In the simplest view of transcriptional regulation, the expression of a gene is turned on or off by changes in the concentration of a transcription factor (TF). We use recent data on noise levels in gene expression to show that it should be possible to transmit much more than just one regulatory bit. Realizing this optimal information capacity would require that the dynamic range of TF concentrations used by the cell, the input/output relation of the regulatory module, and the noise in gene expression satisfy certain matching relations, which we derive. These results provide parameter-free, quantitative predictions connecting independently measurable quantities. Although we have considered only the simplified problem of a single gene responding to a single TF, we find that these predictions are in surprisingly good agreement with recent experiments on the Bicoid/Hunchback system in the early Drosophila embryo and that this system achieves ∼90% of its theoretical maximum information transmission. https://doi.org/10.1073/pnas.0806077105
To understand the structure of a large-scale biological, social, or technological network, it can be helpful to decompose the network into smaller subunits or modules. In this article, we develop an information-theoretic foundation for the concept of modularity in networks. We identify the modules of which the network is composed by finding an optimal compression of its topology, capitalizing on regularities in its structure. We explain the advantages of this approach and illustrate them by partitioning a number of real-world and model networks.
https://doi.org/10.1073/pnas.0611034104
Information theory in biology by Henry Quastler, Editor. 1953. 273 pp. Urbana: University of Illinois Press
There are two kinds of scientific books worth reading. One is the monograph or treatise type, in which a more or less large field of science is presented in a systematic way, and in the
form of a product as finished as possible at the given time. This kind of book may be considered a source of knowledge then available. The other type of book may present a collection of
chapters or individual articles which do not claim to be a complete and systematic treatment of the subject; however the reader not only finds interesting ideas there, but the reading as
such suggests new ideas. Such books are useful. For, although a rough and unfinished idea per se does not even remotely have the value of a well-elaborated scientific study, yet no elaborate
study, no important theory, can be developed without first having a few rough ideas.
The book under consideration definitely belongs to the second category: it is a collection of essays. As the editor states in the Introduction (p. 2) : "The papers in this volume are of a very
different degree of maturity. They range from authoritative reviews of well-known facts to hesitant and tentative formulations of embryonic ideas." He further states (p. 3): "We are
aware of the fact that this volume is largely exploratory."
If the above is to be considered as a shortcoming, then the reviewer does not need to dwell on it, because the editor, and undoubtedly the authors, are fully aware of it, and duly warn
the reader. If we evaluate the book from the point of view of how many ideas it suggests to the reader, then, at least so far as this reviewer is concerned, it must be considered a great success.
Background: Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide ``reverse engineering'' of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods.
Results: We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the c MYC proto-oncogene. We also study the effects of mis estimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors.
MOTIVATION:
Traditional sequence distances require an alignment and therefore are not directly applicable to the problem of whole genome phylogeny where events such as rearrangements make full length alignments impossible. We present a sequence distance that works on unaligned sequences using the information theoretical concept of Kolmogorov complexity and a program to estimate this distance.
RESULTS:
We establish the mathematical foundations of our distance and illustrate its use by constructing a phylogeny of the Eutherian orders using complete unaligned mitochondrial genomes. This phylogeny is consistent with the commonly accepted one for the Eutherians. A second, larger mammalian dataset is also analyzed, yielding a phylogeny generally consistent with the commonly accepted one for the mammals.
AVAILABILITY:
The program to estimate our sequence distance, is available at http://www.cs.cityu.edu.hk/~cssamk/gencomp/GenCompress1.htm. The distance matrices used to generate our phylogenies are available at http://www.math.uwaterloo.ca/~mli/distance.html.
MOTIVATION:
As an increasing number of protein structures become available, the need for algorithms that can quantify the similarity between protein structures increases as well. Thus, the comparison of proteins' structures, and their clustering accordingly to a given similarity measure, is at the core of today's biomedical research. In this paper, we show how an algorithmic information theory inspired Universal Similarity Metric (USM) can be used to calculate similarities between protein pairs. The method, besides being theoretically supported, is surprisingly simple to implement and computationally efficient.
