We’ve talked a lot about roundworms here recently, but there is another phylum of worms that is not round but flat. The only flatworms most of us know are the planaria we cut up in biology class, watching them grow two “heads” or other grotesque forms. Actually, these members of the phylum Platyhelminthes are complex organisms, important for the study of parasitism, evolution, and especially regeneration. Littlewood and Waeschenbach provide an overview in Current Biology:

Linnaeus had little time for worms, pooling them with other invertebrates into the group ‘Vermes’. Beguilingly simple, vermiform (worm-like) organisms include some of the most important species ecologically, evolutionarily, economically and biomedically. On closer examination, few vermiform creatures are truly simple, and many have provided a rich source of evolutionary novelties sparking major radiations. The flatworms are particularly notable (Figure 1). Dorsoventrally flattened with no body cavity other than a gut (acoelomate), flatworms have long been considered simple in body plan and suitable as a starting point in devising other more complex animal forms. We know that other ‘flat worms’ such as acoels and xenoturbellids have pivotal, but still controversial, roles in disentangling the tree of life depending on their true position. Originally placed deep at, or towards, the base of all bilaterally symmetrical animals, these flattened worms are now considered more closely related to echinoderms and vertebrates. True flatworms (phylum Platyhelminthes) are nested securely, but not clearly amongst the Lophotrochozoa, a major grouping amongst the invertebrates. The disparate positions of flatworms, acoels and xenoturbellids on the metazoan tree throws up some weird and wonderful conundra, such as the origins and evolution of the anus, the blastopore and the mouth. [Emphasis added.]

The article goes on to puzzle over their place in Darwin’s tree (what else is new?), but our focus will turn to another flatworm story from another source. It’s a paper that claims to get something from nothing — a complete model of how flatworm regeneration works. Daniel Lobo and Michael Levin of Tufts University mimicked mutation/selection processes on a computer and out popped a solution to a complex problem. Here’s another chance to pit intelligent design against undirected processes.

Their paper in PLOS Computational Biology is called, “Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration.” Its tie-in to Darwinian processes is summarized by Live Science, which celebrates the paper’s achievement: “An artificial intelligence (AI) system has solved a puzzle that has eluded scientists for more than 100 years: how a tiny, freshwater flatworm regenerates its body parts.”

Cue the Darwinese:

The system was programmed by Daniel Lobo, a post-doctoral researcher at Tufts and co-author of the study. It works by taking models that attempt to explain how regeneration occurs and subjecting them to a process of “natural selection.”

Essentially, potential models are run through a virtual simulator that mimics various experiments on planaria; then, the results are compared with the outcomes of published experiments in which planaria have been cut into pieces and sometimes manipulated with drugs or by having genes knocked out before regenerating into full organisms.

In each cycle, the potential models that best fit the results are “bred” with each other to create new models and less accurate ones are discarded. This process is repeated until the models “evolve” into one that fits the data perfectly.

Using this process, the AI system was able to produce a model that correctly predicted all 16 experiments included in the data set within just 42 hours, the researchers said. In addition, the model anticipated the results of a series of novel experiments carried out by the researchers to test its predictive power.

Readers are told that the method was “inspired by the principles of evolution.”

We hardly need to say anything in response, because our readers have undoubtedly already found the trick. Information was snuck into the model at several points: the design of the program and the computer it runs on, the selection of a target, methods for testing outcomes, and rejection of any that don’t meet the pre-established criteria. This is not “natural selection” at all; it’s artificial selection, intelligently guided like Richard Dawkins’s old weasel blunder.

