What is life?
When Erwin Schrödinger posed this question in 1944, in a book of the same name, he was 57 years old. He had won the Nobel in Physics eleven years earlier, and was arguably past his glory days. Indeed, at that time he was working mostly on his ill-fated “Unitary Field Theory.” By all accounts, the publication of “What is Life?”—venturing far outside of a theoretical physicist’s field of expertise—raised many eyebrows. How presumptuous for a physicist to take on one of the deepest questions in biology! But Schrödinger argued that science should not be compartmentalized:
“Some of us should venture to embark on a synthesis of facts and theories, albeit with second-hand and incomplete knowledge of some of them—and at the risk of making fools of ourselves.”
Schrödinger’s “What is Life” has been extraordinarily influential, in one part because he was one of the first who dared to ask the question seriously, and in another because it was the book that was read by a good number of physicists—famously both Francis Crick and James Watson independently, but also many a member of the “Phage group,” a group of scientists that started the field of bacterial genetics—and steered them to new careers in biology. The book is perhaps less famous for the answers Schrödinger suggested, as almost all of them have turned out to be wrong.
Highlights, Quotes, & Marginalia
our existence can succinctly be described as “information that can replicate itself,” the immediate follow-up question is, “Where did this information come from?”
from an information perspective, only the first step in life is difficult. The rest is just a matter of time.
Through decades of work by legions of scientists, we now know that the process of Darwinian evolution tends to lead to an increase in the information coded in genes. That this must happen on average is not difficult to see. Imagine I start out with a genome encoding n bits of information. In an evolutionary process, mutations occur on the many representatives of this information in a population. The mutations can change the amount of information, or they can leave the information unchanged. If the information changes, it can increase or decrease. But very different fates befall those two different changes. The mutation that caused a decrease in information will generally lead to lower fitness, as the information stored in our genes is used to build the organism and survive. If you know less than your competitors about how to do this, you are unlikely to thrive as well as they do. If, on the other hand, you mutate towards more information—meaning better prediction—you are likely to use that information to have an edge in survival.
There are some plants with huge amounts of DNA compared to their “peers”–perhaps these would be interesting test cases for potential experimentation of this?
DNA as a data storage medium has several advantages, including far greater data density compared to electronic media. We propose that schemes for data storage in the DNA of living organisms may benefit from studying the reconstruction problem, which is applicable whenever multiple reads of noisy data are available. This strategy is uniquely suited to the medium, which inherently replicates stored data in multiple distinct ways, caused by mutations. We consider noise introduced solely by uniform tandem-duplication, and utilize the relation to constant-weight integer codes in the Manhattan metric. By bounding the intersection of the cross-polytope with hyperplanes, we prove the existence of reconstruction codes with greater capacity than known error-correcting codes, which we can determine analytically for any set of parameters.
Lane lays out a “brief” history of the 4 billion years of life on Earth. Discusses isotopic fractionation and other evidence that essentially shows a bottleneck between bacteria and archaea (procaryotes) on the one hand and eucaryotes on the other, the latter of which all must have had a single common ancestor based on the genetic profiles we currently see. He suggest that while we should see even more diversity of complex life, we do not, and he hints at the end of the chapter that the reason is energy.
In general, it’s much easier to follow than I anticipated it might be. His writing style is lucid and fluid and he has some lovely prose not often seen in books of this sort. It’s quite a pleasure to read. Additionally he’s doing a very solid job of building an argument in small steps.
I’m watching closely how he’s repeatedly using the word information in his descriptions, and it seems to be a much more universal and colloquial version than the more technical version, but something interesting may come out of it from my philosophical leanings. I can’t wait to get further into the book to see how things develop.
This last section got pretty heavy into evolution and touched on ideas of information theory applied to biology and complexity, but didn’t actually mention them. Surprisingly he mentioned Jeremy England by name! He nibbled around the edges of the field to tie up the plot, but there’s some reasonable philosophical questions hiding here in the end of the book that I’ll have to pull into a more lengthy review.
Recent studies of active matter have stimulated interest in the driven self-assembly of complex structures. Phenomenological modeling of particular examples has yielded insight, but general thermodynamic principles unifying the rich diversity of behaviors observed have been elusive. Here, we study the stochastic search of a toy chemical space by a collection of reacting Brownian particles subject to periodic forcing. We observe the emergence of an adaptive resonance in the system matched to the drive frequency, and show that the increased work absorption by these resonant structures is key to their stabilization. Our findings are consistent with a recently proposed thermodynamic mechanism for far-from-equilibrium self-organization.
A qualitatively more diverse range of possible behaviors emerge in many-particle systems once external drives are allowed to push the system far from equilibrium; nonetheless, general thermodynamic principles governing nonequilibrium pattern formation and self-assembly have remained elusive, despite intense interest from researchers across disciplines. Here, we use the example of a randomly wired driven chemical reaction network to identify a key thermodynamic feature of a complex, driven system that characterizes the “specialness” of its dynamical attractor behavior. We show that the network’s fixed points are biased toward the extremization of external forcing, causing them to become kinetically stabilized in rare corners of chemical space that are either atypically weakly or strongly coupled to external environmental drives.
A chemical mixture that continually absorbs work from its environment may exhibit steady-state chemical concentrations that deviate from their equilibrium values. Such behavior is particularly interesting in a scenario where the environmental work sources are relatively difficult to access, so that only the proper orchestration of many distinct catalytic actors can power the dissipative flux required to maintain a stable, far-from-equilibrium steady state. In this article, we study the dynamics of an in silico chemical network with random connectivity in an environment that makes strong thermodynamic forcing available only to rare combinations of chemical concentrations. We find that the long-time dynamics of such systems are biased toward states that exhibit a fine-tuned extremization of environmental forcing.
