Digital Metabolism: Computing and Biology

• Digital metabolism is a framework that encodes metabolic processes through discrete digital models, enabling simulation and regulation in both biological and artificial systems.
• It leverages diverse methodologies such as spiking neural networks, metabolic P systems, and genome-scale reconstructions to dynamically manage energy flow and substrate conversion.
• The approach supports applications in neuromorphic hardware, personalized digital twins, and synthetic biocircuit design through integrated computational and mathematical techniques.

Digital metabolism refers to the explicit encoding, simulation, and regulation of metabolic processes within computational, biological, or neuromorphic architectures by leveraging discrete, digitally parameterized models. In diverse contexts ranging from spiking neural networks to genome-scale cellular reconstructions and synthetic biology, digital metabolism enables the dynamic management, optimization, and homeostasis of energy flow, substrate conversion, and information storage. This paradigm subsumes methodologies spanning energy-aware neural hardware, programmable metabolic P systems, multi-scale digital twins for human physiology, molecular communication-based digestion modeling, and the two-tiered coordination between genetic and analog metabolic layers.

1. Formal Principles and Definitions

Digital metabolism is operationalized by embedding proxies for metabolic states, typically realized via thermodynamic parameters or discrete regulatory codes, directly into the mathematical structure of a networked system. In spiking neural models, metabolic state is mapped to temperature $T$ (Kelvin), with Q$_{10}$ scaling of conductances and plasticity amplitudes. For chemical networks and biological organisms, digital metabolic regulation is achieved through discrete genetic apparatuses managing substrate flow, reaction rates, and molecular memory.

• In neuronal models: metabolic state $T$ governs $\tau_m$, $g_l$, and plasticity amplitudes $A_\pm$ via exponential Q$_{10}$ factors (Öner et al., 25 Dec 2025).
• In metabolic P systems: a set of metabolites $M$, rewriting rules $R$, initial concentrations $I$, and flux functions $\Phi$ are assembled into a grammar $G=(M,R,I,\Phi)$, supporting Turing-complete computation (Guiraldelli et al., 2015).
• For human metabolic reconstruction: stoichiometric matrix $N \in \mathbb{R}^{m \times n}$ encodes mass conservation, with reaction fluxes $v$ constrained via $N v = 0$ (Noronha et al., 2016).
• In managed-metabolism theory: genetic sequences $g \in \mathcal{G}$ encode constraint maps $R: \mathcal{G} \rightarrow \mathcal{C}$, modulating analog network dynamics by digital information (Stewart, 2017).

2. Methodological Realizations

Digital metabolism spans various mathematical and computational frameworks:

• Conductance-based SNNs: Each neuron follows

$$C_m \frac{dV}{dt} = g_{l,\mathrm{sim}}(T)(E_l - V) + g_e(t)(E_e - V) + g_i(t)(E_i - V),$$

where $$g_{l,\mathrm{sim}}(T) = g_{l,\mathrm{ref}} Q_{10,m}^{(T-T_\mathrm{ref})/10}$$ scales leak conductance with temperature, affecting $\tau_m$ and firing rates (Öner et al., 25 Dec 2025).

• MPPC Metabolic Computing: Algorithms are mapped to metabolic circuits via metabolites, rewriting rules, stoichiometry matrices, and flux regulators. The dynamics are discrete, updating concentrations $X[t+1] = X[t] + \mathbf{A} U[t]$ (Guiraldelli et al., 2015).
• Genome-scale Reconstructions: ReconMap encodes the full Recon 2 stoichiometry, enabling constraint-based optimization (FBA) and interactive visualization of flux and omics overlays (Noronha et al., 2016).
• Regenerative Logic-Core Protocol (RLCP): Transformer models undergo adversarial deep-layer gradient reversal, decoupling logic from factual memory. Loss functions penalize retrieval of fact-entity associations, enforcing a thermodynamic phase transition to a crystallized logic core (Peng et al., 15 Jan 2026).
• Whole-body Physiology: ODE systems for 7 organs and 31 enzymatic steps utilize Michaelis–Menten kinetics modulated by insulin/glucagon ratios, simulating days-long feed–fast cycles and storage pool dynamics (Carstensen et al., 2022).
• Starch Digestion Modeling: Advection–diffusion–reaction PDEs with Michaelis–Menten hydrolysis describe gut transit, with communication metrics for delay and path-loss, enabling individualized digital twins (Vimalajeewa et al., 2021).
• Hopfield-like Cellular Memory: Metabolic networks self-organize into attractor states

$$E(\mathbf{s}) = -\frac{1}{2} \sum_{i \neq j} W_{ij} s_i s_j + \sum_i \theta_i s_i,$$

with functional memory stored in covalent marks, complemented by genetic (epigenetic) memory (Fuente, 2015).

