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Computational tools for themodeling of protein structures as well as for structure-based protein design rely on models to quantify the respective molecular interactions from the atomic to the amino-acid residue levels. For this purpose, both physics-based models and statistical models, namely, models derived from statistics of experimentally determined protein sequence and structure data, have been used. For structure prediction tasks, especially for fold recognition, statist [Detail] ...
Statistical energy functions are general models about atomic or residue-level interactions in biomolecules, derived from existing experimental data. They provide quantitative foundations for structural modeling as well as for structure-based protein sequence design. Statistical energy functions can be derived computationally either based on statistical distributions or based on variational assumptions. We present overviews on the theoretical assumptions underlying the various types of approaches. Theoretical considerations underlying important pragmatic choices are discussed.
Gene expression is a complex biochemical process, involving many specific processes such as transcription, translation, switching between promoter states, and regulation. All these biochemical processes inevitably lead to fluctuations in mRNA and protein abundances. This noise has been identified as an important factor underlying the observed phenotypic variability of genetically identical cells in homogeneous environments. Quantifying the contributions of different sources of noise using stochastic models of gene expression is an important step towards understanding fundamental cellular processes and cell-to-cell variability in expression levels. In this paper, we review progresses in quantitative study of simple gene expression systems, including some results that we have not published. We analytically show how specific processes associated with gene expression affect expression levels. In particular, we derive the analytical decomposition of expression noise, which is important for understanding the roles of the factorial noise in controlling phenotypic variability. We also introduce a new index (called attribute factor) to quantify expression noise, which has more advantages than the commonly-used noise indices such as noise intensity and Fano factor.
It is widely believed that in Drosophila melanogaster the pattern of Bicoid protein gradient plays a crucial role in the segmentation stage of embryo development. As a result of its fundamental role, modelling the Bicoid gradient has become increasingly popular for researchers from many different areas of study. The aim of this paper is to bring together the most prominent studies on this maternal gene and discuss how existing techniques for modelling this gradient have evolved over the years.
Collective motion can be observed in biological systems over a wide range of length scales, from large animals to bacteria. Collective motion is thought to confer an advantage for defense and adaptation. A central question in the study of biological collective motion is how the traits of individuals give rise to the emergent behavior at population level. This question is relevant to the dynamics of general self-propelled particle systems, biological self-organization, and active fluids. Bacteria provide a tractable system to address this question, because bacteria are simple and their behavior is relatively easy to control. In this mini review we will focus on a special form of bacterial collective motion, i.e., bacterial swarming in two dimensions. We will introduce some organization principles known in bacterial swarming and discuss potential means of controlling its dynamics. The simplicity and controllability of 2D bacterial behavior during swarming would allow experimental examination of theory predictions on general collective motion.
