Chris Moriarty - Spin Control

“So anyway. The virus. This is still just in the region of wild surmise. But I think it’s an evolutionary search algorithm. Fontana, the human who thought up the Turing Soup idea, spent his whole life working on the relationship between genetic robustness and evolvability. In other words, if species need to change rapidly to respond to changes in their environment, then why are most evolutionarily successful organisms so genetically resistant to change? What’s the adaptive value of all the epistatic effects and redundancy that UN-based commercial splicers are always deleting from their genomes and we’re always trying to preserve? Fontana’s big idea was something he called neutral networks. I remember neutral networks from first-year genetic engineering. It’s central to understanding how genotype space maps onto phenotype space: how DNA turns the biological equivalent of a computer program into an actual living organism. It’s also why designers always run into those ‘you can’t get there from here’ design problems. You know: the changes that look like minor tweaks but turn out to involve so many splices that all have so many unintended side effects that you can’t make the ‘tweak’ without stripping the whole geneset down to its bolts and starting over again.”

Arkady nodded. This was a familiar problem for Syndicate design teams, and a major reason for the slow, cautious, incremental changes within genelines. The pre-Breakaway corporate genetic engineers had been far bolder; but they’d assumed cull rates for their company-owned constructs that even MotaiSyndicate would have found ethically indefensible.

“So that’s one of the first lessons in genetic engineering: Just because two organisms ‘look’ the same doesn’t mean their genesets look the same. It just means that their DNA inhabits the same neutral network. And one of the basic truisms of evolutionary genetics is that the most successful species usually have the largest neutral networks. Fontana theorized that this was because neutral networks were nature’s way of minimizing the chances of a you-can’t-get-there-from-here problem. He explained how neutral networks do that by talking about maps of old Europe on Earth. Which makes sense, I guess, since neutral networks are all about the importance of boundaries and territory. Imagine you’re walking around some country. France, let’s say. But you want to get to Germany. Well, if you’re walking around somewhere in the middle of France, it’s going to take a lot of steps to get to Germany…steps that, in the mutational context, are all fraught with an overwhelming risk of producing nonviable phenotypes. But if you happen to be exactly on the border between the two countries, all you need to do is take one easy step and presto, you’re in Germany. And the bigger your country is, the longer your borders are, and the more places you can get to in just one step. Fontana called those border crossings—single mutations that shift an organism into a new phenotype—gateway mutations. The bigger the neutral network, the more gateway mutations. The more gateway mutations, the less risk of a you-can’t-get-there-from-here problem…known in the real world as an extinction event.”

“So how does Turing Soup fit into that?” Arkady asked.

“Well, I’m running into the limits of my understanding of Algorithmic Chemistry here, but Fontana envisioned neutral networks as search spaces and mutation as a search algorithm just like the search algorithms you’d use to find information in a database. The bigger the database, the more data there is to mine, and the more data you can get. That’s the expanding neutral networks side of the equation. But there’s another limiting factor as well: How good is your algorithm at searching the database? The better the search algorithm, the faster it sifts the kernels of relevant data from the chaff. So Fontana looked at mutations accumulating inside neutral networks as a mechanism through which organisms ‘search’ the entire space of the current phenotype for possible improvements or responses to environmental alterations. Now I get a little bit itchy at the idea that organisms ‘search’ their genotype in any meaningful way. I just don’t think evolution works that way. But on the other hand, genetic engineers spend a lot of time improving their neutral network search algorithms. And if you could engineer organisms to search their neutral networks more efficiently, you could turn walking ghosts into viable populations…which is exactly what I think someone’s done on Novalis.”

Arkady sank onto his own stool, floored by the magnitude of what Arkasha was describing. “I’m not even going to touch the developmental biology problems with that idea—”

“I know, I know.”

“—but how would you even begin to prove someone had done it on Novalis?”

“I can’t. Not in any time frame that’s going to make a difference to this mission. But I can say that Bella’s virus looks a hell of a lot more like a terraforming tool than a bioweapon.”

Arkady bit his lip.

“What?”

“Well…I was just thinking about that old saying about a weed being a perfectly good plant in the wrong place. Isn’t a bioweapon just a perfectly good terraforming tool in the wrong place?”

“So you are saying you agree with Ahmed.”

“No! I’m just pointing out that you’d better have an answer to that question, because there’s no way he won’t ask it.”

“I’ll have an answer,” Arkasha said. “One way or another I’ll have an answer.”

“Well, don’t push yourself too hard…okay?”

“I promise I’ll be sensible. And thanks for…well, taking care of me.”

“Anyone would have done the same.”

“That’s the kind of thing you always say. It doesn’t make it true.”

Arkasha’s eyes glittered. He really did look feverish, Arkady told himself for the second time in as many minutes.

He pressed his lips to Arkasha’s forehead in a kiss so sweet and chaste that they might have been two brother monks on one of the Russian icons their geneline’s features were said to be modeled on.

Of course he hadn’t meant it to be a kiss at all; he’d been checking Arkasha’s temperature, just as he’d done countless times over the course of the sickness. But somehow it hadn’t ended up that way.

He stood there, one hand still on Arkasha’s shoulder, feeling like an army that had outrun its supply lines. Arkasha was utterly still under his hand, his eyes wide and dark, his face oddly expressionless. But Arkady could feel the heat of his skin through the thin shirt, and the bones of his shoulder beneath their too-thin veil of muscle and tendon.

“I just—” Arkady began. And then he stopped, because Arkasha had begun to speak in the same instant.

“What?” they both said—and then laughed nervously.

Arkasha raised a hand, then let it fall without completing the gesture. “I should get back to work,” he said.

“Why?” Arkady asked. He slid his hand up to the nape of Arkasha’s neck, certain that Arkasha would shy away from the touch but unable to stop himself. “Why won’t you just let yourself be happy?”

Arkasha blinked like a man stepping out of a shuttered room into bright sunlight. “I’ve made a lot of trouble for myself,” he whispered. “You wouldn’t be doing yourself any favors.”

“I don’t care.”

“You should care,” Arkasha said. But he let himself be drawn closer.

“I don’t care,” Arkady repeated.

Then, still half-convinced he would be pushed away, he took Arkasha in his arms and kissed him again…this time not at all chastely.

“I love your cowlicks,” Arkasha said. “I’m endlessly, abjectly grateful to whatever poor slob is responsible for them. If he hadn’t fallen asleep on the job, you might be a completely different person. You might never have fallen in love with your silly little ants. And I might never have met you, let alone fallen in love with you.”