Reverse-engineering of biochemical reaction networks from spatio-temporal correlations of fluorescence fluctuations

Recent developments of fluorescence labeling and highly advanced microscopy techniques have enabled observations of activities of biosignaling molecules in living cells. The high spatial and temporal resolutions of these video microscopy experiments allow detection of fluorescence fluctuations at the timescales approaching those of enzymatic reactions. Such fluorescence fluctuation patterns may contain information about the complex reaction-diffusion system driving the dynamics of the labeled molecule. Here, we have developed a method of identifying the reaction-diffusion system of fluorescently labeled signaling molecules in the cell, by combining spatio-temporal correlation function analysis of fluctuating fluorescent patterns, stochastic reaction-diffusion simulations, and an iterative system identification technique using a simulated annealing algorithm. In this report, we discuss the validity and usability of spatio-temporal correlation functions in characterizing the reaction-diffusion dynamics of biomolecules, and demonstrate application of our reaction-diffusion system identification method to a simple conceptual model for small GTPase activation.     

Quantitative measurements in single-cell analysis: towards scalability in microbial bioprocess development

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Coordinating early kidney development: lessons from gene targeting

The kidney is widely used to study the mechanisms of organogenesis. Its development involves fundamental processes, such as epithelial branching, induced morphogenesis and cytodifferentiation, which are common to the development of many other organs. Gene-targeting experiments have greatly improved our understanding of kidney development, and have revealed many important genes that regulate early kidney organogenesis, some of which have a role in inherited human kidney disorders. Although our understanding of how the kidney is assembled is still limited, these studies are beginning to provide insights into the genetic and cellular interactions that regulate early organogenesis.     

The single-cell and spatial transcriptional landscape of human gastrulation and early brain development

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Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data

Single-cell RNA-seq (scRNA-seq) can be used to characterize cellular heterogeneity in thousands of cells. The reconstruction of a gene network based on coexpression patterns is a fundamental task in scRNA-seq analyses, and the mutual exclusivity of gene expression can be critical for understanding such heterogeneity. Here, we propose an approach for detecting communities from a genetic network constructed on the basis of coexpression properties. The community-based comparison of multiple coexpression networks enables the identification of functionally related gene clusters that cannot be fully captured through differential gene expression-based analysis. We also developed a novel metric referred to as the exclusively expressed index (EEI) that identifies mutually exclusive gene pairs from sparse scRNA-seq data. EEI quantifies and ranks the exclusive expression levels of all gene pairs from binary expression patterns while maintaining robustness against a low sequencing depth. We applied our methods to glioblastoma scRNA-seq data and found that gene communities were partially conserved after serum stimulation despite a considerable number of differentially expressed genes. We also demonstrate that the identification of mutually exclusive gene sets with EEI can improve the sensitivity of capturing cellular heterogeneity. Our methods complement existing approaches and provide new biological insights, even for a large, sparse dataset, in the single-cell analysis field.     

Least-squares methods for identifying biochemical regulatory networks from noisy measurements

Background  

We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS) estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS). The Total Least Squares (TLS) technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks.     

Novel topology of a zinc-binding domain from a protein involved in regulating early Xenopus development.

Xenopus nuclear factor XNF7, a maternally expressed protein, functions in patterning of the embryo. XNF7 contains a number of defined protein domains implicated in the regulation of some developmental processes. Among these is a tripartite motif comprising a zinc-binding RING finger and B-box domain next to a predicted alpha-helical coiled-coil domain. Interestingly, this motif is found in a variety of protein including several proto-oncoproteins. Here we describe the solution structure of the XNF7 B-box zinc-binding domain determined at physiological pH by 1H NMR methods. The B-box structure represents the first three-dimensional structure of this new motif and comprises a monomer have two beta-strands, two helical turns and three extended loop regions packed in a novel topology. The r.m.s. deviation for the best 18 structures is 1.15 A for backbone atoms and 1.94 A for all atoms. Structure calculations and biochemical data shows one zinc atom ligated in a Cys2-His2 tetrahedral arrangement. We have used mutant peptides to determine the metal ligation scheme which surprisingly shows that not all of the seven conserved cysteines/histidines in the B-box motif are involved in metal ligation. The B-box structure is not similar in tertiary fold to any other known zinc-binding motif.     

Integrated gene networks in breast cancer development

Breast cancer is a complex and heterogenous disease. Classical molecular medical approaches cannot fully understand and comprehend its pathogenesis. In this review, the development of new biological markers for the early detection and creation of guided and specific therapy of breast cancer are discussed in light of the rapid advances in the “omics”. Results of cancer research in combination with large-scale methods that examine the expression status of genes and proteins have identified a large number of new biomarkers as well as confirmed the human growth hormone as an important player in the pathogenesis of this disease through its autocrine regulation where it influences the activation of Pax5 and HOXA1 gene networks.     

Genome-wide analysis of signaling networks regulating fatty acid-induced gene expression and organelle biogenesis

Reversible phosphorylation is the most common posttranslational modification used in the regulation of cellular processes. This study of phosphatases and kinases required for peroxisome biogenesis is the first genome-wide analysis of phosphorylation events controlling organelle biogenesis. We evaluate signaling molecule deletion strains of the yeast Saccharomyces cerevisiae for presence of a green fluorescent protein chimera of peroxisomal thiolase, formation of peroxisomes, and peroxisome functionality. We find that distinct signaling networks involving glucose-mediated gene repression, derepression, oleate-mediated induction, and peroxisome formation promote stages of the biogenesis pathway. Additionally, separate classes of signaling proteins are responsible for the regulation of peroxisome number and size. These signaling networks specify the requirements of early and late events of peroxisome biogenesis. Among the numerous signaling proteins involved, Pho85p is exceptional, with functional involvements in both gene expression and peroxisome formation. Our study represents the first global study of signaling networks regulating the biogenesis of an organelle.     

Recursive regularization for inferring gene networks from time-course gene expression profiles

Background  

Inferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives.