## Abstract

Intracellular signal transduction is the most important research topic in cell biology, and for many years, model research by system biology based on network theory has long been in progress. This article reviews cell signaling from the viewpoint of information thermodynamics and describes a method for quantitatively describing signaling. In particular, a theoretical basis for evaluating the efficiency of intracellular signal transduction is presented in which information transmission in intracellular signal transduction is maximized by using entropy coding and the fluctuation theorem. An important conclusion is obtained: the average entropy production rate is constant through the signal cascade.

### Keywords

- information thermodynamics
- fluctuation theorem
- average entropy generation rate
- entropy coding

## 1. Introduction

The analysis of intracellular signal transduction is one of the most important research topics in cell molecular biology. Determining the mechanisms for communicating intracellular information in the steady state, responding to changes in the external environment, and converting the change to express genetic information are a significant problem. The presented quantitative analysis may enable a comparison of signal transduction and evaluation of efficiency and should help realize the quantitative reproducibility of data for cell molecular biology and precise theoretical construction.

Gene expression cascade has been extensively studied for network study [1]. A correlation analysis of the expression pattern of a given gene is expected to give useful information for clinical diagnosis [2, 3]. Along with this evolution, protein-protein network theory has developed greatly in graph theory and phase analysis [4, 5]. Taschendorff et al. applied signaling entropy defined by correlation and transition probabilities between the proteins of interest for omics data analysis [5]. Chemokines and immunological networks are also an important theme of network research [6]. Meanwhile, considering specific reaction kinetics and thermodynamic analysis in individual reactions, there have been few studies discussing signal transduction, for example, limited to chemotaxis models of * Escherichia coli* [7], and several theoretical researches about mitogen-activated protein kinase (MAPK) cascade and bistability or ultra-sensitivity and feedback controllability of the cascade have been reported [8, 9, 10, 11, 12, 13]. In addition, information thermodynamics of MAPK cascade has been recently reported [14, 15, 16]. This article reviews these recent studies from information thermodynamics in relation to fluctuation theorem.

## 2. Modeling cell signaling

### 2.1. Signaling cascade model

Intracellular signal transduction is carried out by a chain network of intracellular biochemical reactions. The network is operated by protein-protein interaction [4, 17, 18, 19, 20, 21, 22, 23]. The cell signal cascade considered here is an interesting next chain reaction mechanism: what was originally a substrate of a biochemical reaction becomes an enzyme in the next step and is a signal molecule in each step. This can be interpreted as if signal conversion is occurring rather than changing. It is possible to model this with a chemical reaction equation. The signaling step in the above cascades may be described as follows:

ATP, ADP, and Pi represent adenosine triphosphate, adenosine diphosphate, and inorganic phosphate, respectively. Among signal pathways, the most well-known signal pathway is the MAPK cascade. As a ligand, the epidermal growth factor (EGF) stimulates a single cell via EGF receptor (EGFR) for sequential phosphorylation of c-Raf, MAP kinase-extracellular signal-regulated kinase, and kinase-extracellular signal-regulated kinase (ERK), as shown in Figure 1. This cascade can transmit signal from the cell membrane to the nucleus (Figure 1):

### 2.2. Encoding of signal events

It is possible to apply information theory by considering information source coding of signal molecules. * X* and

_{j}

** represent the signal molecules. The symbol * indicates an activated state, and mutual conversion is possible between these two. In an actual reaction, it takes a sufficiently longer time to change from the activate state to the inactive state than it is changed from the inactive state to the active state. As shown in Eq. (1), a signal series that the activating signal molecule of step*X

_{j}

*-1 activates*j

*molecule is established. Following the order in which the concentration fluctuation of each signal molecule becomes significantly larger than fluctuation at the steady state,*j

X_{1}X_{2}X_{1}*X_{3}… or X_{2}X_{3}X_{2}*X_{1}… and so on.

