👓 Disconnected, fragmented, or united? a trans-disciplinary review of network science | Applied Network Science | César A. Hidalgo

Read Disconnected, fragmented, or united? a trans-disciplinary review of network science by César A. HidalgoCésar A. Hidalgo (Applied Network Science | SpringerLink)
During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.

Highlights, Quotes, Annotations, & Marginalia

Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms.  

August 25, 2018 at 10:18PM

Science and Complexity (Weaver 1948); explained the three eras that according to him defined the history of science. These were the era of simplicity, disorganized complexity, and organized complexity. In the eyes of Weaver what separated these three eras was the development of mathematical tools allowing scholars to describe systems of increasing complexity.  

August 25, 2018 at 10:19PM

Problems of disorganized complexity are problems that can be described using averages and distributions, and that do not depend on the identity of the elements involved in a system, or their precise patterns of interactions. A classic example of a problem of disorganized complexity is the statistical mechanics of Ludwig Boltzmann, James-Clerk Maxwell, and Willard Gibbs, which focuses on the properties of gases.  

August 25, 2018 at 10:20PM

Soon after Weaver’s paper, biologists like Francois Jacob (Jacob and Monod 1961), (Jacob et al. 1963) and Stuart Kaufmann (Kauffman 1969), developed the idea of regulatory networks. Mathematicians like Paul Erdos and Alfred Renyi, advanced graph theory (Erdős and Rényi 1960) while Benoit Mandelbrot worked on Fractals (Mandelbrot and Van Ness 1968), (Mandelbrot 1982). Economists like Thomas Schelling (Schelling 1960) and Wasily Leontief (Leontief 1936), (Leontief 1936), respectively explored self-organization and input-output networks. Sociologists, like Harrison White (Lorrain and White 1971) and Mark Granovetter (Granovetter 1985), explored social networks, while psychologists like Stanley Milgram (Travers and Milgram 1969) explored the now famous small world problem.   

Some excellent references
August 25, 2018 at 10:24PM

First, I will focus in these larger groups because reviews that transcend the boundary between the social and natural sciences are rare, but I believe them to be valuable. One such review is Borgatti et al. (2009), which compares the network science of natural and social sciences arriving at a similar conclusion to the one I arrived.  

August 25, 2018 at 10:27PM

Links are the essence of networks. So I will start this review by comparing the mechanisms used by natural and social scientists to explain link formation.  

August 25, 2018 at 10:32PM

When connecting the people that acted in the same movie, natural scientists do not differentiate between people in leading or supporting roles.  

But they should because it’s not often the case that these are relevant unless they are represented by the same agent or agency.
August 25, 2018 at 10:51PM

For instance, in the study of mobile phone networks, the frequency and length of interactions has often been used as measures of link weight (Onnela et al. 2007), (Hidalgo and Rodriguez-Sickert 1008), (Miritello et al. 2011).  

And they probably shouldn’t because typically different levels of people are making these decisions. Studio brass and producers typically have more to say about the lead roles and don’t care as much about the smaller ones which are overseen by casting directors or sometimes the producers. The only person who has oversight of all of them is the director, and even then they may quit caring at some point.
August 25, 2018 at 10:52PM

Social scientists explain link formation through two families of mechanisms; one that finds it roots in sociology and the other one in economics. The sociological approach assumes that link formation is connected to the characteristics of individuals and their context. Chief examples of the sociological approach include what I will call the big three sociological link-formation hypotheses. These are: shared social foci, triadic closure, and homophily.  

August 25, 2018 at 10:55PM

The social foci hypothesis predicts that links are more likely to form among individuals who, for example, are classmates, co-workers, or go to the same gym (they share a social foci). The triadic closure hypothesis predicts that links are more likely to form among individuals that share “friends” or acquaintances. Finally, the homophily hypothesis predicts that links are more likely to form among individuals who share social characteristics, such as tastes, cultural background, or physical appearance (Lazarsfeld and Merton 1954), (McPherson et al. 2001).  

definitions of social foci, triadic closure, and homophily within network science.
August 26, 2018 at 11:39AM

Yet, strategic games look for equilibrium in the formation and dissolution of ties in the context of the game theory advanced first by (Von Neumann et al. 2007), and later by (Nash 1950).  

August 25, 2018 at 10:58PM

Preferential attachment is the idea that connectivity begets connectivity.  

