3e:null 3f:["$","title",null,{"children":"Parliament of Genes | Alex Lamb | Observable"}] 45:Tab8,{ const context = DOM.context2d(width, height); const colWidth = width / cellCount; const healthyColors = ['lightgray','firebrick','mediumpurple','steelblue']; const badColors = ['gray','firebrick','mediumpurple','steelblue']; let maxFit = 0; while (true) { context.clearRect(0, 0, width, height); if (cellList.length > 0) { let topCell = cellList[cellCount - 1]; maxFit = topCell.fitness; let maxGenes = 0; for (let cell of cellList) { maxGenes = Math.max(cell.elements.length, maxGenes); } for (let i = 0; i < cellCount; i++) { let x = i * colWidth; let h = (cellList[i].fitness * height) / (maxFit * 2); let y = (height/2) - h; context.beginPath(); let hue = Math.floor(cellList[i].id * 360); // context.fillStyle = `hsl(${hue},100%,50%)`; context.fillStyle = `hsl(${hue},60%,40%)`; // context.fillStyle = "black"; context.rect(x, y, colWidth-1, h); context.fill(); if (cellList[i].fail == true) { context.beginPath(); context.fillStyle = "black"; context.rect(x, 0, colWidth-1, 5); context.fill(); } y = (height/2); h = (cellList[i].elements.length * height) / (maxGenes * 2); let defendFrac = cellList[i].justDefend / cellList[i].elements.length; let defectFrac = cellList[i].justDefect / cellList[i].elements.length; let bothFrac = cellList[i].both / cellList[i].elements.length; let neitherFrac = cellList[i].neither / cellList[i].elements.length; let fractions = [neitherFrac,defectFrac,bothFrac,defendFrac]; let colors = healthyColors; if (cellList[i].functional <= minFunctional) colors = badColors; let offset = (height/2); for (let j = 0; j < 4; j++) { let fh = h*fractions[j] context.beginPath(); context.fillStyle = colors[j]; context.rect(x, offset, colWidth-1, fh); context.fill(); offset += fh; } } let level = (height/2); level += (minFunctional * height) / (maxGenes * 2); context.beginPath(); context.moveTo(0, level); context.lineTo(width, level); context.strokeStyle = "gray"; context.stroke(); context.fillStyle = "black"; // context.fillText(`${maxFit}`, -2 + width, 0); context.fillText(`Max fitness: ${maxFit.toFixed(2)}`, width - 78, 10 + height/2); } context.canvas.onmousedown = event => { mutable paused = !paused; }; yield context.canvas; } }46:T9ef,{ const context = DOM.context2d(width, height); while (true) { context.clearRect(0, 0, width, height); if (history.length >= 1) { let segWidth = Math.floor(width / history.length); let startIndex = 0; let segments = history.length; if (history.length > width) { segWidth = 1; startIndex = history.length - width; segments = width; } let barGap = 1; if (segments > 1500) { barGap = 1000; } else if (segments > 150) { barGap = 100; } else if (segments > 15) { barGap = 10; } else { barGap = 1; } for (let i = 1; i < segments; i++) { let xStart = (i-1) * segWidth; let xEnd = i * segWidth; let iStart = startIndex + (i-1); let iEnd = iStart + 1; let yStart = height - (history[iStart].value.defectFrac) * height; let yEnd = height - (history[iEnd].value.defectFrac) * height; context.beginPath(); context.moveTo(xStart, yStart); context.lineTo(xEnd, yEnd); context.strokeStyle = "red"; context.stroke(); yStart = height - (history[iStart].value.defendFrac) * height; yEnd = height - (history[iEnd].value.defendFrac) * height; context.beginPath(); context.moveTo(xStart, yStart); context.lineTo(xEnd, yEnd); context.strokeStyle = "blue"; context.stroke(); yStart = height - (history[iStart].value.fitFrac) * height; yEnd = height - (history[iEnd].value.fitFrac) * height; context.beginPath(); context.moveTo(xStart, yStart); context.lineTo(xEnd, yEnd); context.strokeStyle = "green"; context.stroke(); if (iEnd % barGap == 0) { let v = iEnd * speed; context.beginPath(); context.moveTo(xEnd, 0); context.lineTo(xEnd, height); context.strokeStyle = "gray"; context.stroke(); context.fillText(`${v}`, xEnd + 2, height-10); } } } for (let i = 0; i < 3; i++) { let f = (i+1) / 4; context.beginPath(); context.moveTo(0, (1-f) * height); context.lineTo(width, (1-f) * height); context.strokeStyle = "gray"; context.stroke(); context.fillText(`${f}`, 0, -2 + ((1-f) * height)); } context.canvas.onmousedown = event => { mutable paused = !paused; }; yield context.canvas; } }47:T75e,{ const context = DOM.context2d(width, height); while (true) { context.clearRect(0, 0, width, height); //TODO: Put in heredity plot if (history.length >= 1) { let segWidth = Math.floor(width / history.length); let startIndex = 0; let segments = history.length; if (history.length > width) { segWidth = 1; startIndex = history.length - width; segments = width; } let barGap = 1; if (segments > 1500) { barGap = 1000; } else if (segments > 150) { barGap = 100; } else if (segments > 15) { barGap = 10; } else { barGap = 1; } for (let i = 1; i < segments; i++) { let xStart = (i-1) * segWidth; let xEnd = i * segWidth; let iStart = startIndex + (i-1); let iEnd = iStart + 1; let yStart = height - (history[iStart].value.defectFrac) * height; let yEnd = height - (history[iEnd].value.defectFrac) * height; for (let j = 0; j < height; j++) { if (history[iEnd].value.popSpread[j] > 0) { let hue = Math.floor(j * 360 / height); context.beginPath(); context.moveTo(xStart, j); context.lineTo(xEnd, j); context.strokeStyle = `hsl(${hue},60%,40%)`; context.stroke(); } } if (iEnd % barGap == 0) { let v = iEnd * speed; context.beginPath(); context.moveTo(xEnd, 0); context.lineTo(xEnd, height); context.strokeStyle = "gray"; context.stroke(); context.fillText(`${v}`, xEnd + 2, height-10); } } } context.canvas.onmousedown = event => { mutable paused = !paused; }; yield context.canvas; } }48:Ta4a,md` ### So what's going on? The answer to how come your cells can stay alive is because they all work together to keep selection distorter genes at bay. You might wonder how this is possible. After all, individual genes have no intelligence or agency. And this process of cooperation would need to happen below the level of an individual organism. Doesn't the logic of selfish genes apply? The explanation is that those cells that keep a handle on selection distorter genes get to reproduce. Those that don't end up dying. So sets of genes that suppress distorters outcompete those that do not. This mechanism was first proposed in 1971 by Egbert Leigh who coined the term *Parliament of Genes*, and that's what you're watching here when the sim runs. I learned about it from a wonderful book called [The Origins of Life](https://www.amazon.com/gp/product/B008B38ZIC/ref=dbs_a_def_rwt_bibl_vppi_i4). ### Why should we care? On the face of it, this might seem an esoteric topic for an Observable notebook, but nothing could be further from the truth. The Parliament of Genes is fascinating because it shows how altruism can arise between unrelated entities when those entities live or die based on their ability to cooperate. This has relevance at every scale of life on Earth. It shines a light on how life probably started, as it shows how accidentally cooperative acts might have given rise to what's called 'autocatalytic sets' of mutually reproducing molecules. At the same time, it shows how it's possible to achieve altruism at scale. The Parliament of Genes effect is the most likely explanation for everything from the rise of democracy on the world stage to the appearance of altruistic morality in world religions. And in subsequent notebooks I'll try to put more flesh on both of those statements so you can see how that works. ### Things to try The PofG efect is most impressive when the fitness constraints on groups of cooperating genes are extreme. Try pushing the **minFitness** slider close to the top and watch the cells frantically organize themselves to keep distorters at bay. Make the fitness constraints more relaxed and the organisms will never get as competitive, because the internal pressure of the distorters tring to take over the genome will block the cells from improving. Note: If you push the minFunctional slider so high that all the cells show dark gray, none of them will be able to reproduce, so the sim won't evolve. But set it so that just one or two are light and you'll see an explosion of activity. See how fit you can get your population by tweaking the sliders while the sim runs. `49:T414,//This gets called at the start of the simulation. It sets up our initial conditions. function init() { /* NOTE: When referencing simulation variables directly, we *always* use the keyword mutable, otherwise the sim won't evaluate as expected. */ mutable cellList = []; for (let i = 0; i < cellCount; i++) { let cell = { elements: [], fitness: 0, justDefect: 0, justDefend: 0, both: 0, id: Math.random(), fail: false }; mutable cellList.push(cell); } for (let i = 0; i < geneCount; i++) { let gene = {}; generateGene(gene); let cellIndex = Math.floor(Math.random() * cellCount); //Pick a cell at random let cellGenes = mutable cellList[cellIndex].elements; cellGenes.push(gene); //Put the gene in the cell } for (let i = 0; i < cellCount; i++) { refreshCellStats(mutable cellList[i]); } mutable cellList.sort((a, b) => a.fitness - b.fitness); mutable steps = 0; mutable initialized = true; mutable history = []; }4a:Tb16,function updateGene(cellList, geneList) { let totalGenes = 0; for (let cell of cellList) { totalGenes += cell.elements.length; } let geneIndex1 = Math.floor(Math.random() * totalGenes); let gene = null; let cell = null; let cellGenes = null; let cumulative = 0; for (let c of cellList) { cumulative += c.elements.length; if (geneIndex1 < cumulative) { cell = c; cellGenes = cell.elements; let geneIndex2 = Math.floor(Math.random() * cellGenes.length); gene = cellGenes[geneIndex2]; break; } } if (Math.random() < pMutation) { //Mutate the gene and update the cell stats generateGene(gene); refreshCellStats(cell); } if (gene.defector > 0) { //Check for defenders who might prevent defector reproduction let blocked = false; for (let i = 0; i < defenderCheck; i++) { let other = cellGenes[Math.floor(Math.random() * cellGenes.length)]; if (other != null) { // If the selected other is a defender, and isn't a copy... if (other.defender > 0 && gene.value != other.value && Math.random() < pDefend) { blocked = true; break; } } if (cellGenes.length <= 0) { cell.fail = true; } else { cell.fail = false; } } if (!blocked) { //If the gene isn't blocked, it copies itself and we update cell stats let child = {}; if (Math.random() < pMutation) { generateGene(child); } else { copyGene(gene, child); } cellGenes.push(child); refreshCellStats(cell); } } // If we're updating the entire cell... if (Math.random() < pCellUpdate) { // Cells only reproduce with enough functional genes, also // We only attempt cell reproduction if we land on a good gene // Because useless genes gum up the machinery of cell reproduction by focusing on themselves // And therefore suppress reproduction in proportion to their appearance if (cell.functional > minFunctional) { //And the cell has enough genes to reproduce... let otherIndex = Math.floor(Math.random() * cellCount); let other = cellList[otherIndex]; if (cell.fitness > other.fitness) { replaceCell(cell, other); } } } // If a cell has a non-functional gene count or has bad luck, then maybe it dies instead if (cell.functional <= minFunctional || cell.fitness <= minFitness || Math.random() < pDeath) { for (let i = 0; i < 10; i++) { //Where 10 is a sanity check to prevent endless looping let otherIndex = Math.floor(Math.random() * cellCount); let other = cellList[otherIndex]; if (other.functional > minFunctional && other != cell) { replaceCell(other, cell); break; } } } }4b:T436,function refreshCellStats(cell) { let defectors = 0; let justDefect = 0; let justDefend = 0; let both = 0; let neither = 0; let functionSet = new Set(); for (let gene of cell.elements) { if (gene.defector > 0) { defectors++; if (gene.defender > 0) { both++; } else { justDefect++; } } else { functionSet.add(gene.value); if (gene.defender > 0) { justDefend++; } else { neither++; } } } let defendFrac = (both + justDefend)/cell.elements.length; let coverage = functionSet.size/cell.elements.length; let defectFrac = (both + justDefect)/cell.elements.length; // cell.fitness = 1 - (defectors/cell.elements.length); // cell.fitness = coverage - defendCost * defendFrac; cell.fitness = coverage - (defendCost * defendFrac) - (defectCost * defectFrac); cell.fitness = Math.max(cell.fitness, 0); cell.justDefect = justDefect; cell.justDefend = justDefend; cell.both = both; cell.neither = neither; cell.functional = functionSet.size; }4c:T5d1,// Not sure about this visualization because the explanatory power seems limited // { // const context = DOM.context2d(width, height); // const colWidth = width / cellCount; // const colors = ['rgba(128,128,128,0.5)', // 'rgba(178, 34, 34,0.5)', // 'rgba(70, 130, 180,0.5)', // 'rgba(147, 112, 219, 0.5)'];//But with alpha // while (true) { // context.clearRect(0, 0, width, height); // if (cellList.length > 0) { // for (let i = 0; i < cellCount; i++) { // let x = i * colWidth; // /* // I interate over the genes in the cell // For each gene I draw a line at the Y coordinate for that value // The color of the line is given by the gene type // All lines have an alpha value so they can write over each other // */ // for (let gene of cellList[i].elements) { // let y = gene.value; // let colorIndex = 0; // colorIndex += gene.defector; // colorIndex += gene.defender * 2; // let color = colors[colorIndex]; // context.beginPath(); // context.fillStyle = color; // context.rect(x, y, colWidth-1, 2); // context.fill(); // } // } // } // context.canvas.onmousedown = event => { // mutable paused = !paused; // }; // yield context.canvas; // } // }4d:[] 43:{"id":"a43f31bb9c8d6009","slug":"parliament-of-genes","title":"Parliament of Genes","thumbnail":"3acfd402828e992d9658169c574920160c7ef48f5b90bafa29137381f4e9c66b","access_level":"public","create_time":"$D2019-12-02T02:15:34.228Z","update_time":"$D2019-12-21T22:08:25.592Z","view_time":null,"first_public_time":"$D2019-12-19T01:10:31.607Z","has_unpublished_edits":null,"fork":true,"stars":1,"starred":false,"owner":{"type":"team","id":"8894fb29b6856cb1","login":"alexlamb","name":"Alex Lamb","avatar_url":"https://avatars-next.observableusercontent.com/f73999423b64851d4b327fc2958b4b8d311abd8af0053b041a9f7d4d9bed9465","tier":"starter_2024","bio":"Science fiction author, complex systems researcher, software developer, improv instructor, dad, soft skills trainer, etc."},"version":920,"publish_version":920,"role":null,"authored":false,"authors":[{"type":"individual","id":"8f01320635b06910","login":"alexlamb","name":"Alex Lamb","avatar_url":"https://avatars-next.observableusercontent.com/f73999423b64851d4b327fc2958b4b8d311abd8af0053b041a9f7d4d9bed9465","tier":null,"bio":"Science fiction author, complex systems researcher, software developer, improv instructor, dad, soft skills trainer, etc."}],"members":[{"type":"individual","id":"8f01320635b06910","login":"alexlamb","name":"Alex Lamb","avatar_url":"https://avatars-next.observableusercontent.com/f73999423b64851d4b327fc2958b4b8d311abd8af0053b041a9f7d4d9bed9465","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Parliament of Genes","cells":[{"id":0,"value":"md`# Parliament of Genes`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":400,"value":"md`### What are you seeing here?\nEvery cell in your body can find itself hosting *selection distorter genes* that subvert the protein machinery around them to copy themselves at the cost of the cell as a whole. Such genes show up all the time, and result in the death of the cell if left unchecked. So how come you're still alive? How do cells manage to exist at all? This notebook explains using an interactive simulation. Try clicking on the simulation to run it, and lean more by reading the explanation below.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":389,"value":"md`### Cells ranked by fitness\nEach cell in our population is represented by a vertical stripe. Colored bars above the line show the relative 'fitness' of each cell (how healthy it is). Bars below the line show the proportion of different kinds of genes. Gray shows normal genes. Red shows distorter genes. Blue shows genes that protect against distorters, and purple shows genes that both defend *and* distort. (Weird, but theoretically possible.) Dark gray bars show cells without enough genes to reproduce. \n(Click on the plot to run or pause the simulation)\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":166,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":394,"value":"md`### Distorters, defenders, and overall fitness over time\nBelow is a timeline that shows how the population evolves. The red line shows the number of distorter genes in the population, blue shows the defenders, and green shows the overall health of the population. \n(Click on the plot to run or pause the simulation)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":775,"value":"md`### Genetic drift over time\nI couldn't resist including this plot. It shows the distribution of ID-colors associated with each cell. (Children of cells receive similar colors to their parents.) This means that you can see the genetic drift of the population as it develops. \n(Click on the plot to run or pause the simulation)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":752,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":163,"value":"// viewof bReset = button({value: \"Reset\", description: \"Click here to return the simulation to its initial conditions\"})\nviewof bReset = md``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":108,"value":"md`#### minFitness\nThe fitness level at which instant death kicks in for cells\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"viewof slideMinFit = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"minFitness = slideMinFit / 100;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"md`#### pDefend\nThe probability that a given defender gene will successfully check a distorter\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":86,"value":"viewof slideDefend = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"pDefend = slideDefend / 1000;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"md`### minFunctional\nThe smallest number of differentiated genes an organism needs to persist\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":448,"value":"viewof slideFunctional = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":801,"value":"minFunctional = slideFunctional;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":899,"value":"md`### pDeath\nThe probability that a cell will die, regardless of how fit it is\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":902,"value":"viewof slideDeath = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":904,"value":"pDeath = slideDeath / 200;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":404,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"md`### Simulation Functions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":137,"value":"$49","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":538,"value":"function generateGene(gene) {\n gene.value = Math.floor(Math.random() * valueCount);\n \n gene.defender = 0;\n if (Math.random() < defenderProb) gene.defender = 1;\n gene.defector = 0;\n if (Math.random() < defectorProb) gene.defector = 1;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":551,"value":"function copyGene(gene, child) {\n child.value = gene.value;\n child.defender = gene.defender;\n child.defector = gene.defector;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"//This function gets called each time-step, and updates the state of the population.\nfunction update() {\n /*\n NOTE:\n When referencing simulation variables directly, we *always* use the keyword mutable, \n otherwise the sim won't evaluate as expected.\n */\n\n if (mutable steps % speed == 0) { \n captureState(mutable cellList, mutable history, mutable steps);\n }\n\n updateGene(mutable cellList); \n \n mutable cellList.sort((a, b) => a.fitness - b.fitness);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":527,"value":"$4a","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":566,"value":"function replaceCell(cellA, cellB) {\n //Strip out all the genes from cell B from the gene list, and from cell B\n cellB.elements = [];\n cellB.id = cellA.id + ((Math.random() * 2) - 1) * 0.05;\n if (cellB.id > 1) cellB.id -= 1;\n if (cellB.id < 0) cellB.id += 1;\n\n for (let gene of cellA.elements) {\n if (Math.random() < pMutation) {\n } else {\n //If the gene is being included\n let replica = {};\n if (Math.random() < pMutation) { \n generateGene(replica);\n } else {\n copyGene(gene, replica);\n }\n cellB.elements.push(replica);\n }\n }\n refreshCellStats(cellB);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":535,"value":"$4b","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"//Function to store the outcome of a round for display in the history timeline\nfunction captureState(geneList, history, steps) {\n \n let defectorCount = 0;\n let defenderCount = 0;\n let totalGenes = 0;\n let totalFit = 0;\n \n let popSpread = new Array(height).fill(0);\n \n for (let cell of cellList) {\n for (let gene of cell.elements) {\n if (gene.defector > 0) defectorCount++;\n if (gene.defender > 0) defenderCount++;\n }\n totalGenes += cell.elements.length;\n totalFit += cell.fitness;\n let idIndex = Math.floor(cell.id * height);\n popSpread[idIndex] = 1;\n }\n \n let defectFrac = defectorCount / totalGenes;\n let defendFrac = defenderCount / totalGenes;\n let fitFrac = totalFit / cellList.length;\n \n let moment = {}\n moment.time = steps;\n moment.value = {\n defectFrac: defectFrac,\n defendFrac: defendFrac,\n fitFrac: fitFrac,\n popSpread: popSpread\n }\n history.push(moment);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"md`### Simulation 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