A scientist in China claimed to have created the world’s first gene-edited human beings. How should the U.S. respond?
In this episode, Haley interviews Natalia Komarova, Chancellor's Professor of the School of Physical Sciences at the University of California, Irvine. Komarova talks with Haley at the Ninth International Conference on Complex Systems about her presentation, which explored using applied mathematics to study the spread of mutants, as well as the evolution of popular music.
There’s some interesting sounding research being described here. Be sure to circle back around to some of her papers.
Soon two American biotechnology firms hope to offer couples undertaking in vitro fertilisation the chance to screen embryos before they are implanted. Pre-implantation genetic diagnosis is already widely used to test for chromosomal abnormalities or specific genetic disorders. But MyOme and Genomic Prediction plan to reconstruct the whole sequence of an embryo’s genome using just a few cells from a biopsy and genetic sequences of both parents. They can then, in theory, calculate the risk the embryo will develop a wide range of different diseases in later life—including ailments that are extraordinarily complicated, involving thousands of genetic variants. By selecting between different embryos, those undergoing IVF can optimise the health of their progeny in a way that those who conceive naturally cannot. That raises ethical concerns. Although both firms will screen embryos for disease risk only, there is no reason why traits such as height or intelligence might not be selected in the same way.
Melanie Mitchell, has Farrar, Straus & Giroux assigned your book a publicist yet? I’d love to get an advance review copy when they’re available.
Perhaps it’s time for a follow up conference?
Waiting for the price of some of these to drop.
The 'Baldwin effect' has now been demonstrated at the genetic level in a population of dark-colored lizards adapted to live on a lava flow in the desert.
Highlights, Quotes, Annotations, & Marginalia
One explanation has been that many of an animal’s traits are not fixed, but can change during its lifetime. This “phenotypic plasticity” enables individual animals to alter their appearance or behavior enough to survive in a new environment. Eventually, new adaptations promoting survival arise in the population through genetic changes and natural selection, which acts on the population over generations. This is known as the “Baldwin effect” after the psychologist James Mark Baldwin, who presented the idea in a landmark paper published in 1896. ❧
September 11, 2018 at 08:57AM
Journal article available at: https://www.cell.com/current-biology/pdfExtended/S0960-9822(18)30899-6
A #MeTooSTEM story about requesting a change in tradition
I’ve followed bits of this story for a while since it touches on an area I follow, but I had no idea the harassment was so terrible. The Romeo and Juliette business is just deplorable.
This volume presents the proceedings of ALIFE 2018, the 2018 Conference on Artificial Life, held July 23rd-27th. It took place in Tokyo, Japan (http://2018.alife.org). The ALIFE and ECAL conferences have been the major meeting of the artificial life (ALife) research community since 1987 and 1991, respectively. As a Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE), the 2018 Conference on Artificial Life (ALIFE 2018) will take place outside both Europe and the US, in Tokyo, Japan.
Methods originally developed in Information Theory have found wide applicability in computational neuroscience. Beyond these original methods there is a need to develop novel tools and approaches that are driven by problems arising in neuroscience. A number of researchers in computational/systems neuroscience and in information/communication theory are investigating problems of information representation and processing. While the goals are often the same, these researchers bring different perspectives and points of view to a common set of neuroscience problems. Often they participate in different fora and their interaction is limited. The goal of the workshop is to bring some of these researchers together to discuss challenges posed by neuroscience and to exchange ideas and present their latest work. The workshop is targeted towards computational and systems neuroscientists with interest in methods of information theory as well as information/communication theorists with interest in neuroscience.
From the celebrated neurobiologist and primatologist, a landmark, genre-defining examination of human behavior, both good and bad, and an answer to the question: Why do we do the things we do?
Sapolsky's storytelling concept is delightful but it also has a powerful intrinsic logic: he starts by looking at the factors that bear on a person's reaction in the precise moment a behavior occurs, and then hops back in time from there, in stages, ultimately ending up at the deep history of our species and its evolutionary legacy.
And so the first category of explanation is the neurobiological one. A behavior occurs--whether an example of humans at our best, worst, or somewhere in between. What went on in a person's brain a second before the behavior happened? Then Sapolsky pulls out to a slightly larger field of vision, a little earlier in time: What sight, sound, or smell caused the nervous system to produce that behavior? And then, what hormones acted hours to days earlier to change how responsive that individual is to the stimuli that triggered the nervous system? By now he has increased our field of vision so that we are thinking about neurobiology and the sensory world of our environment and endocrinology in trying to explain what happened.
Sapolsky keeps going: How was that behavior influenced by structural changes in the nervous system over the preceding months, by that person's adolescence, childhood, fetal life, and then back to his or her genetic makeup? Finally, he expands the view to encompass factors larger than one individual. How did culture shape that individual's group, what ecological factors millennia old formed that culture? And on and on, back to evolutionary factors millions of years old.
The result is one of the most dazzling tours d'horizon of the science of human behavior ever attempted, a majestic synthesis that harvests cutting-edge research across a range of disciplines to provide a subtle and nuanced perspective on why we ultimately do the things we do...for good and for ill. Sapolsky builds on this understanding to wrestle with some of our deepest and thorniest questions relating to tribalism and xenophobia, hierarchy and competition, morality and free will, and war and peace. Wise, humane, often very funny, Behave is a towering achievement, powerfully humanizing, and downright heroic in its own right.
About the Course:
Probability and statistics have long helped scientists make sense of data about the natural world — to find meaningful signals in the noise. But classical statistics prove a little threadbare in today’s landscape of large datasets, which are driving new insights in disciplines ranging from biology to ecology to economics. It's as true in biology, with the advent of genome sequencing, as it is in astronomy, with telescope surveys charting the entire sky.
