Method for inferring and extracting reliable genetic interactions from time-series profile of gene expression

Recent advances in technologies such as DNA microarrays have provided an abundance of gene expression data on the genomic scale. One of the most important projects in the post-genome-era is the systemic identification of gene expression networks. However, inferring internal gene expression structure from experimentally observed time-series data are an inverse problem. We have therefore developed a system for inferring network candidates based on experimental observations. Moreover, we have proposed an analytical method for extracting common core binomial genetic interactions from various network candidates. Common core binomial genetic interactions are reliable interactions with a higher possibility of existence, and are important for understanding the dynamic behavior of gene expression networks. Here, we discuss an efficient method for inferring genetic interactions that combines a Step-by-step strategy (Y. Maki, Y. Takahashi, Y. Arikawa, S. Watanabe, K. Aoshima, Y. Eguchi, T. Ueda, S. Aburatani, S. Kuhara, M. Okamoto, An integrated comprehensive workbench for inferring genetic networks: Voyagene, Journal of Bioinformatics and Computational Biology 2(3) (2004) 533.) with an analysis method for extracting common core binomial genetic interactions.     

Dynamical properties of model gene networks and implications for the inverse problem

We study the inverse problem, or the "reverse-engineering" problem, for two abstract models of gene expression dynamics, discrete-time Boolean networks and continuous-time switching networks. Formally, the inverse problem is similar for both types of networks. For each gene, its regulators and its Boolean dynamics function must be identified. However, differences in the dynamical properties of these two types of networks affect the amount of data that is necessary for solving the inverse problem. We derive estimates for the average amounts of time series data required to solve the inverse problem for randomly generated Boolean and continuous-time switching networks. We also derive a lower bound on the amount of data needed that holds for both types of networks. We find that the amount of data required is logarithmic in the number of genes for Boolean networks, matching the general lower bound and previous theory, but are superlinear in the number of genes for continuous-time switching networks. We also find that the amount of data needed scales as 2(K), where K is the number of regulators per gene, rather than 2(2K), as previous theory suggests.     

Distributed parameter identification for a label-structured cell population dynamics model using CFSE histogram time-series data

In this work we address the problem of the robust identification of unknown parameters of a cell population dynamics model from experimental data on the kinetics of cells labelled with a fluorescence marker defining the division age of the cell. The model is formulated by a first order hyperbolic PDE for the distribution of cells with respect to the structure variable x (or z) being the intensity level (or the log₁₀-transformed intensity level) of the marker. The parameters of the model are the rate functions of cell division, death, label decay and the label dilution factor. We develop a computational approach to the identification of the model parameters with a particular focus on the cell birth rate α(z) as a function of the marker intensity, assuming the other model parameters are scalars to be estimated. To solve the inverse problem numerically, we parameterize α(z) and apply a maximum likelihood approach. The parametrization is based on cubic Hermite splines defined on a coarse mesh with either equally spaced a priori fixed nodes or nodes to be determined in the parameter estimation procedure. Ill-posedness of the inverse problem is indicated by multiple minima. To treat the ill-posed problem, we apply Tikhonov regularization with the regularization parameter determined by the discrepancy principle. We show that the solution of the regularized parameter estimation problem is consistent with the data set with an accuracy within the noise level in the measurements.      

Association of Misexpression with Sterility in Hybrids of <Emphasis Type="Italic">Drosophila simulans</Emphasis>and <Emphasis Type="Italic">D. mauritiana</Emphasis>

Recent studies have identified genes associated with hybrid sterility and other hybrid dysfunctions, but the consequences of introgressions of these speciation genes are often poorly understood. Previously, we identified a panel of genes that are underexpressed in sterile male hybrids of Drosophila simulans and D. mauritiana relative to pure species. Here, we build on this reverse-genetics approach to demonstrate that the underexpression of at least five of these genes in hybrids is associated with hybrid sterility and that these five genes are coordinately regulated. We map one upstream regulator of these genes to a region previously shown to harbor one or more factors causing hybrid sterility. Finally, we show that the genes underexpressed in hybrids are often highly conserved, as might be predicted for downstream targets of the genetic changes that cause hybrid sterility. This approach integrates forward genetics with reverse genetics to show a proximate consequence of the introgression of particular hybrid sterility-conferring regions between species: underexpression of genes necessary for normal spermatogenesis.[Reviewing Editor: Martin Kreitman]     

