Simulation of the emergence of cell-like morphologies with evolutionary potential based on virtual molecular interactions

This study explored the emergence of life using a simulation model approach. The “multiset chemical lattice model” allows the placement of virtual molecules of multiple types in each lattice cell in a two-dimensional space. This model was capable of describing a wide variety of states and interactions, such as the diffusion, chemical reaction, and polymerization of virtual molecules, in a limited number of lattice cell spaces. Moreover, this model was capable of describing a wide variety of states and interactions, even in the limited lattice cell space of 100 × 100 cells. In this study, I assumed 18 types of virtual molecules, i.e., 18 virtual numbers that do not correspond to real molecules with chemical reactions represented by transformation of the numbers that occur with a specified reaction rate probability. Furthermore, it considered the energy metabolism and energy resources in the environment, and was able to reproduce “evolution,” in which a certain cell-like shape that adapted to the environment survived under conditions of decreasing amounts of energy resources in the environment. This enabled the simulation of the emergence of cell-like shapes with the four minimum cellular requirements, i.e., boundary, metabolism, replication, and evolution, based solely on the interaction of virtual molecules.


C. Emergence of cell like behaviors
Cell-like regions that are adapted to environments with scarce energy resources survive.

A. Cellular automata model by Ishida
A self-replicating model that combines the Turing pattern model and Conway's life game The central spotted pattern was divided into two spots, which then spread until the field was filled with spotted patterns.
Rewrite the transition rules of the cellular automata model into a chemical reaction network.
Figure 1.Schematic overview of the "Multiset chemical lattice model".(A) Ishida 21 improved the cellular automata model and found various shapes, such as self-replication.(B) This model allows the placement of multiple virtual molecules of multiple types in a single cell on a two-dimensional lattice space.The application of discrete stochastic transitions to each molecule allows the description of a wide variety of states and interactions in a limited number of cells, such as the migration (diffusion), transformation (chemical reaction), and linkage (polymerization) of virtual molecules.(C) Distribution of the two types of polymerized molecules.In the initial stage of the time step, an area of accumulation of red polymerized molecules was formed in the center of the lattice field, followed by the continuous disruption of this region of polymerized molecules.Concomitantly, it was confirmed that the information of the w value was retained in the green polymerized molecules and transmitted to the space.(D) The construction of an extended model that explicitly incorporates energy metabolism reproduced the phenomenon of evolution in which a certain cell-like shape that adapted to the environment survives under conditions of decreasing amounts of energy resources in the environment.
Vol:.(1234567890) www.nature.com/scientificreports/resource supply.In addition, I confirmed whether genes that favor energy production would survive in environments with scarce energy resources.The results of the time series of the case in which the amount of energy resources E input into the computational space was 800 are shown in Fig. 2A.In the figure, polymerized molecule 2, which was responsible for cell morphology, is shown in red.Under conditions of abundant energy resources, this cell-like shape emerged and divided, reproducing the same phenomenon as that reported for Ishida's previous model.
Even if the initial conditions and parameter values were the same, the pattern of the cell-like morphology changed with each calculation because excess molecules that cannot be divided into the six orientations in the molecular diffusion process are assigned to the orientations via random numbers, and because, for some parts, the presence or absence of molecular conversion is determined probabilistically based on reaction probabilities.
Figure 2B reports the amount of polymerized molecule 1 (green area in the figure), which played the role of an informant, that was generated and degraded.As the cell-like regions divided and increased in number within the computational domain, the amount of polymerized molecule 1 (informant) that was produced and degraded also increased.Thus, informants were repeatedly generated and decomposed, and, by introducing the action of selection through mutation, it was possible to change the information held by informants.

Results of the energy resource supply
The results of 10 calculations for varying amounts of the energy resource E (the amount of new energy supply when the number of molecule 20 was zero in each lattice cell) are shown in Fig. 3A.The vertical axis in Fig. 3A indicates the evolution of the ratio of the number of five consecutive molecules 6 in the polymerized molecule.As mentioned above, when molecules 6 and 7 in polymerized molecule 1 were randomly exchanged, five consecutive molecules 6 had an average occurrence rate of 19% when the composition ratio of molecule 6 was 0.68.

