An artificial intelligence system has for the first time reverse-engineered the regeneration mechanism of planaria—the small worms whose extraordinary power to regrow body parts has made them a research model in human regenerative medicine.
The discovery by Tufts University biologists presents the first model of regeneration discovered by a non-human intelligence and the first comprehensive model of planarian regeneration, which had eluded human scientists for over 100 years. The work, published in the June 4, 2015, issue of PLOS Computational Biology, demonstrates how “robot science” can help human scientists in the future.
In order to bioengineer complex organs, scientists need to understand the mechanisms by which those shapes are normally produced by the living organism. However, a significant knowledge gap persists between molecular genetic components identified as being necessary to produce a particular organism shape and understanding how and why that particular complex shape is generated in the correct size, shape and orientation, said the paper’s senior author, Michael Levin, Ph.D., Vannevar Bush professor of biology and director of the Tufts Center for Regenerative and Developmental Biology.
“Most regenerative models today derived from genetic experiments are arrow diagrams, showing which gene regulates which other gene. That’s fine, but it doesn’t tell you what the ultimate shape will be. You cannot tell if the outcome of many genetic pathway models will look like a tree, an octopus or a human,” said Levin. “Most models show some necessary components for the process to happen, but not what dynamics are sufficient to produce the shape, step by step. What we need are algorithmic or constructive models, which you could follow precisely and there would be no mystery or uncertainty. You follow the recipe and out comes the shape.”
Such models are required in order to know what triggers could be applied to such a system to cause regeneration of particular components, or other desired changes in shape. However, no such tools yet exist for mining the fast-growing mountain of published experimental data in regeneration and developmental biology, said the paper’s first author, Daniel Lobo, Ph.D., post-doctoral fellow in the Levin lab.
To address this challenge, Lobo and Levin developed an algorithm that would use evolutionary computation to produce regulatory networks able to “evolve” to accurately predict the results of published laboratory experiments that the researchers entered into a database.
“Our goal was to identify a regulatory network that could be executed in every cell in a virtual worm so that the head-tail patterning outcomes of simulated experiments would match the published data,” Lobo said.
Tufts biologists devloped an algorithm that used evolutionary computation to produce regulatory networks able to “evolve” to accurately predict the results of published research on planarian regeneration.
As expected, the initial random regulatory networks usually could not produce any of the experimental results. New candidate networks were generated by randomly combining previous networks and performing random changes, additions and deletions. Each candidate network was tested in a virtual worm, under simulated experiments. The algorithm compared the resulting shape from the simulation with real published data in the database. As evolution proceeded, gradually the new networks could explain more experiments in the database comprising most of the known planarian experimental literature regarding head vs. tail regeneration.
First Regenerative Model Discovered by Artificial Intelligence
The researchers ultimately applied the algorithm to a combined experimental dataset of 16 key planarian regeneration experiments to determine if the approach could identify a comprehensive regulatory network of planarian generation. After 42 hours, the algorithm returned the discovered regulatory network, which correctly predicted all 16 experiments in the dataset. The network comprised seven known regulatory molecules as well as two proteins that had not yet been identified in existing papers on planarian regeneration.
“This represents the most comprehensive model of planarian regeneration found to date. It is the only known model that mechanistically explains head-tail polarity determination in planaria under many different functional experiments and is the first regenerative model discovered by artificial intelligence,” said Levin.
Lobo and Levin are both trained in computer science and bring an unusual perspective to the field of developmental biology. Levin majored in computer science and biology at Tufts before earning his Ph.D. in genetics. Lobo earned a Ph.D. in the field before joining the Levin lab.
The paper represents a successful application of the growing field of “robot science” – which Levin says can help human researchers by doing much more than crunch enormous datasets quickly.
The Latest on: Robot science
via Google News
The Latest on: Robot science
- Sex Robots and Vegan Meat by Jenny Kleeman, review: is science out of moral control?on July 12, 2020 at 1:03 am
Had he gone on to probe the technological possibilities afforded by sex and death, his visionary essay would not only have become a bible of Silicon Valley, but a handy proposal for Jenny Kleeman’s ...
- This MIT robot combats COVID-19 and may soon be in your grocery storeon July 11, 2020 at 8:22 am
A robot that neutralizes aerosolized forms of the coronavirus could soon be coming to a supermarket near you. MIT’s Computer Science and Artificial Intelligence Laboratory team partnered with Ava ...
- Can robots, drones and data make our cities smarter?on July 10, 2020 at 4:48 pm
Will the "anthropause" brought on by lockdowns make our cities greener, cleaner and quieter in future?
- DC Computer Science Teacher Receives $50K Award From Amazonon July 10, 2020 at 1:41 pm
Lynn Garnaat of KIPP DC College Prep High School was named a 2020 Amazon Future Engineer Teacher of the Year Award recipient.
- In a time of social distancing, robots could be just what the doctor orderedon July 9, 2020 at 4:31 pm
Robots' roles at work and home have grown since the novel coronavirus spread around the world. They rove. They disinfect. They make drinks. They even bust a few dance moves.
- This Robot Did 700 Chemistry Experiments in Just 8 Dayson July 9, 2020 at 3:50 pm
A new autonomous chemistry robot works on complex experiments without management. Powerful computers can narrow down huge pools of candidates and parameters, in this case finding an exciting new ...
- The appliance of science: professor creates lab robot that never stopson July 8, 2020 at 4:01 pm
A team of scientists has designed an autonomous machine that can do the job of . . . a scientist.The robotic researcher, KUKA-1, decides which experiments it will carry out then performs them in a ...
- No masks, no coughs: Robots can be just what the doctor ordered in time of social distancingon July 8, 2020 at 2:39 pm
Since the virus arrived, robots have offered their services as bartenders, security guards and deliverymen. But they don’t necessarily need to supplant humans, researchers say. They can also bridge ...
- Autonomous Robot Scientist Does in Days What It Would Take a Human Months to Completeon July 8, 2020 at 8:00 am
"The aim is to have the ability for scientists to do much more ambitious and interesting chemistry. You still need the scientist," a professor involved in the years-long AI project told Newsweek.
- Business Under COVID-19: Robots May Be Used To Jumpstart Different Economieson July 7, 2020 at 9:48 am
Robot sanitizers can cover larger areas in shorter times, the experiment revealed Robots are also being looked at by companies to replace workers in the face of COVID-19 Companies are trying out new ...
via Bing News