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.
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))
November 16, 2016 – November 18, 2016
Noyce Conference Room
This workshop will address a fundamental question in theoretical biology: Does the relationship between statistical physics and the need of biological systems to process information underpin some of their deepest features? It recognizes that a core feature of biological systems is that they acquire, store and process information (i.e., perform computation). However to manipulate information in this way they require a steady flux of free energy from their environments. These two, inter-related attributes of biological systems are often taken for granted; they are not part of standard analyses of either the homeostasis or the evolution of biological systems. In this workshop we aim to fill in this major gap in our understanding of biological systems, by gaining deeper insight in the relation between the need for biological systems to process information and the free energy they need to pay for that processing.
The goal of this workshop is to address these issues by focusing on a set three specific question:
How has the fraction of free energy flux on earth that is used by biological computation changed with time?;
What is the free energy cost of biological computation / function?;
What is the free energy cost of the evolution of biological computation / function.
In all of these cases we are interested in the fundamental limits that the laws of physics impose on various aspects of living systems as expressed by these three questions.
Purpose: Research Collaboration
SFI Host: David Krakauer, Michael Lachmann, Manfred Laubichler, Peter Stadler, and David Wolpert
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.
AT LAST WE have it in English. Summa Technologiae, originally published in Polish in 1964, is the cornerstone of Stanislaw Lem’s oeuvre, his consummate work of speculative nonfiction. Trained in medicine and biology, Lem synthesizes the current science of the day in ways far ahead of most science fiction of the time.
I came across this book review quite serendipitously today via an Auerbach article in Slate, which I’ve bookmarked. I found a copy of the book and have added it to the top of my reading pile. As I’m currently reading an advance reader edition of Sean Carroll’sThe Big Picture, I can only imagine how well the two may go together despite being written nearly 60 years apart.
Yesterday, via a notification from Lanyard, I came across a notice for the upcoming conference “The Information Universe” which hits several of the sweet spots for areas involving information theory, physics, the origin of life, complexity, computer science, and microbiology. It is scheduled to occur from October 7-9, 2015 at the Infoversum Theater in Groningen, The Netherlands.
I’ll let their site speak for itself below, but they already have an interesting line up of speakers including:
Erik Verlinde, Professor Theoretical Physics, University of Amsterdam, Netherlands
Alex Szalay, Alumni Centennial Professor of Astronomy, The Johns Hopkins University, USA
Gerard ‘t Hooft, Professor Theoretical Physics, University of Utrecht, Netherlands
Gregory Chaitin, Professor Mathematics and Computer Science, Federal University of Rio de Janeiro, Brasil
Charley Lineweaver, Professor Astronomy and Astrophysics, Australian National University, Australia
Lude Franke, Professor System Genetics, University Medical Center Groningen, Netherlands