Figure 2.
Time series of a case where the amount of energy resources E input into the computational space was 800.(A) Polymerized molecule 2, which is responsible for cell morphology, is shown in red.Under conditions of abundant energy resources, this cell-like shape emerged and divided.(B) Amount of polymerized molecule 1 (green area in the figure), which plays the role of an informant, that was generated and degraded.As the celllike regions divided and increased in number within the computational domain, the amount of polymerized molecule 1 (informant) that was produced and degraded also increased.www.nature.com/scientificreports/All of the surviving computational cases in the 10 trials had an occurrence rate of 19% or higher, indicating that cell-like regions with a high occurrence rate of polymerized molecule 1 survived in the computational lattice space.Cell-like regions with informants that had a larger ratio of five consecutive molecules 6 were more likely to survive by producing more energy molecules 25.When the energy resource supply E was lower, the amount of energy resource input into the lattice space was reduced and the number of cases in which the cell shape disappeared in a short time step increased.Figure 3B reports the results of 10 trials for each energy resource E, showing the number of survival and disappearance events.When the resource supply E exceeded 500, the number of surviving cases was increased.Therefore, a greater supply of energy resource E implied an increased tendency to survive of the cell-like regions.

Results of the mutation rates of polymerized molecule 1
In the standard case, the mutation of polymerized molecule 1 was set to 0.1.Figure 3C depicts the results obtained when the mutation rate varied.This figure indicates the number of survival and extinction events for each of the 10 trials with different mutation rates in the case of energy resource E = 500.The results showed that higher mutation rates did not increase the number of surviving cell-like regions, probably because a high mutation rate increases the frequency of replacement of molecules 6 and 7, thus hampering the maintenance of five consecutive portions of molecule 6 for a long period; i.e., the survival ratio cannot be maintained at a high level.This result suggests that the cell-like regions will survive even when the amount of energy resources supplied to the lattice space is reduced.Figure 3B indicates that the environment created at E = 200 is difficult for cell-like regions to survive, but cell-like regions survived even when the amount of energy resources was 200 or 100 after the 4000th step.This finding suggests that regions that acquired the high number of consecutive sections of five molecules 6 were able to survive.

Results of the calculation performed in an environment of decreasing energy resource supply
A. Ratio of 5 consecutive molecular 6 (Energy resource =500, Mutation rate = 0.1)

Discussion
In this model, 18 artificial molecules were assumed, and the unique residual rate of each molecule was set, as well as the reaction probability of intermolecular reactions and the molecular ratio of molecule 6 to molecule 7 of polymerized molecule 1 (representing the parameter of morphology).Molecules were placed in each cell as initial values, and the local numerical manipulation of the diffusion alone, reaction, polymerization, and decomposition of each type of molecule allowed us to simulate the emergence and replication of cell-like shapes in which polymerized molecule 2 aggregated.A model was constructed in which the information on the morphological parameter was retained in the form of the molecular configuration ratio of polymerized molecule 1, and this configuration ratio was inherited in the process of the emergence and disappearance of self-replicating regions.Furthermore, a mutation process was introduced in which the composition ratio of molecules 6 and 7 of polymerized molecule 1 was not changed, whereas the order of the molecules was randomly changed and a setting in which the amount of energy (molecule 25) production changed according to the number of five consecutive molecules 6 in polymerized molecule 1 was introduced.
If the ratio of molecule 6 to molecule 7 in polymerized molecule 1 was maintained at 68:32 and molecules 6 and 7 were randomly exchanged, the average ratio of five consecutive molecules 6 was 19%.However, when looking at the calculation cases in which the generation, division, and disappearance of cell-like shapes continued, the ratio of five consecutive cases became higher than 19% in all cases.This means that if, during the mutation process, the reaction network that maintains the cell shape fails to maintain the shape because of the lack of energy molecules (molecule 25) when the ratio of five consecutive molecules 6 is not high, the cell-shaped region will disappear.Conversely, a phenomenon is expected to be observed in which the cell-like shape that retains the polymerized molecule 1 with a high ratio of five contiguous molecules 6 survives.
When the amount of energy resources (molecule 20) input into the computational lattice space varied, more abundant energy resources implied an increase in the survival of the cell-like shapes.Furthermore, when the amount of the energy resource input was reduced after a certain time step, the cell-like region that retained the polymerized molecule 1 with a high ratio of five consecutive molecules 6 survived.Because this region was able to maintain high energy production, it is thought that the cell shape can survive and adapt to environments with low energy resources (which would not normally be conducive to survival).The model suggests that, even in an environment with limited energy resources, individuals with informants that can generate a great amount of energy will survive; therefore, it is thought that a basic process of evolution in response to the environment was created.
The surviving cell-like regions were found to have a greater ratio of five conservative molecules 6 than the random average.This corresponds to spontaneous selection for genetic information (survival of individuals that happen to be in favorable conditions).This suggests a primitive evolution, in which cells with more favorable informants than the average survive.
In the previous Ishida 23 model, only three types of cell shape emergence, replication, and metabolism were realized; in contrast, the present model may have shown that the emerged cellular shape also possessed "evolutionary potential." Based on the Ishida 23 model, a model was constructed that explicitly incorporated reactions of energy metabolism by adding three new molecules in addition to the 15 original molecules.This model was able to produce cell-like shapes with the ability to evolve to an energy resource environment.Based on this model, a cell-like shape with the four conditions of a cell, i.e., boundary, metabolism, replication, and evolution, could be emergently created by assuming several chemical reactions and molecular polymerization.
Although this model remains a simple model at present, as the number of molecule types increase and the degree of freedom of the reaction increases, and under energy supply that is sufficient to sustain the reaction, new reaction paths and multiple reaction loops may be formed.This could potentially reproduce the process of evolution to more complex cells.The potential application of this mechanism to actual chemical reactions may provide a basis for considering the synthesis of minimal cells (protocells) or the self-organized synthesis of nanomachines.
Conversely, the weakness of this model lies in that it assumed that the formation and decomposition of 100 linked polymerized molecules can be easily realized.In fact, this cannot be easily achieved in a natural reaction environment unless a catalyst or other means are already present.Several difficulties remain regarding the use of this model to explain the emergent process in which the first life simultaneously maintained all four conditions in the absence of a catalyst.
To solve this issue, as the next stage of research, it is necessary to construct a computational model that includes the emergence process of long polymerized molecules with catalytic functions, starting from single molecules and light polymerized molecules with a few molecules (which can polymerize and degrade naturally via the action of minerals or other substances without protein catalysts), and simulate life emergence including "catalyst emergence."