The data have changed. Maybe it's time our data analysis tools did, too.
During this three-month online course, starting June 11th, instructors Hector Zenil and Narsis Kiani will introduce students to concepts from the exciting new field of Algorithm Information Dynamics to search for solutions to fundamental questions about causality — that is, why a particular set of circumstances lead to a particular outcome.
Algorithmic Information Dynamics (or Algorithmic Dynamics in short) is a new type of discrete calculus based on computer programming to study causation by generating mechanistic models to help find first principles of physical phenomena building up the next generation of machine learning.
The course covers key aspects from graph theory and network science, information theory, dynamical systems and algorithmic complexity. It will venture into ongoing research in fundamental science and its applications to behavioral, evolutionary and molecular biology.
Students should have basic knowledge of college-level math or physics, though optional sessions will help students with more technical concepts. Basic computer programming skills are also desirable, though not required. The course does not require students to adopt any particular programming language for the Wolfram Language will be mostly used and the instructors will share a lot of code written in this language that student will be able to use, study and exploit for their own purposes.
- The course will begin with a conceptual overview of the field.
- Then it will review foundational theories like basic concepts of statistics and probability, notions of computability and algorithmic complexity, and brief introductions to graph theory and dynamical systems.
- Finally, the course explores new measures and tools related to reprogramming artificial and biological systems. It will showcase the tools and framework in applications to systems biology, genetic networks and cognition by way of behavioral sequences.
- Students will be able apply the tools to their own data and problems. The instructors will explain in detail how to do this, and will provide all the tools and code to do so.
The course runs 11 June through 03 September 2018.
Tuition is $50 required to get to the course material during the course and a certificate at the end but is is free to watch and if no fee is paid materials will not be available until the course closes. Donations are highly encouraged and appreciated in support for SFI's ComplexityExplorer to continue offering new courses.
In addition to all course materials tuition includes:
- Six-month access to the Wolfram|One platform (potentially renewable by other six) worth 150 to 300 USD.
- Free digital copy of the course textbook to be published by Cambridge University Press.
- Several gifts will be given away to the top students finishing the course, check the FAQ page for more details.
Best final projects will be invited to expand their results and submit them to the journal Complex Systems, the first journal in the field founded by Stephen Wolfram in 1987.
About the Instructor(s):
Hector Zenil has a PhD in Computer Science from the University of Lille 1 and a PhD in Philosophy and Epistemology from the Pantheon-Sorbonne University of Paris. He co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. He is also the head of the Algorithmic Nature Group at LABoRES, the Paris-based lab that started the Online Algorithmic Complexity Calculator and the Human Randomness Perception and Generation Project. Previously, he was a Research Associate at the Behavioural and Evolutionary Theory Lab at the Department of Computer Science at the University of Sheffield in the UK before joining the Department of Computer Science, University of Oxford as a faculty member and senior researcher.
Narsis Kiani has a PhD in Mathematics and has been a postdoctoral researcher at Dresden University of Technology and at the University of Heidelberg in Germany. She has been a VINNOVA Marie Curie Fellow and Assistant Professor in Sweden. She co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. Narsis is also a member of the Algorithmic Nature Group, LABoRES.
Hector and Narsis are the leaders of the Algorithmic Dynamics Lab at the Unit of Computational Medicine at Karolinska Institute.
Alyssa Adams has a PhD in Physics from Arizona State University and studies what makes living systems different from non-living ones. She currently works at Veda Data Solutions as a data scientist and researcher in social complex systems that are represented by large datasets. She completed an internship at Microsoft Research, Cambridge, UK studying machine learning agents in Minecraft, which is an excellent arena for simple and advanced tasks related to living and social activity. Alyssa is also a member of the Algorithmic Nature Group, LABoRES.
The development of the course and material offered has been supported by:
- The Foundational Questions Institute (FQXi)
- Wolfram Research
- John Templeton Foundation
- Santa Fe Institute
- Swedish Research Council (Vetenskapsrådet)
- Algorithmic Nature Group, LABoRES for the Natural and Digital Sciences
- Living Systems Lab, King Abdullah University of Science and Technology.
- Department of Computer Science, Oxford University
- Cambridge University Press
- London Mathematical Society
- Springer Verlag
- ItBit for the Natural and Computational Sciences and, of course,
- the Algorithmic Dynamics lab, Unit of Computational Medicine, SciLifeLab, Center for Molecular Medicine, The Karolinska Institute
Course dates: 11 Jun 2018 9pm PDT to 03 Sep 2018 10pm PDT
- A Computational Approach to Causality
- A Brief Introduction to Graph Theory and Biological Networks
- Elements of Information Theory and Computability
- Randomness and Algorithmic Complexity
- Dynamical Systems as Models of the World
- Practice, Technical Skills and Selected Topics
- Algorithmic Information Dynamics and Reprogrammability
- Applications to Behavioural, Evolutionary and Molecular Biology
Another interesting course from the SFI. Looks like an interesting way to spend the summer.
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.
Investigating information storage and processing in biological systems
We work on novel ways to understand and control complex pattern formation. We use techniques of molecular genetics, biophysics, and computational modeling to address large-scale control of growth and form. We work in whole frogs and flatworms, and sometimes zebrafish and human tissues in culture. Our projects span regeneration, embryogenesis, cancer, and learning plasticity – all examples of how cellular networks process information. In all of these efforts, our goal is not only to understand the molecular mechanisms necessary for morphogenesis, but also to uncover and exploit the cooperative signaling dynamics that enable complex bodies to build and remodel themselves toward a correct structure. Our major goal is to understand how individual cell behaviors are orchestrated towards appropriate large-scale outcomes despite unpredictable environmental perturbations.