If the probability that a signal molecule appears in one signal event is proportional to the concentration, then

with

This gives τ the duration of the overall signal event, and the total number of signal events occurring during that time is taken as the total number of signal molecules X. The total signal event number Ψ in a given reaction event can be described as follows:

The entropy of the signal event can be defined logarithmically. The logarithm of Ψ is approximated according to Starling’s Equation [10]:

Here, we used Eqs. (1) and (2). This right-hand side is in the form of well-known mixed entropy. Each step of the signal pathway is considered to be a mixed state of two kinds of signal molecules.

### 2.3. Definition of code length

Here, the signal length for the time series formed by cellular signaling molecules is defined according to the theory of information source coding (Figure 2). τ _{+ j} is the duration of the state in which the phosphorylated molecule is in an increasing state, and τ _{− j} is negative with respect to the increase in the non-phosphorylated molecule (the decline phase of the phosphorylated molecule). A positive value is assigned for τ _{+ j}, and a negative value is assigned for τ _{− j} giving consideration of the direction of signal transduction. For example, even if a signal is transmitted in the positive direction, if the same amount of signal is transmitted in the opposite direction, the signal becomes a net zero. To evaluate such a signal amount, the direction needs to be considered. In order to capture this, positive and negative signs are assigned to time. The definition of one total code length, i.e., total length of the given signal event, the following is given:

Then, (3) and (4) can be used to obtain

Here, the average entropy production rate is defined during the phosphorylation or activation of signaling molecule using an arbitrary parameter * s*:

_{j}

### 2.4. Entropy coding

In order to maximize the number of signal events in a given duration, the relationship between the appearance probability (4) and the code length (9) should be calculated. The Lagrange undetermined constant method is adopted for this. If the constraint conditions are given by (5), (7), and (9), the function * G* can be defined as follows [16]:

If the partial derivative of * G* on the right side is taken with the occurrence probability and the total number of signal molecules, respectively, then

If the right sides of Eqs. (12), (13), and (14) are set equal to zero, then

and

This produces a simple result. Here, (15) and (16) are called entropy coding [16].

## 3. Information thermodynamics of cell signal transduction

### 3.1. Application of binary code theory

In practice, the signal transduction system can be classified according to two types of signaling molecules: the activated type is phosphorylated at each step of the reaction chain, and the inactive type is non-phosphorylated.

In terms of the change, the objective was to evaluate information transmission between each signal transmission step in the cascade. Increasing the active form induces the chemical potential caused by the mixed entropy change of each step in the signaling cascade and allows for biological signaling. * j* step component is extracted from Eq. (3)

Consider the entropy flow between the steps. For example, when a cell system is stimulated by the external environment or the state of a receptor at the boundary fluctuates (e.g., activation type) because of a change of the external environment, the signal cascades up to step * j* (i.e., transmitted). In this case, because the signal is not transmitted to step

*+ 1, the concentration fluctuation of*j

*or*X

_{j}

** differs between steps*X

_{j}

*and*j

*+ 1, and an entropy flow can occur.*j

When the signal event starts and the signal is transmitted to the * j* step, fluctuation is observed in the

*step as follows:*j

The signal has not yet reached the * j* + 1 step; hence, the entropy of the

j

^{th}molecule remains:

We can calculate the entropy current:

Here, the logarithm of the ratio of the inactive signal molecule to the active signal molecule appeared. This form often appears. Assuming that there is no new generation of signal molecules

Here, we defined the entropy current per one signal molecule, * c*:

_{j}

### 3.2. Fluctuation and signal transduction

Even in the steady state, signal events represented by this code sequence are occurring. When there is minor change in the extracellular environment, the amount of binding complex between the receptor on the cell membrane surface and stimulant ligand increases. This fluctuation increases the phosphorylated form of another signal molecule next to the complex and increases the fluctuation of the active type signal molecule through a chain reaction. Based on the signaling in the steady state, the increase in fluctuation indicates a signal response. In this manner, cell signaling can be distinguished as in the steady state or a fluctuation response to a change in the external environment.