Model configuration
In this study, an extended two-dimensional lattice-type multiset chemical model, as reported by Ishida 22 , was constructed.This Ishida model applied a multiset chemical model in lattice space.The computational representation of chemical reactions has been modeled as a multiset chemical model, which is also known as an "artificial chemical model" 24 .Multiset refers to the concept of a mathematical class plus multiplicity.Using the multiset concept, it was possible to construct a model that took into account the number of molecules as well as the type of molecules.For example, considering molecules a and b, a multiset representation of the state with three molecules The rewriting rule can also be set to occur with a certain reaction probability.Once the rewriting rule and the reaction probability of the chemical reaction were determined, this modeling allowed the number of molecules to be rewritten.
In each lattice cell, the number of molecules was recorded for each molecular species.The following is the modeling method of the diffusion, reaction, and polymerization processes of each molecule in the lattice cell.
As shown in Fig. 5A, molecular diffusion can be represented by the exchange of molecular numbers between adjacent cells.In this model, the diffusion of molecules and polymerized molecules in each cell was represented  An empty box, after the removal of the molecules from it, represented a state in which the polymerized molecules were broken down into small molecules.
by the process depicted in Fig. 5A.The residual rate r was defined here as an alternative parameter to the diffusion coefficient.
The residual ratio r, as the parameter of each molecular type, is the fraction of unmoved molecules in each cell.The parameter r is a molecule-specific attribute value that was fixed for all cells and all time steps.As shown in Fig. 5A, the following calculations were performed (where b n,t is the number of molecules in cell n at time t): (1) a proportion of the molecules, i.e., b n,t × (1 − r) × 1/6, diffused toward the six adjacent cells evenly; (2) the residual molecules, b n,t × r, remained in the original cell.If the number of molecules was not an integer multiple of 6, the remainder of the molecules were distributed between adjacent cells with equal probability.
Next, as shown in Fig. 5B, chemical reactions were represented by rewriting the number of molecular types based on reaction probabilities according to the multiset rewriting rule.In this model, 15 types of molecules were set up to replace the algorithm of previous studies that applied the Ishida model 21,23 to molecular reactions, as described below.As this was an artificial chemical model, it did not correspond to real molecules, and molecular species were represented as "molecule 1, " "molecule 2, " etc.The chemical reaction was then assumed to occur at each time step with a certain probability of change in the number of molecules in each cell, as shown in Fig. 5B.For example, in the case of a reaction from molecule A to molecule B, molecule A is converted to molecule B with the reaction probability p.Assuming that the number of molecules A at time t is A n and the number of molecules B is B n , the number of molecules at the next time step is as follows.
Furthermore, macromolecules such as cell membranes and genes play an essential role in the emergent processes of life; therefore, methods that can be used to represent molecular polymers are necessary.In this model, a virtual box filled with virtual molecules collectively was considered, as shown in Fig. 5C.The empty box in the space was placed in anticipation of the polymerization of molecules when the box was filled with them.When the box was emptied after the removal of the molecules, it represented a state in which the polymerized molecules were broken down into small molecules.Furthermore, the box itself was modeled to be diffuse in the lattice space.

