Nested sampling is a Bayesian sampling technique developed to explore probability distributions lo- calised in an exponentially small area of the parameter space. The algorithm provides both posterior samples and an estimate of the evidence (marginal likelihood) of the model. The nested sampling algo- rithm also provides an efficient way to calculate free energies and the expectation value of thermodynamic observables at any temperature, through a simple post-processing of the output. Previous applications of the algorithm have yielded large efficiency gains over other sampling techniques, including parallel tempering (replica exchange). In this paper we describe a parallel implementation of the nested sampling algorithm and its application to the problem of protein folding in a Go-type force field of empirical potentials that were designed to stabilize secondary structure elements in room-temperature simulations. We demonstrate the method by conducting folding simulations on a number of small proteins which are commonly used for testing protein folding procedures: protein G, the SH3 domain of Src tyrosine kinase and chymotrypsin inhibitor 2. A topological analysis of the posterior samples is performed to produce energy landscape charts, which give a high level description of the potential energy surface for the protein folding simulations. These charts provide qualitative insights into both the folding process and the nature of the model and force field used.

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schr"odinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.

Motivation: NMR spectra are widely used in metabolomics to obtain metabolite profiles in complex biological mixtures. Common methods used to assign and estimate concentrations of metabolite involve either an expert manual peak fitting or extra pre-processing steps, such as peak alignment and binning. Peak fitting is very time consuming and is subject to human error. Conversely, alignment and binning can introduce artifacts and limit immediate biological interpretation of models. Results: We present the Bayesian AuTomated Metabolite Analyser for NMR spectra (BATMAN), an R package which deconvolves peaks from 1-dimensional NMR spectra, automatically assigns them to specific metabolites and obtains concentration estimates. The Bayesian model incorporates information on characteristic peak patterns of metabolites and is able to account for shifts in the position of peaks commonly seen in NMR spectra of biological samples. It applies a Markov Chain Monte Carlo (MCMC) algorithm to sample from a joint posterior distribution of the model parameters and obtains concentration estimates with reduced mean estimation error compared with conventional numerical integration methods.

"Transitions between metastable conformations of a dipeptide are investigated using classical molecular dynamics simulation with explicit water molecules. The distribution of the surrounding water at different moments before the transitions and the dynamical correlations of water with the peptide's configurational motions indicate that water is the main driving force of the conformational changes."

"Cellular behavior is governed by gene regulatory processes that are intrinsically dynamic and nonlinear, and are subject to non-negligible amounts of random fluctuations. Such conditions are ubiquitous in physical systems, where they have been studied for decades using the tools of statistical and nonlinear physics. The goal of this review is to show how approaches traditionally used in physics can help in reaching a systems-level understanding of living cells. To that end, we present an overview of the dynamical phenomena exhibited by genetic circuits and their functional significance. We also describe the theoretical and experimental approaches that are being used to unravel the relationship between circuit structure and function in dynamical cellular processes under the influence of noise, both at the single-cell level and in cellular populations, where intercellular coupling plays an important role."

"How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous non-functional interactions between their proteins? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in crowded cellular environment. The model cell includes three independent pathways, whose topologies of PPI subnetworks are different, but whose functional concentrations equally contribute to cell's fitness. The model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a "frustration" effect: strengthening of functional interactions brings about hydrophobic surfaces, which make proteins prone to promiscuous binding.…"

"…Here we study the quantitative relation between adaptive response and background compensation within a modeling framework. In contrast to the commonly held view, we show that any particular type of adaptive response is neither sufficient nor necessary for adaptive enlargement of dynamic range. In particular a precise adaptive response, where system activity is maintained at a constant level at steady state, does not ensure a large dynamic range neither in input signal nor in system output. A general mechanism for input dynamic range enlargement comes about from the activity-dependent modulation of protein responsiveness by multiple biochemical modification, regardless of the type of adaptive response it induces. Therefore hierarchical biochemical processes such as methylation and phosphorylation are natural candidates to induce this property in signalling systems."

"A series of simulations aimed at elucidating the self-assembly dynamics of spherical virus capsids is described. This little-understood phenomenon is a fascinating example of the complex processes that occur in the simplest of organisms. The fact that different viruses adopt similar structural forms is an indication of a common underlying design, motivating the use of simplified, low-resolution models in exploring the assembly process. Several versions of a molecular dynamics approach are described. Polyhedral shells of different sizes are involved, the assembly pathways are either irreversible or reversible, and an explicit solvent is optionally included. …Among the key observations are that efficient growth proceeds by means of a cascade of highly reversible stages, and that while there are a large variety of possible partial assemblies, only a relatively small number of strongly bonded configurations are actually encountered."

ME: what would 'well-designed' biochemical nets look like, if you evolved them in silico?

"The reliability with which a network can transmit a particular frequency component of the input signal tra- jectory is determined by the gain-to-noise ratio of the net- work as a function of frequency. For systems that obey the spectral addition rule [32], that is those for which downstream reactions do not affect the input signal, the gain-to-noise ratio is an intrinsic property of the processing network. For networks that do not obey the spectral addition rule the gain-to-noise ratio will be dependent on the statistics of the input signal. The mutual information between input and output signals, which quantifies the information which can be transmitted about a particular input ensemble, also depends on the particular choice of the input signal.…"

"For understanding the origin of life, it is essential to explain the development of a compartmentalized structure, which undergoes growth and division, from a set of chemical reactions. In this study, a hypercycle with two chemicals that mutually catalyze each other is considered in order to show that the reproduction of a protocell with a growth-division process naturally occurs when the replication speed of one chemical is considerably slower than that of the other chemical. It is observed that the protocell divides after a minority molecule is replicated at a slow synthesis rate, and thus, a synchrony between the reproduction of a cell and molecule replication is achieved. The robustness of such protocells against the invasion of parasitic molecules is also demonstrated."

"…We analyze the Lyapunov spectrum, determine the probability to find stationary oscillating solutions, demonstrate the effect of the nonlinearity on the effective in- and out-degree of the active interaction network and study how the frequency distributions of oscillatory modes of such system depend on the average connectivity."

"The results of this study hold implications not only for the development of artificial photosynthesis systems as a renewable non-polluting source of electrical energy, but also for the future development of quantum-based technologies in areas such as computing – a quantum computer could perform certain operations thousands of times faster than any conventional computer."

The reinvention of the Block Diagram?