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Biological investigation is costly and discovery is not necessarily proportional to effort.In sampling experiments such as high throughput sequencing, there is a natural decreasing rate of discovery as a function of sampling effort since with increased sampling it is increasingly more likely to observe a previous discovery.Species accumulation curves are a classical method for modeling the rate of discovery as a function of sampling effort but have previously been limited to [Detail] ...
The cis-acting regulatory elements, e.g., promoters and ribosome binding sites (RBSs) with various desired properties, are building blocks widely used in synthetic biology for fine tuning gene expression. In the last decade, acquisition of a controllable regulatory element from a random library has been established and applied to control the protein expression and metabolic flux in different chassis cells. However, more rational strategies are still urgently needed to improve the efficiency and reduce the laborious screening and multifaceted characterizations. Building precise computational models that can predict the activity of regulatory elements and quantitatively design elements with desired strength have been demonstrated tremendous potentiality. Here, recent progress on construction of cis-acting regulatory element library and the quantitative predicting models for design of such elements are reviewed and discussed in detail.
Transcription factors (TFs) are major modulators of transcription and subsequent cellular processes. The binding of TFs to specific regulatory elements is governed by their specificity. Considering the gap between known TFs sequence and specificity, specificity prediction frameworks are highly desired. Key inputs to such frameworks are protein residues that modulate the specificity of TF under consideration. Simple measures like mutual information (MI) to delineate specificity influencing residues (SIRs) from alignment fail due to structural constraints imposed by the three-dimensional structure of protein. Structural restraints on the evolution of the amino-acid sequence lead to identification of false SIRs. In this manuscript we extended three methods (direct information, PSICOV and adjusted mutual information) that have been used to disentangle spurious indirect protein residue-residue contacts from direct contacts, to identify SIRs from joint alignments of amino-acids and specificity. We predicted SIRs for homeodomain (HD), helix-loop-helix, LacI and GntR families of TFs using these methods and compared to MI. Using various measures, we show that the performance of these three methods is comparable but better than MI. Implication of these methods in specificity prediction framework is discussed. The methods are implemented as an R package and available along with the alignments at http://stormo.wustl.edu/SpecPred.
Studying the molecular mechanisms that underlie the relationship between drugs and the side effects they produce is critical for drug discovery and drug development. Currently, however, computational methods are still unavailable to assess drug-protein interactions with the aim of globally inferring the contributions of various classes of proteins toward the etiology of side effects. In this work, we integrated data reflecting drug-side effect relationships, drug-target relationships, and protein-protein interactions to develop a novel network-based probabilistic model, SidePro, to evaluate the contributions of proteins toward the etiology of side effects. For a given side effect, the method applies an expectation---maximization algorithm and a diffusion kernel-based approach to estimate each protein’s contribution. We applied this method to a wide range of side effects and validated the results using cross-validation and records from the Side Effect Resource database. We also studied a specific side effect, nephrotoxicity, which is known to be associated with the irrational use of the Chinese herbal compound triptolide, a diterpenoid epoxide in the Thunder of God Vine, <?A3B2 tf="Times New Roman Bold Italic (TrueType)"?>Tripterygium wilfordii Lei-Gong-Teng. Using triptolide as an example, we scored the target proteins of triptolide using our model and investigated the high-scoring proteins and their related biological processes. The results demonstrated that our model could differentiate between the potential side effect targets and therapeutic targets of triptolide. Overall, the proposed model could accurately pinpoint the molecular mechanisms of drug side effects, thus making contribution to safe and effective drug development.
The species accumulation curve, or collector’s curve, of a population gives the expected number of observed species or distinct classes as a function of sampling effort. Species accumulation curves allow researchers to assess and compare diversity across populations or to evaluate the benefits of additional sampling. Traditional applications have focused on ecological populations but emerging large-scale applications, for example in DNA sequencing, are orders of magnitude larger and present new challenges. We developed a method to estimate accumulation curves for predicting the complexity of DNA sequencing libraries. This method uses rational function approximations to a classical non-parametric empirical Bayes estimator due to Good and Toulmin [Biometrika, 1956, 43, 45–63]. Here we demonstrate how the same approach can be highly effective in other large-scale applications involving biological data sets. These include estimating microbial species richness, immune repertoire size, and k-mer diversity for genome assembly applications. We show how the method can be modified to address populations containing an effectively infinite number of species where saturation cannot practically be attained. We also introduce a flexible suite of tools implemented as an R package that make these methods broadly accessible.
Cortical spreading depression (CSD) is an important experimental model for diseases such as stroke, epilepsy and migraine. Previous observations indicated that the amplitude and velocity of the typical direct current potential shift during repetitive CSD waves were varying. The recovery state of the tissue was found related with the variation of successive CSD waves. A computational model in this paper aimed to investigate the role of relative refractory period of CSD. This model simulated that continuous injection of KCl solution induced repetitive CSD waves. The first CSD wave often had a larger amplitude and faster velocity than those of the succeeding secondary waves. The relative refractory period lasted much longer than the recovery of ions turbulence. If the induction interval was long enough for recovery, a series of CSD waves would have the same profile as the first one. In the relative refractory period, an early stimulation might lead to a late initiation of CSD, i.e., “haste makes waste”. The amplitude and velocity of CSD waves were found increasing with the initiation interval and asymptotic to those of the first CSD wave. This study verified that the propagation dynamics of CSD waves is modulated by the relative refractory period. It suggested that the refractory period is critical for preventing undesirable CSD waves.