August 25, 2018 at 10:59PM

Preferential attachment is an idea advanced originally by the statisticians John Willis and Udny Yule in (Willis and Yule 1922), but has been rediscovered numerous times during the twentieth century.  

August 25, 2018 at 11:00PM

Rediscoveries of this idea in the twentieth century include the work of (Simon 1955) (who did cite Yule), (Merton 1968), (Price 1976) (who studied citation networks), and (Barabási and Albert 1999), who published the modern reference for this model, which is now widely known as the Barabasi-Albert model.  

August 25, 2018 at 11:01PM

preferential attachment. In the eyes of the social sciences, however, understanding which of all of these hypotheses drives the formation of the network is what one needs to explore.  

For example what drives attachment of political candidates?
August 26, 2018 at 08:15AM

Finally it is worth noting that trust, through the theory of social capital, has been connected with long-term economic growth—even though these results are based on regressions using extremely sparse datasets.  

And this is an example of how Trump is hurting the economy.
August 26, 2018 at 08:33AM

Nevertheless, the evidence suggests that social capital and social institutions are significant predictors of economic growth, after controlling for the effects of human capital and initial levels of income (Knack and Keefer 1997), (Knack 2002).4 So trust is a relevant dimension of social interactions that has been connected to individual dyads, network formation, labor markets, and even economic growth.  

August 26, 2018 at 08:35AM

Social scientist, on the other hand, have focused on what ties are more likely to bring in new information, which are primarily weak ties (Granovetter 1973), and on why weak ties bring new information (because they bridge structural holes (Burt 2001), (Burt 2005)).  

August 26, 2018 at 09:45AM

heterogeneous networks have been found to be effective promoters of the evolution of cooperation, since there are advantages to being a cooperator when you are a hub, and hubs tend to stabilize networks in equilibriums where levels of cooperation are high (Ohtsuki et al. 2006), (Pacheco et al. 2006), (Lieberman et al. 2005), (Santos and Pacheco 2005).  

August 26, 2018 at 09:49AM

These results, however, have also been challenged by human experiments finding no such effect (Gracia-Lázaro et al. 2012). The study of cooperation in networks has also been performed in dynamic settings, where individuals are allowed to cut ties (Wang et al. 2012), promoting cooperation, and are faced with different levels of knowledge about the reputation of peers in their network (Gallo and Yan 2015). Moreover, cooperating behavior has seen to spread when people change the networks where they participate in (Fowler and Christakis 2010).  

Open questions
August 26, 2018 at 09:50AM

References

1.
Hidalgo CA. Disconnected, fragmented, or united? a trans-disciplinary review of network science. ANS. 2016;1(1). doi:10.1007/s41109-016-0010-3

👓 A Blended Family: Her Mother Was Neanderthal, Her Father Something Else Entirely | New York Times

Read A Blended Family: Her Mother Was Neanderthal, Her Father Something Else Entirely (nytimes.com)
Genetic analysis of bones discovered in a Siberian cave hints that the prehistoric world may have been filled with “hybrid” humans.

👓 'I was shocked it was so easy': ​meet the professor who says facial recognition ​​can tell if you're gay | The Guardian

Read 'I was shocked it was so easy': ​meet the professor who says facial recognition ​​can tell if you're gay by Paul Lewis (the Guardian)
Psychologist Michal Kosinski says artificial intelligence can detect your sexuality and politics just by looking at your face. What if he’s right?
How in God’s name are we repeating so many of the exact problems of the end of the 1800’s? First nationalism and protectionism and now the eugenics agenda?

👓 The stress of the fathers: epigenetics | The Economist

Read The stress of the fathers: epigenetics (Economist Espresso)
Abused or neglected children are more likely to have health problems as adults.
It seems like this has been known for a while or at least I’ve read relate research.

🎧 ‘The Daily’: The Hunt for the Golden State Killer | New York Times

Listened to ‘The Daily’: The Hunt for the Golden State Killer by Michael Barbaro from nytimes.com

Paul Holes was on the verge of retirement, having never completed his decades-long mission to catch the Golden State Killer. Then he had an idea: Upload DNA evidence to a genealogy website.

On today’s episode:

• Paul Holes, an investigator in California who helped to crack the case.

Background reading:

• A spate of murders and rapes across California in the 1970s and 1980s went unsolved for decades. Then, last week, law enforcement officials arrested Joseph James DeAngelo, 72, a former police officer.

• Investigators submitted DNA collected at a crime scene to the genealogy website GEDmatch, through which they were able to track down distant relatives of the suspect. The method has raised concerns about privacy and ethics.

