Is a Whole Piece of the Puzzle Missing from Modern Medicine's Knowledge Paradigm?

Introduction

Why is it that when facing trauma, emergencies, and infections, modern medicine performs like a deity, yet when confronting aging, chronic diseases, and sub-health conditions, it often seems powerless? This is not merely a bottleneck in technological iteration, but a fundamental imbalance in the underlying knowledge paradigm. When life is over-reduced into isolated "parts," we lose our perception of "system state." Today, we need to re-invent medicine — to complete medicine's second paradigm.

I. Medical Knowledge Does Not Have Just One Paradigm

If we understand medicine as a body of knowledge about the human body, then it encompasses at least two basic paradigms.

The first is the objective description of external objects. It understands disease as something identifiable, nameable, and locatable: pathogens, lesions, structural damage, molecular abnormalities, receptors, targets, gene mutations. Its strengths lie in clarity, stability, verifiability, and standardizability, and it has thus become the most successful backbone of modern medicine.

The second is the response mapping of system states. It does not first ask "what is this thing," but first asks "what state is the system currently in," "how will the system respond when facing certain perturbations," and "what pattern relationships exist between different symptoms, functional changes, and overall imbalances." This paradigm does not prioritize naming objects; rather, it prioritizes compressing and expressing the system's modes of response to perturbations.

The former paradigm excels at slicing the world into objects; the latter excels at organizing the world into states. The former is closer to anatomy and nomenclature; the latter is closer to patterns and trajectories. When medicine is truly complete, both should coexist; but the historical evolution of modern medicine has continuously reinforced the former, while the latter has long lacked a formalized language and has thus often been misunderstood as vague, empirical, or even excluded from "hard knowledge" altogether.

II. Why the Object Description Paradigm Is Still Not Enough

The object description paradigm is not wrong; it is simply incomplete. In simple diseases, a single object often suffices to explain most phenomena: a specific bacterial infection, a bone fracture, a blocked blood vessel, a mutation-driven tumor subtype. In such cases, identifying the object is nearly equivalent to grasping the disease.

But in complex diseases, the situation is often different. A person with chronic fatigue, deteriorating sleep, mood swings, elevated inflammation, metabolic abnormalities, and immune imbalance may not necessarily be explained by a single-point object. Even when several abnormal indicators are found, it may still be impossible to answer the more critical questions: Why do these changes co-occur? Why do individuals with the same diagnosis have such different states? Why do some people respond clearly to the same intervention while others show virtually no effect?

This suggests that complex diseases are primarily not a "single-point error" problem, but rather more like a system state deviation problem.

As early as 2011, Barabási et al. proposed the concept of "network medicine," pointing out that complex diseases are essentially perturbations of network modules (Disease Modules) rather than abnormalities of single genes. More intriguingly, the "Causal Emergence" theory proposed by Hoel et al. demonstrated that in complex systems, macro-scale features (such as system-level response patterns and higher-order state labels) often possess stronger causal explanatory power than micro-scale isolated points.

What truly needs to be described is not just a target, but how a system undergoes联动 (coupling), compensation, amplification, imbalance, and reconfiguration under multiple perturbations. Once medicine faces this situation, it must abandon its fixation on micro-level objects and recover another capability: recognizing states.

III. What is "Response Mapping"

Response mapping is neither mystical language nor a reaction against modern medicine. Its true meaning is: compressing and expressing the complex states a system exhibits when facing perturbations, using a set of identifiable, comparable, and deducible patterns.

Here, "response" is not a single symptom, but a system-level mode of reaction; "mapping" is not arbitrary association, but the search for a computable physical space.

Vidal et al. pointed out that the multi-scale interactome is the most mature real-world network substrate for depicting such system response states. Research by Ruiz et al. further demonstrated that interventions (e.g., drugs) do not exert their effects solely through single-point hits, but rather propagate their influence through this multi-scale network.

Therefore, mapping phenomena across different levels — symptoms, signs, functional changes, molecular readouts — into this shared network space becomes the key to grasping the system's evolutionary trajectory.