A data-driven model of the generation of human EEG based on a spatially distributed stochastic wave equation

We discuss a model for the dynamics of the primary current density vector field within the grey matter of human brain. The model is based on a linear damped wave equation, driven by a stochastic term. By employing a realistically shaped average brain model and an estimate of the matrix which maps the primary currents distributed over grey matter to the electric potentials at the surface of the head, the model can be put into relation with recordings of the electroencephalogram (EEG). Through this step it becomes possible to employ EEG recordings for the purpose of estimating the primary current density vector field, i.e. finding a solution of the inverse problem of EEG generation. As a technique for inferring the unobserved high-dimensional primary current density field from EEG data of much lower dimension, a linear state space modelling approach is suggested, based on a generalisation of Kalman filtering, in combination with maximum-likelihood parameter estimation. The resulting algorithm for estimating dynamical solutions of the EEG inverse problem is applied to the task of localising the source of an epileptic spike from a clinical EEG data set; for comparison, we apply to the same task also a non-dynamical standard algorithm.     

Unveiling network-based functional features through integration of gene expression into protein networks

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype–phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.     

Early alterations in gene expression and cell morphology in a mouse model of Huntington's disease

Several mouse models for Huntington's disease (HD) have been produced to date. Based on differences in strain, promoter, construct, and number of glutamines, these models have provided a broad spectrum of neurological symptoms, ranging from simple increases in aggressiveness with no signs of neuropathology, to tremors and seizures in absence of degeneration, to neurological symptoms in the presence of gliosis and TUNEL (terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling) positivity, and finally to selective striatal damage associated with electrophysiological and behavioral abnormalities. We decided to analyze the morphology of striatum and hippocampus from a mouse transgenic line obtained by microinjection of exon 1 from the HD gene after introduction of a very high number of CAG repeat units. We found a massive darkening and compacting of striatal and hippocampal neurons in affected mice, associated with a lower degree of more classical apoptotic cell condensation. We then explored whether this morphology could be explained with alterations in gene expression by hybridizing normal and affected total brain RNA to a panel of 588 known mouse cDNAs. We show that some genes are significantly and consistently up-regulated and that others are down-regulated in the affected brains. Here we discuss the possible significance of these alterations in neuronal morphology and gene expression.     

Ameboid cell motility: a model and inverse problem, with an application to live cell imaging data

In this article a mathematical model for ameboid cell movement is developed using a spring-dashpot system with Newtonian dynamics. The model is based on the facts that the cytoskeleton plays a primary role for cell motility and that the cytoplasm is viscoelastic. Based on the model, the inverse problem can be posed: if a structure like a spring-dashpot system is embedded into the living cell, what kind of characteristic properties must the structure have in order to reproduce a given movement of the cell? This inverse problem is the primary topic of this paper. On one side the model mimics some features of the movement, and on the other side, the solution to the inverse problem provides model parameters that give some insight, principally into the mechanical aspect, but also, through qualitative reasoning, into chemical and biophysical aspects of the cell. Moreover, this analysis can be done locally or globally and in different media by using the simplest possible information: positions of the cell and nuclear membranes. It is shown that the model and solution to the inverse problem for simulated data sets are highly accurate. An application to a set of live cell imaging data obtained from random movements of a human brain tumor cell (U87-MG human glioblastoma cell line) then provides an example of the efficiency of the model, through the solution of its inverse problem, as a way of understanding experimental data.     

Intrinsic noise, gene regulation and steady-state statistics in a two-gene network

The intrinsic noise in a two-gene network model is analysed. The technique of the Fokker-Planck approximation is used to investigate the statistics of noise when the system state is near a stable equilibrium. This is called also the steady-state statistics. The relative size of noise is measured by the Fano factor that is defined as the ratio of the variance to the mean. Our main result shows that in general, the noise control in a two-gene network might be a very complicated process, but for the repressor-repressor system that is a very important case in investigating the genetic switch, the relative size of noise, i.e. the Fano factor, must be bigger than one for both the repressor proteins.