodynamics
• 변형: ATP · Mitochondria · Metabolism
• 응용: Neuromorphic-Computing · Mixture-of-Experts · LLM_Optimization_and_Deployment_Strategies
• Adjacent: Carbon-Footprint

🤖 LLM 활용

언제: 매 energy-efficient AI design. 매 neuromorphic chip exploration. 매 metabolic disease research. 매 carbon-aware ML. 언제 X: 매 specific medical claim (의사 consult). 매 nutrition advice.

❌ 안티패턴

• Bigger model only: 매 energy 의 ignore.
• Dense everything (no sparsity): 매 brain 의 inspiration X.
• Standard FP32: 매 quantization 의 leverage X.
• GPU 의 brain 의 mimic 의 expectation: 매 different paradigm.
• No carbon tracking: 매 sustainability ignore.

🧪 검증 / 중복

• Verified (Lehninger biochemistry, Mitchell chemiosmotic, Loihi / TrueNorth papers).
• 신뢰도 B.
• Related: Neuromorphic-Computing · Mixture-of-Experts · LLM_Optimization_and_Deployment_Strategies · Anarcho-Primitivism (energy 의 lens).

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — pathway + mitochondria + neuromorphic + 매 SNN / MoE / quantization code