Once response mapping is introduced, medicine's perspective shifts. It no longer asks only "which object is broken," but begins to ask: Toward which response mode does this system currently lean? What does this mode imply about which functions are being suppressed and which are being amplified? Is this state more likely to move toward repair, rigidity, or collapse in the future? Which category of intervention is closer to the rewriting path for this state?

In other words, response mapping does not replace objective description, but adds a layer of state language on top of object description that is closer to complex systems.

IV. Why the Era of Complex Diseases Demands the Recovery of Response Mapping

When disease primarily manifests as infection, obstruction, rupture, or necrosis, medicine should of course prioritize identifying objects; but when disease manifests more as fatigue, inflammation, fragility, fluctuation, dysregulation, compensation, sensitization, and declining recovery capacity, medicine can no longer be satisfied with finding a few abnormal values.

What is truly lacking in the era of complex diseases is an intermediate language capable of simultaneously connecting state identification, mechanistic understanding, and intervention selection.

Fortunately, modern network medicine has begun building this bridge: for example, do Valle et al. confirmed that the therapeutic effects of complex interventions with non-single targets (such as polyphenol natural products) can be precisely predicted through the "network proximity" of their targets to disease proteins in the network.

More critically, to characterize the system's long-term responses, we need an extremely stable "state memory layer." Research has shown that epigenetic modifications such as DNA methylation can stably preserve the imprint of environmental perturbations, serving as a memory medium for system states; clinical data have also confirmed that such features exhibit even higher signal-to-noise ratios than direct measurement of certain phenotypes when predicting complex exposure states such as smoking and obesity.

With networks (PPI, etc.) as the mapping space and methylation as the pointer for state characterization, response mapping is no longer metaphysics, but a high-throughput-measurable computational science. This is precisely the underlying logic of Capomics' proposal to "measure life's adaptive capabilities" — we can finally render implicit system responses explicit through algorithms (such as the SEMO Algorithm) and methylation clocks.

V. Why Traditional Chinese Medicine Deserves to Be Re-Understood

If we understand TCM simply as an ancient object-naming system, then of course it appears imprecise, because it is not adept at defining what modernity understands as pathogens, receptors, structures, and molecules. But if we shift perspectives and understand TCM as a long-accumulated response mapping language, many things become different.

Perhaps the most valuable part of TCM lies not in whether it has directly articulated modern molecular mechanisms, but in its preservation of a grammar for high-order compression of system states. Wind, cold, summer heat, dampness, dryness, and fire are not necessarily "objects" first and foremost, but more likely compressed labels for archetypal response states.

A landmark 2023 study in Science Advances directly validated this: the effectiveness of TCM's "herb-symptom" relationships can be precisely explained through the "network proximity" between herb targets and symptom-related protein network modules. This demonstrates that TCM's traditional principle-method-recipe-medicine framework is essentially an intervention mapping algorithm based on network topological distance.

Therefore, what is truly worth re-reading in TCM is not forcing every term into a hard translation of some protein or pathway, but rather seeing: it represents a knowledge capability long neglected by modern medicine — the capability for macro-level response mapping of complex system states. When this macro-level wisdom meets modern DNA methylation big data networks, it will radiate entirely new vitality.

Conclusion

The future of medicine should not only continue accelerating on the single line of "object description," but must also recover another long-weakened main thread: response mapping of system states. The former provides us with pathogens, structures, molecules, and targets; the latter provides us with patterns, states, trajectories, and intervention directions. A theory truly oriented toward complex diseases, aging, and individualized medicine should not choose between the two, but should learn to rejoin them.

The work of Response Mapping Theory begins precisely here: first acknowledging that medicine has two basic paradigms, then building a computable digital language for the second paradigm.

🖋 Author's Note: "Response Mapping" does not remain solely at the level of philosophical speculation. To move from theory to reality, we need a concrete handle. Over the past few years, our team (DeepOMe 深度甲基) has been dedicated to engineering this network medicine-based system response theory. Using DNA methylation as the epigenetic memory layer and PPI interaction networks as the substrate, we have developed the SEMO Algorithm (System-level Response Primitives). What we are doing is transforming the abstract concept of "life system deviation" into quantifiable, traceable, and intervenable digital clocks in every saliva test, truly realizing the vision of Re-invent Medicine.