Configuration of the molecular reaction network
Based on Ishida's model 22 , 15 different molecules were set up to replace the algorithm of the model 21,23 , which realized the emergence of a cell-like shape in the cellular automaton model via molecular reactions.Ishida's model 21 is a CA model, which combines the Turing pattern model with Conway's Game of Life. Figure 6A presents an overview of the transition rules for this CA model.This model allows for the creation of diverse patterns (region formation, movement, and replication) in the 2D field.Figure 6B provides an overview of the transition rule conversions of this CA model to the multiset chemical model (see Ishida's model 22 for details).In Ishida's multiset model 22 , the process for calculating the sum of states of the CA model is replaced by two virtual molecules changing during diffusion.Furthermore, the operation of the number of states indicated in the transition expression is replaced by the molecular changes in chemical reactions, and the decision process by inequality is replaced by the molecules that become surplus due to molecular reactions.By substituting these processes, I could successfully derive a model that exhibits the same behavior (region formation, moving, and self-replication) as the CA transition rule.The 15 molecules had the following roles.
-Molecule 1: the material to be converted into each molecule (initially, a large number of these molecules were placed in the lattice space).-Molecules 2 and 3: corresponded to diffusing substances in the Turing pattern model (the difference in the diffusion coefficients was expressed by the difference in their residual rate).-Molecules 4 and 5: resulting from Molecules 2 and 3 during diffusion, respectively.-Molecules 6 and 7: materials for "polymerized molecule 1, " which was a polymer of molecules 6 and 7, with the ratio of molecules 6 and 7 representing the morphology parameter in the Ishida model 24  Moreover, in Ishida's model 22 , the whole reaction pathways for each molecule were set up as shown in Fig. 7.By setting up the molecular diffusion and reaction in each lattice as described above, it was possible to realize algorithms similar to those of the Ishida's CA model 21 and to generate a variety of forms, including Turing patterns.The reaction like a + b = > a + c in Fig. 7 indicates that the reaction proceeds under the condition that molecule a is present as a catalyst.The system then set up cyclical reaction paths where molecules decompose at a certain ratio and return to molecule 1; and the overall number of molecules is conserved.Moreover, this chain of reactions is thought to model metabolism concomitantly.
Furthermore, based on the molecular setup designed in this study, new molecules related to energy metabolism and their reactions were added.Specific descriptions are presented in the following section.

Supply of energy resources and their removal from lattice space
The former Ishida model 22 is built on the assumption of the presence of sufficient energy in the lattice space for all chemical reactions to proceed, and does not explicitly incorporate energy metabolism.In the present study, the model was extended to allow the introduction of molecules that served as energy resources and molecules that were consumed as energy during each chemical reaction.
First, molecule 20 was introduced as an energy resource, with molecule 25 carrying energy to assist the chemical reactions of other molecules and molecule19 being a waste molecule from which energy was taken from the 25 molecule.The resource molecule 20 s were continuously supplied in constant amounts throughout the lattice space, whereas the waste molecule 19 s was excluded from the space.These molecules allowed the explicit inflow and removal of energy into and out of the lattice space.
The reactions of the energy molecules were set up as follows.
Reaction conditions: There must be a polymerized molecule 1 with a molecular arrangement that serves as a catalyst.
Reaction rate: It is set that the reaction rate varies according to the amount of a specific catalyst sequence (in which five molecules 6 are connected in succession) in the polymerized molecule 1.
Moreover, in each of the reactions included in the reaction network shown in Fig. 7, molecule 25 was set to be consumed with each reaction as the reaction energy.
Molecule 25 → Molecule 19 + energy.Based on the conditions described above, to maintain the cell-like shape in which the polymerized molecules 2 are assembled, molecules 25 must continue to be supplied to the area.For molecule 25 to exist, molecule 20 must be supplied to each lattice cell.