Take chemistry, add energy, get life. The first tests of Jeremy England’s provocative origin-of-life hypothesis are in, and they appear to show how order can arise from nothing.
Interesting article with some great references I’ll need to delve into and read.
The situation changed in the late 1990s, when the physicists Gavin Crooks and Chris Jarzynski derived “fluctuation theorems” that can be used to quantify how much more often certain physical processes happen than reverse processes. These theorems allow researchers to study how systems evolve — even far from equilibrium.
I want to take a look at these papers as well as several about which the article is directly about.
Any claims that it has to do with biology or the origins of life, he added, are “pure and shameless speculations.”
Some truly harsh words from his former supervisor? Wow!
maybe there’s more that you can get for free
Most of what’s here in this article (and likely in the underlying papers) sounds to me to have been heavily influenced by the writings of W. Loewenstein and S. Kauffman. They’ve laid out some models/ideas that need more rigorous testing and work, and this seems like a reasonable start to the process. The “get for free” phrase itself is very S. Kauffman in my mind. I’m curious how many times it appears in his work?
Syndicated copies to:
Apparently there’s a beta Bioinformatics group on Stack Exchange now. It’s just come out of alpha in the last few days and it appears anyone can join now.
Dr. Walker introduces the concept of information, then proposes that information may be a necessity for biological complexity in this thought-provoking talk on the origins of life.
Sara is a theoretical physicist and astrobiologist, researching the origins and nature of life. She is particularly interested in addressing the question of whether or not “other laws of physics” might govern life, as first posed by Erwin Schrodinger in his famous book What is life?. She is currently an Assistant Professor in the School of Earth and Space Exploration and Beyond Center for Fundamental Concepts in Science at Arizona State University. She is also Fellow of the ASU -Santa Fe Institute Center for Biosocial Complex Systems, Founder of the astrobiology-themed social website SAGANet.org, and is a member of the Board of Directors of Blue Marble Space. She is active in public engagement in science, with recent appearances on “Through the Wormhole” and NPR’s Science Friday.
Admittedly, she only had a few short minutes, but it would have been nice if she’d started out with a precise definition of information. I suspect the majority of her audience didn’t know the definition with which she’s working and it would have helped focus the talk.
Her description of Speigelman’s Monster was relatively interesting and not very often seen in much of the literature that covers these areas.
I wouldn’t rate this very highly as a TED Talk as it wasn’t as condensed and simplistic as most, nor was it as hyper-focused, but then again condensing this area into 11 minutes is far from simple task. I do love that she’s excited enough about the topic that she almost sounds a little out of breath towards the end.
There’s an excellent Eddington quote I’ve mentioned before that would have been apropos to have opened up her presentation that might have brought things into higher relief given her talk title:
This article provides answers to the two questions posed in the title. It is argued that, contrary to many statements made in the literature, neither entropy, nor the Second Law may be used for the entire universe. The origin of this misuse of entropy and the second law may be traced back to Clausius himself. More resent (erroneous) justification is also discussed.
Life is so remarkable, and so unlike any other physical system, that it is tempting to attribute special factors to it. Physics is founded on the assumption that universal laws and principles underlie all natural phenomena, but is it far from clear that there are 'laws of life' with serious descriptive or predictive power analogous to the laws of physics. Nor is there (yet) a 'theoretical biology' in the same sense as theoretical physics. Part of the obstacle in developing a universal theory of biological organization concerns the daunting complexity of living organisms. However, many attempts have been made to glimpse simplicity lurking within this complexity, and to capture this simplicity mathematically. In this paper we review a promising new line of inquiry to bring coherence and order to the realm of biology by focusing on 'information' as a unifying concept.
One of the most important use cases of ontologies is the calculation of similarity scores between a query and items annotated with classes of an ontology. The hierarchical structure of an ontology does not necessarily reflect all relevant aspects of the domain it is modelling, and this can reduce the performance of ontology-based search algorithms. For instance, the classes of phenotype ontologies may be arranged according to anatomical criteria, but individual phenotypic features may affect anatomic entities in opposite ways. Thus, "opposite" classes may be located in close proximity in an ontology; for example enlarged liver and small liver are grouped under abnormal liver size. Using standard similarity measures, these would be scored as being similar, despite in fact being opposites. In this paper, we use information about opposite ontology classes to extend two large phenotype ontologies, the human and the mammalian phenotype ontology. We also show that this information can be used to improve rankings based on similarity measures that incorporate this information. In particular, cosine similarity based measures show large improvements. We hypothesize this is due to the natural embedding of opposite phenotypes in vector space. We support the idea that the expressivity of semantic web technologies should be explored more extensively in biomedical ontologies and that similarity measures should be extended to incorporate more than the pure graph structure defined by the subclass or part-of relationships of the underlying ontologies.
Pachter, a computational biologist, returns to CalTech to study the role and function of RNA.
Pachter, a computational biologist and Caltech alumnus, returns to the Institute to study the role and function of RNA.
Lior Pachter (BS ’94) is Caltech’s new Bren Professor of Computational Biology. Recently, he was elected a fellow of the International Society for Computational Biology, one of the highest honors in the field. We sat down with him to discuss the emerging field of applying computational methods to biology problems, the transition from mathematics to biology, and his return to Pasadena.Continue reading “👓 A Conversation with @LPachter (BS ’94) | Caltech”