Cell signaling is essentially a network, a system that moves violently within the microscopic level and exhibits certain properties at the macroscopic level. Pen ~ Fun ~ Pavilion www.biquge.info but we can't be satisfied with calling it a Tao-like whole, we need to decompose and understand it step by step according to the reductionist idea, and then integrate the data we collect from the perspective of the whole network.

Signal transduction is the adaptation of the body's network to the environment, and the occurrence of disease is related to the change of matching degree at all levels, that is, the result of a selective expression of the normal network. The focus is on the delivery of information. In terms of the understanding of the network, information is a high-dimensional structure that can be substituted into the differential equations between the levels of the original network.

The inevitable presence of distribution makes different cells different in their sensitivity to different signals.

The various signal transduction pathways we understand at this stage are the collapse and contraction of the network, and the selective expression of the network. The network changes according to the changes in the environment to maintain the overall balance. Therefore, the pathway is a specific observation, meaningful but not all-meaning, and we need to use it as a basis to continuously couple the various levels, constantly ascend the dimension, and empirically make this high-dimensional structure conform to the concrete expression of reality.

The specific pathway is the collapse of the network, which is an eigen, that is, our general observation. But their existence is not isolated, but interdependent. So how do we influence the signaling pathway so that it can behave what we expect: a sequence matching operation for a certain block.

The formation of pathways is the distribution and differentiation of networks, just like the social division of labor. Thus there is such a property: the structure is becoming more and more refined, but the dependence on the outside world is increasing.

Life is essentially resistant to changes in the environment, that is, to changes in resistance to the tendency of entropy to increase, Lenz's law.

According to the view of network science, all high-dimensional structures can be projected as low-dimensional structures, so we project the network as an infinite number of first-order sequence structures (which are dimensions that we can understand), and then understand the network as a whole based on the matching operation of the sequences. Like 1100110+0101110=1010001.

Distribution is a fundamental property of the network: sequences of the first order are not random combinations, but meaningful. Hence the emergence of patterns, that is, the formation of computing modules. The high-dimensional structures that can be formed by the operation between modules can also have the emergence of high-dimensional structures. The continuous traversal of the hierarchy makes it possible to form a certain network structure, which is a path that is continuously iterated according to the local optimal solution. (For example, the operation of probability is not a simple multiplication of 0.6*0.6*0.6.) Rather, it is added according to a certain distribution: 0.4*0.6*0.6*3+0.4*0.4*0.6*3+0.4^3) We can draw a certain distribution function based on this.

A network is a high-dimensional structure based on certain principles, that is, the interaction of units at different levels, and finally the coupling of various levels to form an overall structure. The nature of this time can be seen as the emergence of a system. A classic example of an organism is the binding of receptors and ligands. This is essentially a matching operation for a sequence.

We understand that pathways are relatively high-dimensional sequences and relatively low-dimensional networks. Therefore, it can be projected as a lower-dimensional sequence, so that a certain operation can be performed (quantized absolute logic 1/0).

The matching operation of sequences is one of the basic properties of networks. Networks can be decomposed into multi-level sequential operations: the promotion or suppression of signals, with both promotion of promotion (cascade reaction) and suppression of inhibition (feedback regulation...... This is the result of the multi-level coupling of the network.

The signals of the cell are a reflection of the life process and also control the life process to a certain extent, which is like a coupling structure of dynamic equilibrium. A certain direction can be selectively expressed according to the specific environment.

The relationship between probability and order of magnitude enables a pyramid-like distribution. Structured structures occupy a large part of the distribution of the main body (maintaining the overall existence of the network), and specific specialized structures occupy a small part (selective expression of the environment)

The selective expression of the coupling structure of the signaling pathway is the behavior of various cells: cell differentiation, migration, apoptosis, etc. Moreover, these levels can be traversed over and over again: the behavior of the cell is the matrix of the pathway (the scoring matrix formed by the sequence matching of the pathway) - the behavior of the tissue is the matrix formed by the behavior of the cell and the matrix of the hierarchy of the cellular pathway...... Each level is a collection of selective expressions of the others. (e.g. a=20%b+50%c+30%d, of course, it is the matrix operation of the network)

The coupling of pathways is essentially a combination, and due to the explosive growth of the combination, a certain property can emerge, that is, the formation of new pathways. Their interaction forms a network, the result of which is a multi-level sequence-matched structure, each of which is selectively expressed, and therefore can have a discrete nature. This is a complex network of signals.

Disease is a relatively normal state of other states, which is a selective expression of the network, which can be regarded as a different range of values in the scoring matrix

The nodes of the network can be thought of as a level of convergence, i.e., a dynamic equilibrium that exhibits a particular direction. As a result, the specific path of the joint bookstore of many nodes has been presented.

Multi-level coupling may seem to have a small probability of formation, but at least it is the result of filtering infinite possibilities, so that anything that is possible can exist. Such as a variety of complex signaling pathways.

For the network, the formation of modules is a fundamental emergent property, which is the basis for computation and the basis for being able to exhibit discreteness on the basis of the continuity of sequence matching.

G protein-coupled receptors are an example of multi-level coupling: multi-protein coupling, which can cause domain changes by sequence binding to ligands, which is a switching mechanism.

Each pathway is sensitive, but the activation of multiple pathways forms a whole with a certain robustness, like a normal distribution function. At the same time, there are many cells, and there are also many tissues, and the properties that organs can express are multi-layered. It's the power of numbers.

Biology is a multi-dimensional coupled high-dimensional structure, and its every move is the result of infinite operations, which are based on hierarchical operations to make information transmission or operations have a certain convergence, and then the overall matrix operations to obtain certain intrinsics. This is a filtering algorithm that makes reasonable guesses based on various patterns

The activation of the basic interactions of the network makes the effects of various properties a necessity, and the mechanisms of regulation are coupled with the structure of the network such as positive and negative feedbacks. This is based on multi-level coupling, such as antibodies and anti-antibodies of the immune system, suppression of sequences and inhibition of inhibition, and so on.

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