A stunning story with some ingenious detective work. I worry what the potential privacy problems are off in the future, though one of the ideas here is that it actually helps protect the privacy of some individuals who are wrongly and maliciously accused and thus saves a lot of time and money.

The subtleties will be when we’re using this type of DNA evidence more frequently for lower level crimes while at the same time the technology gets increasingly cheaper to carry out.

👓 How Many Genes Do Cells Need? Maybe Almost All of Them | Quanta Magazine

Read How Many Genes Do Cells Need? Maybe Almost All of Them (Quanta Magazine)
An ambitious study in yeast shows that the health of cells depends on the highly intertwined effects of many genes, few of which can be deleted together without consequence.
There could be some interesting data to play with here if available.

I also can’t help but wonder about applying some of Stuart Kauffman’s ideas to something like this. In particular, this sounds very reminiscent to his analogy of what happens when one strings thread randomly among a pile of buttons and the resulting complexity.

👓 Mutating DNA caught on film | Science | AAAS

Read Mutating DNA caught on film by Elizabeth Pennisi (Science | AAAS)
Study in bacteria shows how regularly DNA changes and how few of those changes are deadly
https://www.youtube.com/watch?v=Vi38IqxkW68

This is a rather cool little experiment.

h/t to @moorejh via Twitter:

Bookmarked on March 16, 2018 at 12:15PM

👓 The First Species to Have Every Individual’s Genome Sequenced | The Atlantic

Read The First Species to Have Every Individual’s Genome Sequenced by Ed Yong (The Atlantic)
It’s an endearing, giant, flightless, New Zealand parrot, and it’s a poster child for the quantified-self movement.
Kakapo

👓 White nationalists are flocking to genetic ancestry tests — but many don’t like their results | Stat News

Read White nationalists are flocking to genetic ancestry tests. Some don’t like what they find by Eric Boodman (Stat News)
It was a strange moment of triumph against racism: The gun-slinging white supremacist Craig Cobb, dressed up for daytime TV in a dark suit and red tie, hearing that his DNA testing revealed his ancestry to be only “86 percent European, and … 14 percent Sub-Saharan African.” The studio audience whooped and laughed and cheered. And Cobb — who was, in 2013, charged with terrorizing people while trying to create an all-white enclave in North Dakota — reacted like a sore loser in the schoolyard. “Wait a minute, wait a minute, hold on, just wait a minute,” he said, trying to put on an all-knowing smile. “This is called statistical noise.”

👓 EXCLUSIVE: First human embryos edited in U.S., using CRISPR | MIT Technology Review

Read EXCLUSIVE: First human embryos edited in U.S., using CRISPR by Steve Connor (MIT Technology Review)
Researchers have demonstrated they can efficiently improve the DNA of human embryos.

IPAM Workshop on Regulatory and Epigenetic Stochasticity in Development and Disease, March 1-3

Bookmarked IPAM Workshop on Regulatory and Epigenetic Stochasticity in Development and Disease (Institute for Pure and Applied Mathematics, UCLA | March 1-3, 2017)
Epigenetics refers to information transmitted during cell division other than the DNA sequence per se, and it is the language that distinguishes stem cells from somatic cells, one organ from another, and even identical twins from each other. In contrast to the DNA sequence, the epigenome is relatively susceptible to modification by the environment as well as stochastic perturbations over time, adding to phenotypic diversity in the population. Despite its strong ties to the environment, epigenetics has never been well reconciled to evolutionary thinking, and in fact there is now strong evidence against the transmission of so-called “epi-alleles,” i.e. epigenetic modifications that pass through the germline.

However, genetic variants that regulate stochastic fluctuation of gene expression and phenotypes in the offspring appear to be transmitted as an epigenetic or even Lamarckian trait. Furthermore, even the normal process of cellular differentiation from a single cell to a complex organism is not understood well from a mathematical point of view. There is increasingly strong evidence that stem cells are highly heterogeneous and in fact stochasticity is necessary for pluripotency. This process appears to be tightly regulated through the epigenome in development. Moreover, in these biological contexts, “stochasticity” is hardly synonymous with “noise”, which often refers to variation which obscures a “true signal” (e.g., measurement error) or which is structural, as in physics (e.g., quantum noise). In contrast, “stochastic regulation” refers to purposeful, programmed variation; the fluctuations are random but there is no true signal to mask.