RESULTS:
Structural similarity between proteins in four different datasets was measured using the USM. The sample employed represented alpha, beta, alpha-beta, tim-barrel, globins and serpine protein types. The use of the proposed metric allows for a correct measurement of similarity and classification of the proteins in the four datasets.
AVAILABILITY:
All the scripts and programs used for the preparation of this paper are available at http://www.cs.nott.ac.uk/~nxk/USM/protocol.html. In that web-page the reader will find a brief description on how to use the various scripts and programs.
Living systems are distinguished in nature by their ability to maintain stable, ordered states far from equilibrium. This is despite constant buffeting by thermodynamic forces that, if unopposed, will inevitably increase disorder. Cells maintain a steep transmembrane entropy gradient by continuous application of information that permits cellular components to carry out highly specific tasks that import energy and export entropy. Thus, the study of information storage, flow and utilization is critical for understanding first principles that govern the dynamics of life. Initial biological applications of information theory (IT) used Shannon's methods to measure the information content in strings of monomers such as genes, RNA, and proteins. Recent work has used bioinformatic and dynamical systems to provide remarkable insights into the topology and dynamics of intracellular information networks. Novel applications of Fisher-, Shannon-, and Kullback-Leibler informations are promoting increased understanding of the mechanisms by which genetic information is converted to work and order. Insights into evolution may be gained by analysis of the the fitness contributions from specific segments of genetic information as well as the optimization process in which the fitness are constrained by the substrate cost for its storage and utilization. Recent IT applications have recognized the possible role of nontraditional information storage structures including lipids and ion gradients as well as information transmission by molecular flux across cell membranes. Many fascinating challenges remain, including defining the intercellular information dynamics of multicellular organisms and the role of disordered information storage and flow in disease.
Among other interesting observations in it, he calls attention to the fact that, “according to the journal Nature, a third of all studies never even get cited, let alone repeated.”
For scholars of Fisher, Popper, and Kuhn, some of this discussion won’t be quite so novel, but for anyone designing scientific experiments, the effects discussed here are certainly worthy of notice and further study and scrutiny.
You know that automated machine language translation is not in good shape when the editor-in-chief of the IEEE’s Signal Processing Magazine says:
As an anecdote, during the early stage in creating the Chinese translation of the [Signal Processing] magazine, we experimented with automated machine translation first, only to quickly switch to professional human translation. This makes us appreciate why “universal translation” is the “needs and wants” of the future rather than of the present; see [3] for a long list of of future needs and wants to be enabled by signal processing technology.
From hiccups to wisdom teeth, our own bodies are worse off than most because of the differences between the wilderness in which we evolved and the modern world in which we live.
A short and interesting list of examples showing proof of our evolution.
The chessboard is the world; the pieces are the phenomena of the universe; the rules of the game are what we call the laws of Nature. The player on the other side is hidden from us. We know that his play is always fair, just, and patient. But also we know, to our cost, that he never overlooks a mistake or makes the smallest allowance for ignorance.
At TEDGlobal 2010, author Matt Ridley shows how, throughout history, the engine of human progress has been the meeting and mating of ideas to make new ideas. It's not important how clever individuals are, he says; what really matters is how smart the collective brain is.
When extrapolated a bit, this thesis is one of the best arguments for why Twitter and other methods of social media are so useful. There really is a great idea at the core of this presentation.
Finally, after 140 years, Robert Strain and Philip Gressman at the University of Pennsylvania have found a mathematical proof of Boltzmann’s equation, which predicts the motion of gas molecules.
Abstract
This is a brief announcement of our recent proof of global existence and rapid decay to equilibrium of classical solutions to the Boltzmann equation without any angular cutoff, that is, for long-range interactions. We consider perturbations of the Maxwellian equilibrium states and include the physical cross-sections arising from an inverse-power intermolecular potential r-(p-1) with p > 2, and more generally. We present here a mathematical framework for unique global in time solutions for all of these potentials. We consider it remarkable that this equation, derived by Boltzmann (1) in 1872 and Maxwell (2) in 1867, grants a basic example where a range of geometric fractional derivatives occur in a physical model of the natural world. Our methods provide a new understanding of the effects due to grazing collisions.
N. Krasnogor, D. A. Pelta (Bioinformatics. 2004 May 1;20(7):1015-21. Epub 2004 Jan 29.)