Computers Math

The gut is one of the most complex organs in the body. Inside, it teems with a diverse microbial population that interacts and cooperates with intestinal cells to digest food and drugs. Disruptions in this microbiome have strong links to a wide spectrum of diseases, such as inflammatory bowel disease, obesity, asthma, and even psychological and behavioral disorders.
Valid models of the gut are therefore immensely useful for understanding its function and associated ailments. In APL Bioengineering, by AIP Publishing, researchers from the University of California, Berkeley and Lawrence Berkeley National Lab described how gut-on-a-chip devices can bridge lab models and human biology.
Organ-on-a-chip devices are miniaturized models of human organs. They contain tiny microchannels where cells and tissue cultures interact with precisely controlled nutrients. Regulating the cell’s environment in such a way is crucial for creating realistic models of tissue.
Using these models avoids the time-consuming and costly challenges of clinical trials and the ethical issues behind animal testing.
“Medical research is currently facing major hurdles, both in terms of understanding the basic science governing the function of human organs and the research and development of new drugs and therapeutics,” said author Amin Valiei. “Access to valid models of human organs that can be studied conveniently in the lab can significantly accelerate scientific discoveries and the development of new medications.”
Modeling the microbiome is particularly difficult because of its unique environmental conditions. Through creative design, gut-on-a-chip devices can simulate many of these properties, such as the gut’s anaerobic atmosphere, fluid flow, and pulses of contraction/relaxation. Growing intestinal cells in this environment means that they more closely resemble human biology compared to standard laboratory cell cultures.
“Recent gut-on-a-chip models have demonstrated success in maintaining a viable coculture of the human intestinal cells and the microbiome for a few days and even up to weeks,” said Valiei. “This opens new ways to analyze the microbiome under biologically relevant conditions.”
The authors highlight key gut-on-a-chip devices and their success in simulating microbial and human cellular biology. They also describe current disease models and drug studies using the technology.
“Its unique capabilities make the organ-on-a-chip apt for plenty of research investigations in the future,” said Valiei.
The team is currently investigating dysbiosis, an imbalance in the gut microbial community with major health consequences. They aim to find innovative ways to diagnose, mitigate, and treat this condition. More

A “biocomputer” powered by human brain cells could be developed within our lifetime, according to Johns Hopkins University researchers who expect such technology to exponentially expand the capabilities of modern computing and create novel fields of study.
The team outlines their plan for “organoid intelligence” today in the journal Frontiers in Science.
“Computing and artificial intelligence have been driving the technology revolution but they are reaching a ceiling,” said Thomas Hartung, a professor of environmental health sciences at the Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering who is spearheading the work. “Biocomputing is an enormous effort of compacting computational power and increasing its efficiency to push past our current technological limits.”
For nearly two decades scientists have used tiny organoids, lab-grown tissue resembling fully grown organs, to experiment on kidneys, lungs, and other organs without resorting to human or animal testing. More recently Hartung and colleagues at Johns Hopkins have been working with brain organoids, orbs the size of a pen dot with neurons and other features that promise to sustain basic functions like learning and remembering.
“This opens up research on how the human brain works,” Hartung said. “Because you can start manipulating the system, doing things you cannot ethically do with human brains.”
Hartung began to grow and assemble brain cells into functional organoids in 2012 using cells from human skin samples reprogrammed into an embryonic stem cell-like state. Each organoid contains about 50,000 cells, about the size of a fruit fly’s nervous system. He now envisions building a futuristic computer with such brain organoids.

