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In many applications, such as those arising from the field of cellular networks, it is often desired to determine the interaction (graph) structure of a set of differential equations, using as data measured sensitivities. This note proposes an approach to this problem.
Two simple (rotator and one-particle) mechanistic models are suggested to describe simultaneously at a minimal level of sophistication two basic functions of F$_1$-ATPase: a motor regime driven by ATP hydrolysis and its inverted function as ATP synthesis. This description is consistent with the so-called rotary binding-change mechanism, a milestone of functioning ATP synthase, and uses a stepping (driving) function associated with two sequences of time instants, at which hydrolysis and synthesis reactions occur. It is useful to analyse experimental data and numerical simulations indeed predict corresponding dynamic behavior.
Despite the success and popularity of oligonucleotide arrays as a high-throughput technique for measuring mRNA expression levels, quantitative calibration studies have until now been limited. The main reason is that suitable data was not available. However, calibration data recently produced by Affymetrix now permits detailed studies of the intensity dependent sensitivity. Given a certain transcript concentration, it is of particular interest to know whether current analysis methods are capable of detecting differential expression ratios of 2 or higher. Using the calibration data, we demonstrate that while current techniques are capable of detecting changes in the low to mid concentration range, the situation is noticeably worse for high concentrations. In this regime, expression changes as large as 4 fold are severely biased, and changes of 2 are often undetectable. Such effects are mainly the consequence of the sequence specific binding properties of probes, and not the result of optical saturation in the fluorescence measurements. GeneChips are manufactured such that each transcript is probed by a set of sequences with a wide affinity range. We show that this property can be used to design a method capable of reducing the high intensity bias. The idea behind our methods is to transfers the weight of a measurement to a subset of probes with optimal linear response at a given concentration, which can be achieved using local embedding techniques.
During the germinal center reaction a characteristic morphology is developed. In the framework of a recently developed space-time-model for the germinal center a mechanism for the formation of dark and light zones has been proposed. The mechanism is based on a diffusing differentiation signal which is secerned by follicular dendritic cells. Here, we investigate a possible influence of recently found chemokines for the germinal center formation in the framework of a single-cell-based stochastic and discrete three-dimensional model. We will also consider alternative possible chemotactic pathways that may play a role for the development of both zones. Our results suggest that the centrocyte motility resulting from a follicular dendritic cell-derived chemokine has to exceed a lower limit to allow the separation of centroblasts and centrocytes. In contrast to light microscopy the dark zone is ring shaped. This suggests that FDC-derived chemoattractants alone cannot explain the typical germinal center morphology.
Understanding how growth induces form is a longstanding biological question. Many studies concentrated on the shapes of plant cells, fungi or bacteria. Some others have shown the importance of the mechanical properties of bacterial walls and plant tissues in pattern formation. Here I sketch a simple physical picture of cell growth. The study is focussed on isolated cells that have walls. They are modeled as thin elastic shells containing a liquid, which pressure drives the growth as generally admitted for bacteria or plant cells. Requiring mechanical equilibrium leads to estimations of typical cell sizes, in quantitative agreement with compiled data including bacteria, cochlear outer hair, fungi, yeast, root hair and giant alga cells.
The expression levels of many thousands of genes can be measured simultaneously by DNA microarrays (chips). This novel experimental tool has revolutionized research in molecular biology and generated considerable excitement. A typical experiment uses a few tens of such chips, each dedicated to a single sample - such as tissue extracted from a particular tumor. The results of such an experiment contain several hundred thousand numbers, that come in the form of a table, of several thousand rows (one for each gene) and 50 - 100 columns (one for each sample). We developed a clustering methodology to mine such data. In this review I provide a very basic introduction to the subject, aimed at a physics audience with no prior knowledge of either gene expression or clustering methods. I explain what genes are, what is gene expression and how it is measured by DNA chips. Next I explain what is meant by "clustering" and how we analyze the massive amounts of data from such experiments, and present results obtained from analysis of data obtained from colon cancer, brain tumors and breast cancer.
A sensitivity analysis of general stoichiometric networks is considered. The results are presented as a generalization of Metabolic Control Analysis, which has been concerned primarily with system sensitivities at steady state. An expression for time-varying sensitivity coefficients is given, and the Summation and Connectivity Theorems are generalized. The results are compared to previous treatments. The analysis is accompanied by a discussion of the computation of the sensitivity coefficients and an application to a model of phototransduction.
The paper puts forward an unusual prediction that cultivating a clone can curtail the lifespan of the clone donor. Neither the arrangement of this suggested empirical study nor the analyses of the anticipated outcomes rely on the accompanying theoretical contemplations. This prediction has come from the interpretation of the genome as a "barcode". The genome is considered as an identification label rather than a repository of control information, so living beings are portrayed as a community of users on the "Internet of the physical Universe". Thus, biological objects with identical (or nearly identical) DNA structures can interfere, and the surmised remote impact appears tangible. The effect of clone-donor interaction leads to a decisive Experimentum Crucis that can reject the common view on the organization of biological information processing. Exploitation of this effect can be potentially dangerous.