3. Emergent Network-Level Phenomena and Regulatory Dynamics

Digitally regulated metabolism yields several nontrivial emergent behaviors:

• Bifurcation and Stability: In SNNs, metabolic state controls attractor regimes: hypometabolic states plateau at low rates, normometabolic states optimize entropy and sparsity, hypermetabolic states induce seizure-like synchrony. STDP kernels deform, with amplitude and time constant scaling dictating coincidence detection precision and synaptic specificity (Öner et al., 25 Dec 2025).
• Phase Transitions in AI Models: RLCP training induces a “phase transition” where factual retention (layer probe accuracy) collapses to random chance (~7%), while reasoning regimens (chain-of-thought, CoT) spontaneously emerge, attributed to the thermodynamic expulsion of entangled facts and network “crystallization” (Peng et al., 15 Jan 2026).
• Pattern Completion and Memory Storage: Hopfield-like attractor dynamics enable metabolic networks to correct errors and recall functional patterns, with basins of attraction and critical capacity characterized by mean-field theory; stable memory is embedded structurally in covalent marks (Fuente, 2015).
• Constraint-Driven Cooperation: The managed-metabolism hypothesis formalizes how digitally-coded constraints support beneficial metabolic species and suppress free riders, overcoming the cooperation barrier and enabling the emergence of individuality and open-ended functionality (Stewart, 2017).

4. Applications and Computational Platforms

Digital metabolism directly informs the design of computational, biological, and engineering applications:

• Neuromorphic Hardware: Temperature or power draw regulates neuron and synapse kinetics, implementing Q$_{10}$-like lookup tables on-chip, enabling real-time self-stabilization, prevention of runaway plasticity, and energy conservation (Öner et al., 25 Dec 2025).
• Interactive Systems Biology: ReconMap provides web-based visualizations, constraint-based modeling (FBA), and community annotation for human metabolism, facilitating integration of multi-omics data and experimental workflows (Noronha et al., 2016).
• Programmable Biocircuits: Metabolic P systems support algorithmic-to-biochemical compilation, driving synthetic cells capable of implementing arbitrary computation; toolkits allow program translation, simulation, and verification (Guiraldelli et al., 2015).
• Digital Twins for Health: Whole-body simulators and molecular-communication models calibrate to individual parameters, supporting personalized prediction of glucose trajectories, nutritional optimization, and management of metabolic diseases (Carstensen et al., 2022, Vimalajeewa et al., 2021).
• AI Modularization: RLCP and related protocols enable separation of logical reasoning from factual memory in LLMs, suggesting future architectures with discrete “Neural CPUs” and “Symbolic RAMs” (Peng et al., 15 Jan 2026).

5. Implications for Theory, Synthetic Biology, and Systems Medicine

Digital metabolism bridges theoretical, biotechnological, and computational domains:

• Turing Universality: Positively-controlled MP systems are Turing-universal, establishing metabolic computing’s formal equivalence to classical algorithms and suggesting constructive automated design flows for synthetic biology (Guiraldelli et al., 2015).
• Epigenetics and Memory: Cellular metabolic memory is dynamically encoded via attractor networks and structurally stored in covalent modifications, with epigenetic processes serving as the conservative, inheritable substrate (Fuente, 2015).
• Two-Tiered Cellular Architecture: Life’s defining structure is the digital regulation of analog metabolism—genetic codes as the manager, metabolic dynamics as the substrate—enabling evolvable constraint-driven management, robust cooperation, and individuation (Stewart, 2017).
• Constraint-Based System Optimization: Digital platforms like ReconMap and whole-body ODE models provide the quantitative basis for systems pharmacology, in silico trials, and closed-loop metabolic control, advancing precision medicine and health optimization (Noronha et al., 2016, Carstensen et al., 2022).

6. Future Directions and Open Questions

Current research identifies several avenues and unresolved challenges in digital metabolism:

• Identification and mechanistic mapping of “metabolized” neural units and regulatory heads in AI models following RLCP training (Peng et al., 15 Jan 2026).
• Scaling digital metabolism frameworks to larger, more heterogeneous biological and artificial systems, ensuring robustness of emergent behaviors under variable initializations and network architectures (Öner et al., 25 Dec 2025, Carstensen et al., 2022).
• Hybrid loss functions and modular architectures coupling neural logic cores with symbolic memory for factual retrieval, as suggested by DeepSeek and RLCP approaches (Peng et al., 15 Jan 2026).
• Controlled ablations of constraint maps and epigenetic writers/erasers in managed-metabolism models to dissect the dynamical routes through which digital control emerges and is maintained in biological networks (Stewart, 2017).
• Expanded digital twin deployment, integrating individual-specific multi-scale parameterizations for predictive metabolic diagnostics and therapy (Vimalajeewa et al., 2021, Carstensen et al., 2022).

Digital metabolism therefore presents a unified framework for the digitally-driven management, simulation, and optimization of metabolic processes, spanning computational neuroscience, cellular systems biology, synthetic biology, and artificial intelligence.