Replication Data for: Exploring The Behavior of Bioelectric Circuits using Evolution Heuristic Search

Configuration values for each experiment In order to recreate the experiments in this paper, we assume the reader has the basic knowledge on how to use the BETSE (https://github.com/betsee/betse) simulator. The supplementary materials here include all the configurations needed to run BETSE to produce each of the bioelectrical patterns in this paper. The file structure of supplementary materials folder (Paper - BioElectric - BETSE – configs) is: / ├───Task 1 - Vmem change as little as possible over time ├───Task 2 - Tissues that have as little change as possible through time, but with high variance between cells ├───Task 3 - Fit specific Vmem ├───Task 4 - High variance between Vmem cells ├───Task 5 – Smiley and bullseye ├───Task 6 - Patterns with insensitivity to shape or size of tissue ├───Task 7 - Tissue that self-heal when a few cells getting input ├───Task 8 - Tissue that retain Vmem (memory) after stimulation ├───Task 9 - cells that respond similar to flip flop operation turn out to be form of memory using chaos └───Interesting patterns │ ├───Configurations │ ├───Image Frames │ └───Movies | TOC Tasks 1-9 Each of the Task 1 to Task 9 directories contains configuration files for the corresponding Task (as described in this paper). Within each Task directory there is at least one top-level .yaml file; this is the configuration file that should be fed into BETSE to reproduce that Task. Some Task directories also have additional subfolders (e.g., extra_configs, geo) that will automatically be discovered and used by BETSE, as directed by the aforementioned .yaml configuration file. Interesting patterns We had assumed that our parameter/fitness space would have smooth gradual properties that would help our heuristic search algorithm to progress toward the desire morphological target. However, we found that our naïve approach presented a parameter space that was too confusing for exploration by our heuristic algorithm. We did encounter many “interesting patterns”; but rather than occurring along a continually increasing gradient of fitness score, these patterns presented “islands” of fitness, as shown by the peaks and valleys in Supplementary Figure 1. This proved to be problematic, because our heuristic algorithm required a steadily increasing fitness gradient in order to progress smoothly to the target morphology. The presence of morphologically “interesting” islands (peaks or valleys) interrupted what we had hoped would be a smoothly increasing gradient. The result was that the heuristic algorithm could “get stuck” in particular areas along the serpentine line of fitness scores, effectively stopping it from further exploration of other “islands” along that line. The “Interesting Patterns” directory contains 3 subdirectories – Configuration, Image Frames, and Movies – as well as a Table of Contents file. The “Image Frames” directory contains forms from the tissue process in the experiment. The “Movies” directory contains the full movies for each experiment. The TOC excel file contains 32 rows, each of which contains an image of the target tissue morphology (the “interesting pattern”), a description of fitness target that was used by the heuristic search, and the serial number of the experiment that produced that pattern. This serial number corresponds to the name of a subdirectory under Configurations. If the reader wants to reproduce the image frames and the movies for any particular “interesting pattern” shown in the TOC, s/he should go to the appropriately named Configurations subdirectory. For example, to reproduce the first target image shown in the TOC (“target gradient from high right to low left”), go to “Interesting Patterns/Configurations/0.241245355552222370.241245356”, and then run BETSE using the top-level .yaml file in that folder (gradientVmem_Betse_config.yaml). Similarly, if the readers want to watch the whole cell tissue activity during the simulated timeline go to “Interesting Patterns/Movies/0.241245355552222370.241245356”. For selected frames from that movie the reader should go to “Interesting Patterns/Image Frames/0.241245355552222370.241245356”.本数据集包含所有实验的配置参数。若要复现本文中的实验，读者需具备使用BETSE模拟器（https://github.com/betsee/betse）的基础知识。本补充材料涵盖了运行BETSE以生成本文中所有生物电模式所需的全部配置文件。 补充材料文件夹（命名为Paper - BioElectric - BETSE – configs）的目录结构如下： / ├───任务1：随时间变化尽可能小的膜电位（Vmem） ├───任务2：随时间变化尽可能小、但细胞间膜电位差异显著的组织 ├───任务3：拟合特定膜电位值 ├───任务4：细胞间膜电位差异显著 ├───任务5：笑脸与靶心图案 ├───任务6：对组织形状与尺寸不敏感的模式 ├───任务7：少量细胞受刺激后可自我修复的组织 ├───任务8：受刺激后可保留膜电位（记忆）的组织 ├───任务9：类似触发器操作的细胞——利用混沌实现记忆形式 └───趣味模式 ├───配置文件 ├───图像帧 └───影片 | 任务1-9目录总览 任务1至任务9的每个目录均包含对应实验任务的配置文件（详见本文描述）。每个任务目录下至少包含一个顶层.yaml配置文件，该文件需输入至BETSE以复现对应任务。部分任务目录还包含额外子文件夹（如extra_configs、geo），上述.yaml配置文件将自动识别并调用这些子文件夹。 ### 趣味模式 我们最初假设参数/适应度空间具备平滑渐变特性，可辅助启发式搜索算法逐步逼近目标形态学靶点。但实际发现，我们的朴素方法所得到的参数空间过于复杂，难以通过启发式算法进行探索。期间我们得到了大量“趣味模式”，但这些模式并非沿适应度分数持续递增的梯度分布，而是呈现出适应度“孤岛”特征，如补充图1中的峰谷分布所示。 这一现象给实验带来了阻碍：我们的启发式搜索算法需要稳定递增的适应度梯度，才能平滑地向目标形态推进。而形态学上的“趣味”孤岛（峰或谷）打断了我们预期的平滑递增梯度，导致启发式算法可能卡在适应度分数曲线的特定区域，无法继续探索该曲线上其他“孤岛”。 “趣味模式”目录包含3个子目录——配置文件、图像帧、影片，以及一个目录清单文件。其中： “图像帧”目录存储实验中组织演化过程的帧图像； “影片”目录存储各实验的完整模拟影片； 目录清单Excel文件包含32条记录，每条记录均包含目标组织形态（即“趣味模式”）的图像、启发式搜索所用的适应度目标描述，以及生成该模式的实验序列号。该序列号与“配置文件”子目录下的子文件夹名称一一对应。 若要复现目录清单中任意“趣味模式”对应的图像帧与影片，读者可前往对应命名的“配置文件”子目录。例如，若要复现目录清单中首个目标图像（“从高右到低左的梯度膜电位”），请进入“趣味模式/配置文件/0.241245355552222370.241245356”目录，再使用该文件夹内的顶层.yaml配置文件gradientVmem_Betse_config.yaml运行BETSE。同理，若需查看模拟时间线内完整的细胞组织活动影片，请前往“趣味模式/影片/0.241245355552222370.241245356”目录；若需查看该影片的选定帧，请前往“趣味模式/图像帧/0.241245355552222370.241245356”目录。