Catalytic function set up based on polymerization molecule 1
In the reaction that converts molecule 20 to molecule 25, polymerized molecule 1 was assumed as the catalyst.As shown in Fig. 8A, polymerized molecule 1 was composed of molecule 6 and molecule 7 in a specific ratio, Figure 7. Network map of chemical reactions 22 .Summary of the network of chemical reactions in Ishida's model 22 .Molecular diffusion and a cyclic network of molecular reactions were constructed to create an algorithm identical to the Ishida model 21 .These reaction networks allow the formation of cell-like shapes and the emergence of self-replication.
with the ratio being a parameter of morphology; however, concomitantly, a specific sequence of molecule 6 was assumed to have a catalytic function.
A composition ratio of 0.68 of molecule 6 and molecule 7 (molecule 6/(molecule 6 + molecule 7)) was used in this paper, in reference to the Ishida model 22 setting.This value was a parameter that allowed the emergence of cell-like shapes and the emergence of a continuously dividing state in the original model, in which the ratio of these molecules alone was used as a parameter for morphogenesis, without considering the order of molecules 6 and 7.
In the present study, the ratio of molecule 6 to molecule 7 was used as a parameter, and a specific sequence of molecule 6 was used as a catalyst.Specifically, as shown in Fig. 8B, it was assumed that, if there were five consecutive sections of molecule 6, this part had a catalytic function to produce energy molecule 25.Such a setting is an analogy of a particular sequence of amino acids performing a particular catalytic function.
Here, as shown in Fig. 8C, the number of consecutive portions of molecule 6 in the polymerization molecule 1 in each lattice was counted, and the reaction probability was set to change according to the number of configurations of five consecutive portions of molecule 6.Although any sequence can be used as the catalytic function, www.nature.com/scientificreports/

Conditions of calculations
The diffusion of molecules can vary according to the residual rate of each molecule, and the values in Table 1 were set as the standard case, referring to the settings in the previous Ishida's model.A higher residual rate implied a higher percentage of molecules remaining in the lattice cell.Moreover, a smaller value resulted in a more rapid diffusion.

Initial conditions
As an initial condition, molecule 1 was placed throughout the lattice with 1,000,000 molecules in each cell, molecules 6 and 7 with 50,000 molecules, and molecule 12 with 100,000 molecules.Concomitantly, 100 energy resource molecules 20 and 100 energy molecules 25 were placed.
In addition, 100 molecules 2 and 3 in some cells were placed in the central area of the lattice field (Fig. 9B).In addition, 10 polymerized molecules 1, which were polymerized with ratio molecules 6 and 7 with morphological parameter w, were placed in the central region of the space, as shown in Fig. 9C.
For the determination of the standard case value of each parameter, the parameters were adjusted through several trials and errors, in reference to the values of the previous Ishida model 22 .Table 1 lists the values of each parameter for the standard case.

Calculation case
The effects of the major parameters on the calculation results are summarized in the Ishida model 22 .The parameters investigated in this study were the amount of energy resources E (the number of molecules 20) supplied to the lattice space at each computational step and the mutation rate of the polymerized molecule 1.In each cell, it was assumed that the following number of molecules 20 would be supplied in the next step if molecule 20 was zero.
In addition, to simulate an environment in which the energy resources decreased over time, a setting in which energy resources E were reduced after a certain time step was also used in calculations at the same time.The number of resources was reduced to 200 after the 4000th step and to 100 after the 6000th step relative to the initial supply of resources.Furthermore, the mutation rate was calculated for 0.1, 0.2, 0.3, 0.4, and 0.5 using 0.1 as the standard.