Maybe that’s why Lobo and Levin don’t push the Darwinian evolution angle quite as hard in the actual paper. They talk about using an “evolutionary algorithm,” for sure, and using random mutations:

The inferring method iteratively produces regulatory networks that better predict the experiments in the dataset. Fig 6 shows a selection of candidate regulatory networks generated during the search of the model in Fig 3C (see S1 File for the system of equations for each regulatory network). The initial random regulatory networks (generation 0) usually cannot reproduce any of the resultant phenotypes in the dataset, neither maintain the wild type morphology pattern. New candidate regulatory networks are generated by randomly combining previous networks and performing random changes, additions, and deletions, including nodes representing knocked-down genes in the experiments or unknown nodes found de novo. Incrementally, the new candidate networks can explain a higher number of experiments, and the final regulatory network can correctly explain all the experiments in the dataset.

But clearly they started with information-rich network models, and then used their own intelligence to guide the mutations to the target. It’s not fundamentally different from the farmer who breeds the individuals he desires out of variations that arise naturally. Lobo and Levin have their intelligence-guided hands all over this program:

New regulatory networks are produced from existing ones through crossover and mutation operators. A crossover mixes randomly two networks to produce two new networks. Products that are in common between the two networks are copied to the new networks randomly, each network receiving one of each product, while products not shared are distributed randomly between the two new networks. Products are copied to a new regulatory network together with their regulatory links. If the regulatory product of a copied link does not exist in the new network, it is substituted randomly by another regulatory product.

They’re using the word “randomly” a lot, but it’s guided randomness. The computer models are allowed to vary, but the programmers are steering everything to the target. In fact, they continually insert their hands into the works to make corrections, because they know what would happen if they didn’t.

Mutations alter the regulatory network randomly…. These evolutionary parameters are not optimized; however, a higher probability of deletion with respect duplication is necessary to bias the evolution towards simpler networks and prevent bloating.

The evolutionary algorithm stops when a network with zero error is found and the complexity (number of products and links) of the simpler network with zero error have not been decreased for a certain number of generations. This extra evolutionary time is used to simplify the best network found, since the mutation operators are biased towards simpler networks.

So, sorry — no score for Darwin. But can’t they claim that the innovation did come from random mutations, because they didn’t know the form of the final model till chance produced it? Isn’t that an example of information generated de novo by unguided processes? Not really. They knew their target, and they designed the computer program to reach it. The resulting model was a member of a subset of all possible models that meet the criteria. It may not have been the only model that worked, but when it hit the jackpot, it satisfied the design criteria and was artificially selected.

Lobo and Levin never mention Darwin or “natural selection” in their paper. Their “evolutionary algorithm” came front-loaded with sufficient intelligent design to solve the problem. It is “evolutionary” only in the sense that it unfolds as the design criteria are met via “mutation” and artificial selection. Darwinian evolution cannot see a target and select its mutations accordingly.

So as they celebrate their achievement, look for the intelligent design implicit in their version of “evolution”:

Our approach is broadly applicable to any model system whose experimental procedures and anatomical outcomes can be formalized and can readily be extended to other problems in morphogenesis, including embryonic development or the programmed self-assembly of hybrid systems such as bioinspired robots. The models discovered with this method allow the identification of the key mechanisms and the major regulatory products, including those directly perturbed during the experiments as well as-yet unidentified necessary products, explaining the resultant experimental phenotypes. Such models are required for the identification of intervention strategies to produce desired changes in large-scale shape, for birth defects, regenerative medicine, or synthetic bioengineering research. Our method represents a proof of principle towards the use of evolutionary search and quantitative spatial simulation to help constructively understand complex morphological outcomes in embryogenesis, regeneration, and synthetic bioengineering.

You may recall that William Dembski proved from the “No Free Lunch Theorems” that no evolutionary search is superior to blind search — unless information is provided by intelligence.

Speaking of robots, engineers at the Norwegian University of Science and Technology (NTNU) claim to have created a robot that “learns everything from scratch,” like a child. “We’ve given it almost no pre-defined knowledge on purpose,” one of the engineers claims. Is this an example of information arising from nothing? Or has the information been inserted into the system by intelligence? We leave this one as an exercise.

Image by Richard Ling (Flickr) [CC BY-SA 2.0], via Wikimedia Commons.