### 3.3. Adaptation of fluctuation theorem to analysis of signal transduction

We defined transitional probability * p* (

*1|*j +

*), which is the probability of step (*j

*+ 1) given step*j

*, and*j

*(*p

*|*j

*+ 1), which is the transitional probability of step*j

*given*j

*+ 1 step during*j

*. The logarithm of ratio*τ

_{j}

*(*p

*+ 1|*j

*) / p (*j

*|*j

*+ 1) is divided by*j

*−*τ

_{j}

*and taking the limit, the AEPR from the*τ

_{−j}

*to the*j

*1 field satisfies the steady fluctuation theorem (FT) [24]:*j +

with

where * s* is an arbitrary parameter representing the progression of a reaction event. This fluctuation theorem leads to various nonequilibrium relations among cumulates of the current. We have an equation below using signal current density [16, 24]:

_{j}

Substituting the right side in Eq. (23) into the right side of Eq. (27), we had an important result [16]:

By substituting Eqs. (15) and (16) obtained by entropy coding on the right side of Eq. (28), using τ << | τ

_{j}

_{− j}| in contrast to Eq. (27) obtains:

Subsequently, Eqs. (24), (29), and (30) provide

Accordingly, entropy coding is given using Eqs. (30) and (31):

are obtained. In conclusion, the AEPR is conserved during the whole cascade of signal transduction [16].

## 4. Conclusion

Here, each signal step is handled as actual biochemical reactions based on kinetics and thermodynamics. Signal transduction is interpreted based on encoding theory and fluctuation theorem. Regarding the relationship between information and entropy in thermodynamic mechanisms [25, 26, 27], information thermodynamics has seen remarkable developments in recent years, and information theory and thermodynamics are easier to understand when they are integrated. In particular, the theoretical results based on analysis of the Szilard engine model [28] have made it possible to compute the mutual information [29, 30] and the amount of work that can be extracted from a system by free energy changes [15]. Thus, information thermodynamics may be the theoretical basis of the signal transduction.

## Acknowledgments

This work was supported by a Grants-in-Aid from the Ministry of Education, Culture, Sports, Science, and Technology, Japan (Project No. 17013086; http://kaken.nii.ac.jp/ja/p/17013086).

