489 research outputs found
The Algorithmic Origins of Life
Although it has been notoriously difficult to pin down precisely what it is
that makes life so distinctive and remarkable, there is general agreement that
its informational aspect is one key property, perhaps the key property. The
unique informational narrative of living systems suggests that life may be
characterized by context-dependent causal influences, and in particular, that
top-down (or downward) causation -- where higher-levels influence and constrain
the dynamics of lower-levels in organizational hierarchies -- may be a major
contributor to the hierarchal structure of living systems. Here we propose that
the origin of life may correspond to a physical transition associated with a
shift in causal structure, where information gains direct, and
context-dependent causal efficacy over the matter it is instantiated in. Such a
transition may be akin to more traditional physical transitions (e.g.
thermodynamic phase transitions), with the crucial distinction that determining
which phase (non-life or life) a given system is in requires dynamical
information and therefore can only be inferred by identifying causal
architecture. We discuss some potential novel research directions based on this
hypothesis, including potential measures of such a transition that may be
amenable to laboratory study, and how the proposed mechanism corresponds to the
onset of the unique mode of (algorithmic) information processing characteristic
of living systems.Comment: 13 pages, 1 tabl
Different Kinds of Protein Folding Identified with a Coarse-Grained Heteropolymer Model
Applying multicanonical simulations we investigated folding properties of
off-lattice heteropolymers employing a mesoscopic hydrophobic-polar model. We
study for various sequences folding channels in the free-energy landscape by
comparing the equilibrium conformations with the folded state in terms of an
angular overlap parameter. Although all investigated heteropolymer sequences
contain the same content of hydrophobic and polar monomers, our analysis of the
folding channels reveals a variety of characteristic folding behaviors known
from realistic peptides.Comment: 3 pages, 2 figure
A Realistic Model under which the Genetic Code is Optimal
The genetic code has a high level of error robustness. Using values of
hydrophobicity scales as a proxy for amino acid character, and the Mean Square
measure as a function quantifying error robustness, a value can be obtained for
a genetic code which reflects the error robustness of that code. By comparing
this value with a distribution of values belonging to codes generated by random
permutations of amino acid assignments, the level of error robustness of a
genetic code can be quantified. We present a calculation in which the standard
genetic code is shown to be optimal. We obtain this result by (1) using
recently updated values of polar requirement as input; (2) fixing seven
assignments (Ile, Trp, His, Phe, Tyr, Arg, and Leu) based on aptamer
considerations; and (3) using known biosynthetic relations of the 20 amino
acids. This last point is reflected in an approach of subdivision (restricting
the random reallocation of assignments to amino acid subgroups, the set of 20
being divided in four such subgroups). The three approaches to explain
robustness of the code (specific selection for robustness, amino acid-RNA
interactions leading to assignments, or a slow growth process of assignment
patterns) are reexamined in light of our findings. We offer a comprehensive
hypothesis, stressing the importance of biosynthetic relations, with the code
evolving from an early stage with just glycine and alanine, via intermediate
stages, towards 64 codons carrying todays meaning.Comment: 22 pages, 3 figures, 4 tables Journal of Molecular Evolution, July
201
The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications
The principle goal of computational mechanics is to define pattern and
structure so that the organization of complex systems can be detected and
quantified. Computational mechanics developed from efforts in the 1970s and
early 1980s to identify strange attractors as the mechanism driving weak fluid
turbulence via the method of reconstructing attractor geometry from measurement
time series and in the mid-1980s to estimate equations of motion directly from
complex time series. In providing a mathematical and operational definition of
structure it addressed weaknesses of these early approaches to discovering
patterns in natural systems.
Since then, computational mechanics has led to a range of results from
theoretical physics and nonlinear mathematics to diverse applications---from
closed-form analysis of Markov and non-Markov stochastic processes that are
ergodic or nonergodic and their measures of information and intrinsic
computation to complex materials and deterministic chaos and intelligence in
Maxwellian demons to quantum compression of classical processes and the
evolution of computation and language.
This brief review clarifies several misunderstandings and addresses concerns
recently raised regarding early works in the field (1980s). We show that
misguided evaluations of the contributions of computational mechanics are
groundless and stem from a lack of familiarity with its basic goals and from a
failure to consider its historical context. For all practical purposes, its
modern methods and results largely supersede the early works. This not only
renders recent criticism moot and shows the solid ground on which computational
mechanics stands but, most importantly, shows the significant progress achieved
over three decades and points to the many intriguing and outstanding challenges
in understanding the computational nature of complex dynamic systems.Comment: 11 pages, 123 citations;
http://csc.ucdavis.edu/~cmg/compmech/pubs/cmr.ht
Scientific and personal recollections of Roberto Petronzio
This paper aims to recall some of the main contributions of Roberto Petronzio
to physics, with a particular regard to the period we have been working
together. His seminal contributions cover an extremely wide range of topics:
the foundation of the perturbative approach to QCD, various aspects of weak
interaction theory, from basic questions (e.g. the mass of the Higgs) to
lattice weak interaction, lattice QCD from the beginning to most recent
computations.Comment: 15 pages, 5 figures, talk presented at Lattice 201
Role of Proteome Physical Chemistry in Cell Behavior.
We review how major cell behaviors, such as bacterial growth laws, are derived from the physical chemistry of the cell's proteins. On one hand, cell actions depend on the individual biological functionalities of their many genes and proteins. On the other hand, the common physics among proteins can be as important as the unique biology that distinguishes them. For example, bacterial growth rates depend strongly on temperature. This dependence can be explained by the folding stabilities across a cell's proteome. Such modeling explains how thermophilic and mesophilic organisms differ, and how oxidative damage of highly charged proteins can lead to unfolding and aggregation in aging cells. Cells have characteristic time scales. For example, E. coli can duplicate as fast as 2-3 times per hour. These time scales can be explained by protein dynamics (the rates of synthesis and degradation, folding, and diffusional transport). It rationalizes how bacterial growth is slowed down by added salt. In the same way that the behaviors of inanimate materials can be expressed in terms of the statistical distributions of atoms and molecules, some cell behaviors can be expressed in terms of distributions of protein properties, giving insights into the microscopic basis of growth laws in simple cells
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