This workshop will serve as a forum for scientists and engineers with an interest in computational biology to explore the role of stochasticity in regulation, development and evolution, and its epigenetic basis. Just as thinking about stochasticity was transformative in physics and in some areas of biology, it promises to fundamentally transform modern genetics and help to explain phase transitions such as differentiation and cancer.

This workshop will include a poster session; a request for poster titles will be sent to registered participants in advance of the workshop.
Speaker List:
Adam Arkin (Lawrence Berkeley Laboratory)
Gábor Balázsi (SUNY Stony Brook)
Domitilla Del Vecchio (Massachusetts Institute of Technology)
Michael Elowitz (California Institute of Technology)
Andrew Feinberg (Johns Hopkins University)
Don Geman (Johns Hopkins University)
Anita Göndör (Karolinska Institutet)
John Goutsias (Johns Hopkins University)
Garrett Jenkinson (Johns Hopkins University)
Andre Levchenko (Yale University)
Olgica Milenkovic (University of Illinois)
Johan Paulsson (Harvard University)
Leor Weinberger (University of California, San Francisco (UCSF))

NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016

Bookmarked NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016 by NIMBioS (nimbios.org)
This tutorial will review the basics of theory in the field of evolutionary quantitative genetics and its connections to evolution observed at various time scales. Quantitative genetics deals with the inheritance of measurements of traits that are affected by many genes. Quantitative genetic theory for natural populations was developed considerably in the period from 1970 to 1990 and up to the present, and it has been applied to a wide range of phenomena including the evolution of differences between the sexes, sexual preferences, life history traits, plasticity of traits, as well as the evolution of body size and other morphological measurements. Textbooks have not kept pace with these developments, and currently few universities offer courses in this subject aimed at evolutionary biologists. There is a need for evolutionary biologists to understand this field because of the ability to collect large amounts of data by computer, the development of statistical methods for changes of traits on evolutionary trees and for changes in a single species through time, and the realization that quantitative characters will not soon be fully explained by genomics. This tutorial aims to fill this need by reviewing basic aspects of theory and illustrating how that theory can be tested with data, both from single species and with multiple-species phylogenies. Participants will learn to use R, an open-source statistical programming language, to build and test evolutionary models. The intended participants for this tutorial are graduate students, postdocs, and junior faculty members in evolutionary biology.

Nick Lane and Philip Ball Discuss Mitochondria, Sex, and How to Live Longer

Bookmarked Nick Lane and Philip Ball Discuss Mitochondria, Sex, and How to Live Longer by Philip Ball (Nautil.us)
In his 2010 book, Life Ascending: The Ten Great Inventions of Evolution, Nick Lane, a biochemist at University College London, explores with eloquence and clarity the big questions of life: how it began, why we age and die, and why we have sex. Lane been steadily constructing an alternative view of evolution to the one in which genes explain it all. He argues that some of the major events during evolutionary history, including the origin of life itself, are best understood by considering where the energy comes from and how it is used. Lane describes these ideas in his 2015 book, The Vital Question: Why Is Life the Way It Is?. Recently Bill Gates called it “an amazing inquiry into the origins of life,” adding, Lane “is one of those original thinkers who make you say: More people should know about this guy’s work.” Nautilus caught up with Lane in his laboratory in London and asked him about his ideas on aging, sex, and death.
Biochemist Nick Lane explains the elements of life, sex, and aging in an engaging popular science interview.

Read more

Books by Nick Lane

Popular Science Books on Information Theory, Biology, and Complexity

Previously, I had made a large and somewhat random list of books which lie in the intersection of the application of information theory, physics, and engineering practice to the area of biology.  Below I’ll begin to do a somewhat better job of providing a finer gradation of technical level for both the hobbyist or the aspiring student who wishes to bring themselves to a higher level of understanding of these areas.  In future posts, I’ll try to begin classifying other texts into graduated strata as well.  The final list will be maintained here: Books at the Intersection of Information Theory and Biology.

Introductory / General Readership / Popular Science Books

These books are written on a generally non-technical level and give a broad overview of their topics with occasional forays into interesting or intriguing subtopics. They include little, if any, mathematical equations or conceptualization. Typically, any high school student should be able to read, follow, and understand the broad concepts behind these books.  Though often non-technical, these texts can give some useful insight into the topics at hand, even for the most advanced researchers.

Complexity: A Guided Tour by Melanie Mitchell (review)

Possibly one of the best places to start, this text gives a great overview of most of the major areas of study related to these fields.

