Life is Emerging: Review of Siddhartha Mukherjee’s Song of the Cell
The DNA double helix is often the image spiraling at the center of 21st century advances in biomedicine and the growing bioeconomy. And yet, DNA is molecularly inert. DNA, the code for genes, is not alive and is not strictly necessary for life. Ought life be at the center of our communication of living systems? Is not the Cell a superior symbol of life and our manipulation of living systems?
A code for life isn’t a code without the life that instantiates it. A code for life must be translated. The cell is the basic unit of that translation. The cell is the minimal viable package of life as we know it. Therefore, cell biology is at the center of biomedicine’s greatest transformations, suggests Pulitzer-winning physician-scientist Siddhartha Mukherjee in his latest book, The Song of the Cell: The Exploration of Medicine and the New Human.
The Song of the Cell begins with the discovery of cells and of germ theory, featuring characters such as Louis Pasteur and Robert Koch, who brought the cell “into intimate contact with pathology and medicine.” This intercourse would transform biomedicine, leading to the insight that we can treat disease by thinking at the cellular level. The slightest rearrangement of sick cells might be the path toward alleviating suffering for the organism: eroding the cell walls of a bacterium while sparing our human cells; inventing a medium that coaxes sperm and egg to dance into cellular union for in vitro fertilization (IVF); designing molecular missiles that home to the receptors decorating the exterior of cancer cells; teaching adult skin cells to remember their embryonic state for regenerative medicines.
Mukherjee uses the bulk of the book to elucidate key cell types in the human body, along with their “connective relationships” that enable key organs and organ systems to function. This includes the immune system, the heart, the brain, and so on. Mukherjee’s distinctive style features compelling anecdotes and human stories that animate the scientific (and unscientific) processes that have led to our current state of understanding. In his chapter on neurons and the brain, for example, he integrates Santiago Ramon y Cajal’s meticulous black ink sketches of neurons into Mukherjee’s own personal encounter with clinical depression. In one lucid section, he interviews Dr. Helen Mayberg, a pioneering neurologist who takes seriously the descriptive power of her patients’ metaphors, as they suffer from “caves,” “holes,” “voids,” and “force fields” that render their lives gray. Dr. Mayberg aims to stimulate patients’ neuronal cells in a manner that brings back the color.
Beyond exposing the insight and inventiveness that has arisen out of cell-based thinking, it seems that Mukherjee’s bigger project is an epistemological one. The early chapters of The Song of the Cell continually hint at the potential for redefining the basic unit of biology as the cell rather than the gene. The choice to center biomedicine around cells is, above all, a conspicuous choice not to center it around genes (the subject of Mukherjee’s previous book, The Gene), because genes dominate popular science communication.
This choice of cells over genes is most welcome. Cells are alive. Genes are not. Letters—such as the As, Cs, Gs, and Ts that represent the nucleotides of DNA, which make up our genes—must be synthesized into a word or poem or song that offers a glimpse into deeper truths. A key idea embedded in this thinking is that of emergence. Whether in ancient myth or modern art, creation tends to be an emergent process, not a linearly coded script. The cell is our current best guess for the basic unit of life’s emergence, turning a finite set of chemical building blocks—nucleic acids, proteins, sugars, fats—into a replicative, evolving system for fighting stasis and entropy. The cell’s song is one for our times, for it is the song of biology’s emergence out of chemistry and physics, into the “frenetically active process” of homeostasis.
Re-centering our view of biology has practical consequences, too, for how we think about diagnosing and treating disease, and for inventing new medicines. Centering cells presents a challenge: which type of cell to place at the center? Rather than default to the apparent simplicity of DNA as a symbol because it represents the one master code for life, the tension in defining the diversity of cells—a mapping process still far from complete in cutting-edge biology laboratories—can help to create a more thoughtful library of cellular metaphors to shape both the practice and communication of biology.
Further, effective problem solving is often about operating at the right level, or the right scale. The cell feels like appropriate level at which to interrogate many of the diseases that ail us, because the senses that guide our own perceptions of sickness and health—the smoldering pain of inflammation, the tunnel vision of a migraine, the dizziness of a fluttering heart—are emergent.
This, unfortunately, is sort of where Mukherjee leaves the reader, under-exploring the consequences of a biology of emergence. Many practical and profound questions have to do with the ways that each scale of life feeds back on the others. In a tome on Cells and “the future human” I wished that Mukherjee had created more space for seeking the ways that cells will shape and be shaped by the future, of humanity and otherwise.
We are entering a phase of real-world bioengineering that features the modularization of cellular parts within cells, of cells within organs, of organs within bodies, and of bodies within ecosystems. In this reality, we would be unwise to assume that any whole is the mere sum of its parts.
For example, when discussing the regenerative power of pluripotent stem cells, Mukherjee raises the philosophical thought experiment of the Delphic boat, also known as the Ship of Theseus. The boat is made of many pieces of wood, each of which is replaced for repairs over the years, with the boat’s structure unchanged. Eventually none of the boat’s original wood remains: Is it the same boat?
Mukherjee raises the Delphic boat in one paragraph at the end of the chapter on stem cells, as a metaphor related to the possibility of stem cell-enabled regeneration in perpetuity. He does not follow any of the threads of potential answers. Given the current state of cellular engineering, about which Mukherjee is a world expert from his work as a physician-scientist, this book could have used an entire section dedicated to probing this question and, importantly, the ways this thought experiment falls apart.
We are entering a phase of real-world bioengineering that features the modularization of cellular parts within cells, of cells within organs, of organs within bodies, and of bodies within ecosystems. In this reality, we would be unwise to assume that any whole is the mere sum of its parts. Wholeness at any one of these scales of life—organelle, cell, organ, body, ecosystem—is what is at stake if we allow biological reductionism to assume away the relation between those scales.
In other words, Mukherjee succeeds in providing a masterful and compelling narrative of the lives of many of the cells that emerge to enliven us. Like his previous books, it is a worthwhile read for anyone curious about the role of cells in disease and in health. And yet, he fails to offer the broader context of The Song of the Cell.
As leading agronomist and essayist Wes Jackson has written, “The sequence of amino acids that is at home in the human cell, when produced inside the bacterial cell, does not fold quite right. Something about the E. coli internal environment affects the tertiary structure of the protein and makes it inactive. The whole in this case, the E. coli cell, affects the part—the newly made protein. Where is the priority of part now?” [1]
Beyond the ways that different kingdoms of life translate the same genetic code, the practical situation for humanity today relates to the ways that the different disciplines of modern life use values and culture to influence our genes, cells, bodies, and environment. It may be that humans will soon become a bit like the Delphic boat, infused with the buzz of fresh cells to repopulate different niches within our bodies, for healthier, longer lives. But in biology, as in writing, a mixed metaphor can cause something of a cacophony. For we are not boats with parts to be replaced piecemeal. And nor are whales, nor alpine forests, nor topsoil. Life isn’t a sum of parts, and neither is a song that rings true.
[1] Wes Jackson, "Visions and Assumptions," in Nature as Measure (p. 52-53).
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.