This is definitely the quote of the week:
This is definitely the quote of the week:
A new study by Noam Sobel, of the Olfaction Research Group at the Weizmann Institute of Science in Rehovot, Israel, and others are reporting in the journal Science this week that men in their study who sniffed the tears of crying women produced less testosterone and found female faces less arousing.
Previous studies in animals such as mice and mole rats have shown that tears convey important chemical messages which are used to attract or repel others of the same species. There is good evidence for an interesting type means of higher-level chemical communication. These previous studies also incidentally show that “emotional” tears are chemically distinct from “eye-protecting” types of tears.
Scientific American’s “60 Second Science” (via link or listen below) podcast has a good audio overview of the study for those without the time to read the paper.
In press reports, Adam Anderson, a University of Toronto psychologist who was not involved with the study, posited that the results may imply that “tears have some influence on sexual selection, and that’s not something we associate with sadness.” He continued, “It could be a way of warding off unwanted advances.”
This study provides a new hypothesis for the evolution of crying in humans. (Now if only we could find some better reasons for laughter…)
The take home message may be that guys should not take their dates out to weepy chick flicks, or alternately women reluctantly accepting “pity dates” should force their suitors to exactly these types of testosterone damping films.
The transmission of genomic information from coding sequence to protein structure during protein synthesis is subject to stochastic errors. To analyze transmission limits in the presence of spurious errors, Shannon's noisy channel theorem is applied to a communication channel between amino acid sequences and their structures established from a large-scale statistical analysis of protein atomic coordinates. While Shannon's theorem confirms that in close to native conformations information is transmitted with limited error probability, additional random errors in sequence (amino acid substitutions) and in structure (structural defects) trigger a decrease in communication capacity toward a Shannon limit at 0.010 bits per amino acid symbol at which communication breaks down. In several controls, simulated error rates above a critical threshold and models of unfolded structures always produce capacities below this limiting value. Thus an essential biological system can be realistically modeled as a digital communication channel that is (a) sensitive to random errors and (b) restricted by a Shannon error limit. This forms a novel basis for predictions consistent with observed rates of defective ribosomal products during protein synthesis, and with the estimated excess of mutual information in protein contact potentials.
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.
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.
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.
PMID: 14751983 DOI: 10.1093/bioinformatics/bth031
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.
PMID: 17083004 DOI: 10.1007/s11538-006-9141-5