Entropy Demystified: The Second Law Reduced to Plain Common Sense by Arieh Ben-Naim

One of the best books on the concept of entropy out there.  It can be read even by middle school students with no exposure to algebra and does a fantastic job of laying out the conceptualization of how entropy underlies large areas of the broader subject. Even those with Ph.D.’s in statistical thermodynamics can gain something useful from this lovely volume.

The Information: A History, a Theory, a Flood by James Gleick (review)

A relatively recent popular science volume covering various conceptualizations of what information is and how it’s been dealt with in science and engineering.  Though it has its flaws, its certainly a good introduction to the beginner, particularly with regard to history.

The Origin of Species by Charles Darwin

One of the most influential pieces of writing known to man, this classical text is the basis from which major strides in biology have been made as a result. A must read for everyone on the planet.

Information, Entropy, Life and the Universe: What We Know and What We Do Not Know by Arieh Ben-Naim

Information Theory and Evolution by John Avery

The Touchstone of Life: Molecular Information, Cell Communication, and the Foundations of Life by Werner R. Loewenstein (review)

Information Theory, Evolution, and the Origin of Life by Hubert P. Yockey

The four books above have a significant amount of overlap. Though one could read all of them, I recommend that those pressed for time choose Ben-Naim first. As I write this I’ll note that Ben-Naim’s book is scheduled for release on May 30, 2015, but he’s been kind enough to allow me to read an advance copy while it was in process; it gets my highest recommendation in its class. Loewenstein covers a bit more than Avery who also has a more basic presentation. Most who continue with the subject will later come across Yockey’s Information Theory and Molecular Biology which is similar to his text here but written at a slightly higher level of sophistication. Those who finish at this level of sophistication might want to try Yockey third instead.

The Red Queen: Sex and the Evolution of Human Nature by Matt Ridley

Grammatical Man: Information, Entropy, Language, and Life  by Jeremy Campbell

Life’s Ratchet: How Molecular Machines Extract Order from Chaos by Peter M. Hoffmann

Complexity: The Emerging Science at the Edge of Order and Chaos by M. Mitchell Waldrop

The Big Picture: On the Origins of Life, Meaning, and the Universe Itself (Dutton, May 10, 2016) 

In the coming weeks/months, I’ll try to continue putting recommended books on the remainder of the rest of the spectrum, the balance of which follows in outline form below. As always, I welcome suggestions and recommendations based on others’ experiences as well. If you’d like to suggest additional resources in any of the sections below, please do so via our suggestion box. For those interested in additional resources, please take a look at the ITBio Resources page which includes information about related research groups; references and journal articles; academic, research institutes, societies, groups, and organizations; and conferences, workshops, and symposia.

Lower Level Undergraduate

These books are written at a level that can be grasped and understood by most with a freshmen or sophomore university level. Coursework in math, science, and engineering will usually presume knowledge of calculus, basic probability theory, introductory physics, chemistry, and basic biology.

Upper Level Undergraduate

These books are written at a level that can be grasped and understood by those at a junior or senor university level. Coursework in math, science, and engineering may presume knowledge of probability theory, differential equations, linear algebra, complex analysis, abstract algebra, signal processing, organic chemistry, molecular biology, evolutionary theory, thermodynamics, advanced physics, and basic information theory.

Graduate Level

These books are written at a level that can be grasped and understood by most working at the level of a master’s level at most universities.  Coursework presumes all the previously mentioned classes, though may require a higher level of sub-specialization in one or more areas of mathematics, physics, biology, or engineering practice.  Because of the depth and breadth of disciplines covered here, many may feel the need to delve into areas outside of their particular specialization.

Probability Models for DNA Sequence Evolution

Bookmarked Probability Models for DNA Sequence Evolution (Springer, 2008, 2nd Edition) by Rick Durrett (math.duke.edu)
While browsing through some textbooks and researchers today, I came across a fantastic looking title: Probability Models for DNA Sequence Evolution by Rick Durrett (Springer, 2008). While searching his website at Duke, I noticed that he’s made a .pdf copy of a LaTeX version of the 2nd edition available for download.   I hope others find it as interesting and useful as I do.

I’ll also give him a shout out for being a mathematician with a fledgling blog: Rick’s Ramblings.

Book Cover of Probability Models for DNA Sequence Evolution by Richard Durrett
Probability Models for DNA Sequence Evolution by Richard Durrett