Computers that run on this “biological hardware” could in the next decade begin to alleviate energy-consumption demands of supercomputing that are becoming increasingly unsustainable, Hartung said. Even though computers process calculations involving numbers and data faster than humans, brains are much smarter in making complex logical decisions, like telling a dog from a cat.
“The brain is still unmatched by modern computers,” Hartung said. “Frontier, the latest supercomputer in Kentucky, is a $600 million, 6,800-square-feet installation. Only in June of last year, it exceeded for the first time the computational capacity of a single human brain — but using a million times more energy.”
It might take decades before organoid intelligence can power a system as smart as a mouse, Hartung said. But by scaling up production of brain organoids and training them with artificial intelligence, he foresees a future where biocomputers support superior computing speed, processing power, data efficiency, and storage capabilities.
“It will take decades before we achieve the goal of something comparable to any type of computer,” Hartung said. “But if we don’t start creating funding programs for this, it will be much more difficult.”
Organoid intelligence could also revolutionize drug testing research for neurodevelopmental disorders and neurodegeneration, said Lena Smirnova, a Johns Hopkins assistant professor of environmental health and engineering who co-leads the investigations.
“We want to compare brain organoids from typically developed donors versus brain organoids from donors with autism,” Smirnova said. “The tools we are developing towards biological computing are the same tools that will allow us to understand changes in neuronal networks specific for autism, without having to use animals or to access patients, so we can understand the underlying mechanisms of why patients have these cognition issues and impairments.”
To assess the ethical implications of working with organoid intelligence, a diverse consortium of scientists, bioethicists, and members of the public have been embedded within the team.
Johns Hopkins authors included: Brian S. Caffo, David H. Gracias, Qi Huang, Itzy E. Morales Pantoja, Bohao Tang, Donald J. Zack, Cynthia A. Berlinicke, J. Lomax Boyd, Timothy DHarris, Erik C. Johnson, Jeffrey Kahn, Barton L. Paulhamus, Jesse Plotkin, Alexander S. Szalay, Joshua T. Vogelstein, and Paul F. Worley.
Other authors included: Brett J. Kagan, of Cortical Labs; Alysson R. Muotri, of the University of California San Diego; and Jens C. Schwamborn of University of Luxembourg. More

A new machine learning model can predict traffic activity in different zones of cities. To do so, a Complexity Science Hub researcher used data from a main car-sharing company in Italy as a proxy for overall city traffic. Understanding how different urban zones interact can help avoid traffic jams, for example, and enable targeted responses of policy makers — such as local expansion of public transportation.
Understanding people’s mobility patterns will be central to improving urban traffic flow. “As populations grow in urban areas, this knowledge can help policymakers design and implement effective transportation policies and inclusive urban planning,” says Simone Daniotti of the Complexity Science Hub.
For example, if the model shows that there is a nontrivial connection between two zones, i.e., that people commute from one zone to another for certain reasons, services could be provided that compensate for this interaction. If, on the flip side, the model shows that there is little activity in a particular location, policymakers could use that knowledge to invest in structures to change that.
Model Also for Other Cities Like Vienna
For this study a major car-sharing company provided the data: the location of all cars in their fleet in four Italian cities (Rome, Turin, Milan, and Florence) in 2017. The data was obtained by constantly querying the service provider’s web APIs, recording the parking location of each car, as well as the start and end timestamps. “This information allows us to identify the origin and destination of each trip,” Daniotti explains.
Daniotti used that as a proxy for all city traffic and created a model that not only allows accurate spatio-temporal forecasting in different urban areas, but also accurate anomaly detection. Anomalies such as strikes and bad weather conditions, both of which are related to traffic.

The model could also make predictions about traffic patterns for other cities such as Vienna. “However, this would require appropriate data,” Daniotti points out.
Outperforming Other Models
While there are already many models designed to predict traffic behavior in cities, “the vast majority of prediction models on aggregated data are not fully interpretable. Even though some structure of the model connects two zones, they cannot be interpreted as an interaction” explains Daniotti. This limits understanding of the underlying mechanisms that govern citizens’ daily routines.
Since only a minimal number of constraints are considered and all parameters represent actual interactions, the new model is fully interpretable.
but What Is Prediction Without Interpretation?
“Of course it is important to make predictions,” Daniotti explains, “but you can make very accurate predictions, and if you don’t interpret the results correctly, you sometimes run the risk of drawing very wrong conclusions.”
Without knowing the reason why the model is showing a particular result, it is difficult to control for events where the model was not showing what you expected. “Inspecting the model and understanding it, helps us, and also policy makers, to not draw wrong conclusions,” Daniotti points out. More