Figure 4
Figure4reports the results obtained by reducing the amount of energy resources supplied to the lattice space over time.As in Fig.3A, the ratio of the number of occurrences of five consecutive molecules 6 in polymerized molecule 1 is shown.In the figure, the results of the calculations are indicated for two calculation patterns; first three cases are one in which the amount of energy resources E was kept constant at 800, and another three cases in which the amount of energy resources was maintained at 800 until step 4000, reduced to E = 200 after step 4000, and reduced to E = 100 after step 6000.This result suggests that the cell-like regions will survive even when the amount of energy resources supplied to the lattice space is reduced.Figure3Bindicates that the environment created at E = 200 is difficult for cell-like regions to survive, but cell-like regions survived even when the amount of energy resources was 200 or 100 after the 4000th step.This finding suggests that regions that acquired the high number of consecutive sections of five molecules 6 were able to survive.
Energy resource supply E = 500 until 4000 time steps, E = 200 after 4000 time step, E = 100 after 6000 time step Constant energy resource supply E = 800

Figure 4 .
Figure 4. Results of the calculations performed in an environment of a decreasing energy resource supply.(A) Results obtained by reducing the amount of energy resources supplied to the grid space over time.As in Fig. 3A, the ratio of the number of occurrences of five consecutive molecules 6 in polymerized molecule 1 is shown.In the figure, the results of the calculations are shown for three cases, one in which the amount of energy resources E was kept constant at 800, and another three cases in which the amount of energy resources was maintained at 800 until step 4000, reduced to E = 200 after step 4000, and reduced to E = 100 after step 6000.
https://doi.org/10.1038/s41598-024-52475-9www.nature.com/scientificreports/ a and two molecules b would be {a, a, a, a, b, b}.The transformation of molecule a and molecule b into molecule c through chemical reactions can be expressed by the following multiset rewriting rule, {a, a, a, b, b} → {a, c, c}.

Figure 5 .
Figure 5. Schematic representation of the diffusion and chemical reaction of molecules, and virtual box model representing the state of the polymers.(A) Molecular diffusion can be represented by the exchange of molecular numbers between adjacent cells.In this model, the diffusion of molecules and polymerized molecules in each cell was represented by the following process.The residual rate was defined here as an alternative parameter to the diffusion coefficient.The residual ratio r, as the parameter of each molecular type, was the fraction of unmoved molecules in each cell.The parameter r, as a molecule-specific attribute value, was fixed for all cells and all time steps.The calculations (as shown in the figure) were performed, where b n,t is the number of molecules in cell n at time t: (1) a proportion of the molecules, b n,t × (1 − r) × 1/6, diffused toward the six adjacent cells evenly; (2) the residual molecules, b n,t × r, remained in the original cell.If the number of molecules was not an integer multiple of 6, the remainder of the molecules were distributed between adjacent cells with equal probability.(B) Chemical reactions were represented by rewriting the number of molecular types based on reaction probabilities.The figure provides one example of the case of a reaction from molecule a to molecule b at reaction rate p. (C) A virtual box filled with virtual molecules collectively was assumed.The empty box in the lattice space was placed to represent the polymerization of molecules when the box was filled with molecules.An empty box, after the removal of the molecules from it, represented a state in which the polymerized molecules were broken down into small molecules.
algorithm; depending on the value of the morphological parameter, the Turing pattern can be controlled from black spots on white, stripe patterns, and white spots on black.-Molecules 8, 9, 10, and 11: describing the transition equation of the Ishida model 23 in chemical reactions.-Molecule 12: the material for "polymerized molecule 2, " which represents the boundary of the cell.-Molecules 13, 14, and 15: describing the transition equation of the Ishida model 23 in chemical reactions.

Molecule 20 →Figure 6 .
Figure 6.Overview of the transition rule conversions of this CA model to a multiset chemical model.(A) Overview of the transition rules of Ishida's CA model 21 , which combines the Turing pattern model with Conway's Game of Life.(B) In Ishida's multiset model22 , the sum rule of Ishida's CA model is replaced by two virtual molecules with molecular changes during diffusion.Furthermore, the operation of the number of states indicated in the transition rule as well as the decision process by inequality are replaced by the molecular changes in chemical reactions and the molecules that become surplus due to molecular reactions respectively.

Figure 8 .
Figure 8. Catalyst modeling.(A) Polymerized molecule 1 is composed of molecule 6 and molecule 7 in a specific ratio, with the ratio being a parameter of morphology.(B) The ratio of molecule 6 to molecule 7 was used as a parameter, and a specific sequence of molecule 6 was used as a catalyst.Specifically, it was assumed that, if there are five consecutive sections of molecule 6, this part has a catalytic function to produce 25 energy molecules.(C) The number of consecutive portions of molecule 6 in polymerization molecule 1 in each lattice was counted, and the reaction probability was set to change according to the number of configurations of five consecutive portions of molecule 6.

Emergence of cell-like regions with evolutionary capacity
D.