## Appendices and nomenclature

MAPK | mitogen-activated protein kinase |

EGF | epidermal growth factor |

EGFR | EGF receptor |

ERK | extracellular signal-regulated kinase |

ATP | adenosine triphosphate |

ADP | adenosine diphosphate |

DNA | deoxyribonucleic acid |

mRNA | messenger ribonucleic acid |

FT | fluctuation theorem |

AEPR | average entropy production rate |

## References

- 1.
Edwards D, Wang L, Sorensen P. Network-enabled gene expression analysis. BMC Bioinformatics. 2012; 13 :167 - 2.
Biasco L, Ambrosi A, Pellin D, Bartholomae C, Brigida I, Roncarolo MG, Di Serio C, von Kalle C, Schmidt M, Aiuti A. Integration profile of retroviral vector in gene therapy treated patients is cell-specific according to gene expression and chromatin conformation of target cell. EMBO Molecular Medicine. 2011; 3 :89-101 - 3.
Dopazo J, Erten C. Graph-theoretical comparison of normal and tumor networks in identifying brca genes. BMC Systems Biology. 2017; 11 :110 - 4.
Teschendorff AE, Banerji CR, Severini S, Kuehn R, Sollich P. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks. Scientific Reports. 2015; 5 :9646 - 5.
Chen CY, Ho A, Huang HY, Juan HF, Huang HC. Dissecting the human protein-protein interaction network via phylogenetic decomposition. Scientific Reports. 2014; 4 :7153 - 6.
Dutrieux J, Fabre-Mersseman V, Charmeteau-De Muylder B, Rancez M, Ponte R, Rozlan S, Figueiredo-Morgado S, Bernard A, Beq S, Couedel-Courteille A, et al. Modified interferon-alpha subtypes production and chemokine networks in the thymus during acute simian immunodeficiency virus infection, impact on thymopoiesis. AIDS. 2014; 28 :1101-1113 - 7.
Sagawa T, Kikuchi Y, Inoue Y, Takahashi H, Muraoka T, Kinbara K, Ishijima A, Fukuoka H. Single-cell E. coli response to an instantaneously applied chemotactic signal. Biophysical Journal. 2014;107 :730-739 - 8.
Blossey R, Bodart JF, Devys A, Goudon T, Lafitte P. Signal propagation of the mapk cascade in xenopus oocytes: Role of bistability and ultrasensitivity for a mixed problem. Journal of Mathematical Biology. 2012; 64 :1-39 - 9.
Purutçuoğlu V, Wit E. Estimating network kinetics of the mapk/erk pathway using biochemical data. Mathematical Problems in Engineering. 2012; 2012 :1-34 - 10.
Zumsande M, Gross T. Bifurcations and chaos in the mapk signaling cascade. Journal of Theoretical Biology. 2010; 265 :481-491 - 11.
Yoon J, Deisboeck TS. Investigating differential dynamics of the mapk signaling cascade using a multi-parametric global sensitivity analysis. PLoS One. 2009; 4 :e4560 - 12.
Lapidus S, Han B, Wang J. Intrinsic noise, dissipation cost, and robustness of cellular networks: The underlying energy landscape of mapk signal transduction. Proceedings of the National Academy of Sciences of the United States of America. 2008; 105 :6039-6044 - 13.
Qiao L, Nachbar RB, Kevrekidis IG, Shvartsman SY. Bistability and oscillations in the Huang-Ferrell model of mapk signaling. PLoS Computational Biology. 2007; 3 :1819-1826 - 14.
Tsuruyama T. The conservation of average entropy production rate in a model of signal transduction: Information thermodynamics based on the fluctuation theorem. Entropy. 2019; 20 :e20040303 - 15.
Tsuruyama T. Information thermodynamics of the cell signal transduction as a szilard engine. Entropy. 2018; 20 :224 - 16.
Tsuruyama T. Information thermodynamics derives the entropy current of cell signal transduction as a model of a binary coding system. Entropy. 2018; 20 :145 - 17.
Cheong R, Rhee A, Wang CJ, Nemenman I, Levchenko A. Information transduction capacity of noisy biochemical signaling networks. Science. 2011; 334 :354-358 - 18.
Hintze A, Adami C. Evolution of complex modular biological networks. PLoS Computational Biology. 2008; 4 :e23 - 19.
Mistry D, Wise RP, Dickerson JA. Diffslc: A graph centrality method to detect essential proteins of a protein-protein interaction network. PLoS One. 2017; 12 :e0187091 - 20.
Imhof P. A networks approach to modeling enzymatic reactions. Methods in Enzymology. 2016; 578 :249-271 - 21.
Selimkhanov J, Taylor B, Yao J, Pilko A, Albeck J, Hoffmann A, Tsimring L, Wollman R. Systems biology. Accurate information transmission through dynamic biochemical signaling networks. Science. 2014; 346 :1370-1373 - 22.
Ito S, Sagawa T. Information thermodynamics on causal networks. Physical Review Letters. 2013; 111 :18063 - 23.
Barato AC, Hartich D, Seifert U. Information-theoretic versus thermodynamic entropy production in autonomous sensory networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 2013; 87 :042104 - 24.
Chong SH, Otsuki M, Hayakawa H. Generalized green-kubo relation and integral fluctuation theorem for driven dissipative systems without microscopic time reversibility. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 2010; 81 :041130 - 25.
Sagawa T, Ueda M. Second law of thermodynamics with discrete quantum feedback control. Physical Review Letters. 2008; 100 :080403 - 26.
Sagawa T, Ueda M. Generalized jarzynski equality under nonequilibrium feedback control. Physical Review Letters. 2010; 104 :090602 - 27.
Ito S, Sagawa T. Maxwell’s demon in biochemical signal transduction with feedback loop. Nature Communications. 2015; 6 :7498 - 28.
Szilard L. On the decrease of entropy in a thermodynamic system by the intervention of intelligent beings. Behavioral Science. 1964; 9 :301-310 - 29.
Uda S, Kuroda S. Analysis of cellular signal transduction from an information theoretic approach. Seminars in Cell & Developmental Biology. 2016; 51 :24-31 - 30.
Uda S, Saito TH, Kudo T, Kokaji T, Tsuchiya T, Kubota H, Komori Y, Ozaki Y, Kuroda S. Robustness and compensation of information transmission of signaling pathways. Science. 2013; 341 :558-561