Single nucleotide polymorphisms (SNPs) often appear in clusters along the length of a chromosome. This is due to variation in local coalescent times caused by,for example, selection or recombination. Here we investigate whether recombination alone (within a neutral model) can cause statistically significant SNP clustering. We measure the extent of SNP clustering as the ratio between the variance of SNPs found in bins of length $l$, and the mean number of SNPs in such bins, $σ^2_l/μ_l$. For a uniform SNP distribution $σ^2_l/μ_l=1$, for clustered SNPs $σ^2_l/μ_l > 1$. Apart from the bin length, three length scales are important when accounting for SNP clustering: The mean distance between neighboring SNPs, $Δ$, the mean length of chromosome segments with constant time to the most recent common ancestor, $\el$, and the total length of the chromosome, $L$. We show that SNP clustering is observed if $Δ< \el \ll L$. Moreover, if $l\ll \el \ll L$, clustering becomes independent of the rate of recombination. We apply our results to the analysis of SNP data sets from mice, and human chromosomes 6 and X. Of the three data sets investigated, the human X chromosome displays the most significant deviation from neutrality.
A novel sensoric technique for the dynamical in situ investigation of a hybridization bio assay is presented, which utilizes a metal bead labeling method. Therein, hybridization results in an increased metal coverage on parts of a substrate, where it takes place. Our sensing principle relies on the measurement of the radio frequency impedance of the hybridization spots. We propose several examples for sensor devices.
As most biological species, photosynthetic lifeforms have evolved to function optimally, despite thermal disorder and with fault tolerance. It remains a challenge to understand how this is achieved. To address this challenge the function of the protein-pigment complex photosystem I (PSI) of the cyanobacterium Synechococcus elongatus is investigated theoretically. The recently obtained high resolution structure of this complex exhibits an aggregate of 96 chlorophylls that are electronically coupled to function as a light-harvesting antenna complex. This paper constructs an effective Hamiltonian for the chlorophyll aggregate to describe excitation transfer dynamics and spectral properties of PSI. For this purpose, a new kinetic expansion method, the sojourn expansion, is introduced. Our study shows that at room temperature fluctuations of site energies have little effect on the calculated excitation lifetime and quantum yield, which compare favorably with experimental results. The efficiency of the system is found to be robust against `pruning' of individual chlorophylls. An optimality of the arrangement of chlorophylls is identified through the quantum yield in comparison with an ensemble of randomly oriented chlorophylls, though, the quantum yield is seen to change only within a narrow interval in such an ensemble.
Photosystem I is a protein-pigment complex that performs photosynthesis in plants, green algae, and cyanobacteria. It contains an aggregate of chlorophylls that absorbs light and delivers the resulting electronic excitation to the special pair of chlorophylls where the excitation energy is used for producing charge separation across the cell membrane. The seemingly random arrangement of chlorophylls in photosystem I poses the question which pathways excitation migration follows towards the special pair after absorption of light at any of its chlorophylls. We employ a master equation to describe the process of excitation migration in photosystem I, and find representative paths of excitation migration based on the mean first-passage time from each chlorophyll to the special pair. The resulting paths, beginning at each of the chlorophylls and ending at the special pair, provide a complete yet distilled picture of the excitation migration towards the special pair.
In this work we introduce a new approach to Dynamical Monte Carlo methods to simulate markovian processes. We apply this approach to formulate and study an epidemic generalized SIRS model. The results are in excellent agreement with the fourth order Runge-Kutta method in a region of deterministic solution. Introducing local stochastic interactions, the Runge-Kutta method is no longer applicable. Thus, we solve the system described by a set of stochastic differential equations by a Dynamical Monte Carlo technique and check the solutions self-consistently with a stochastic version of the Euler method. We also analyzed the results under the herd-immunity concept.
We investigate a statistical model for multidimensional epistasis. The genotype is devided into subsequences, and within each subsequence mutations which occur in a prescribed order are beneficial. The bit-string model used to represent the genotype, may be cast in the form of a ferromagnetic Ising model with a staggered field. We obtain the actual correlations between mutations at different sites, within an equilibrium population at a given "tolerance," which we define to be the temperature of the statistical ensemble.
The origin of chirality, closely related to the evolution of life on the earth, has long been debated. In 1991, Abdus Salam suggested a novel approach to achieve biomolecular homochirality by a phase transition. In his subsequent publication, he predicted that this phase transition could eventually change D-amino acids to L-amino acids as C -H bond would break and H atom became a superconductive atom. Since many experiments denied the configuration change in amino acids, Salam hypothesis aroused suspicion. This paper is aimed to provide direct experimental evidence of a phase transition in alanine, valine single crystals but deny the configuration change of D- to L- enantiomers. New views on Salam phase transition are presented to revalidate its great importance in the origin of homochirality.
We discuss the ``soft-ratcheting'' algorithm which generates targeted stochastic trajectories in molecular systems with scores corresponding to their probabilities. The procedure, which requires no initial pathway guess, is capable of rapidly determining multiple pathways between known states. Monotonic progress toward the target state is not required. The soft-ratcheting algorithm is applied to an all-atom model of alanine dipeptide, whose unbiased trajectories are assumed to follow overdamped Langevin dynamics. All possible pathways on the two-dimensional dihedral surface are determined. The associated probability scores, though not optimally distributed at present, may provide a mechanism for estimating reaction rates.
We present a tentative interpretation of the origin of nematic liquid crystalline order exhibited by dragline silk fibroin solutions collected from the spider Nephila clavipes. Liquid crystallinity is thought to confer certain rheological properties on the fibroin solution which are exploited during the dragline spinning process. We show that the feasibility of liquid crystallinity under physiological conditions depends critically on parameters characterising the amino-acid sequence of the fibroin molecules.
We propose a simple model of recognition, short-term memory, long-term memory and learning.