Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets. Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses. Availability: The methods outlined in this article have been implemented in Matlab and are available on request

Gateway vectors for efficient artificial gene assembly in vitro and expression in yeast Saccharomyces cerevisiae

Peer reviewedPublisher PD

Developmental biology of wood formation

The wood-forming vascular cambium is responsible for the production of a large part of the biomass on this planet. Yet, there is only limited knowledge on how cell proliferation and differentiation in the cambial meristem are regulated. In this thesis the wood-forming tissues of aspen were used as a model system to identify and characterize molecular factors related to cambial meristem activity. An important regulator of cambial meristem activity is the plant hormone auxin. As polar transport is crucial for the delivery of auxin to the cambial zone, we identified homologues of known regulators of polar auxin transport and described their regulation by environmental and developmental factors. Translating changes in auxin concentration into changes in gene expression involves members of the Aux/IAA gene family. Aspen homologues of Aux/IAA genes were cloned and found to be expressed in a highly tissue-specific fashion, which is further influenced by developmental events and changes in the environment. A major response of trees to environmental changes is the suspension of meristematic growth during winter dormancy. A comparison of gene expression in active and dormant cambia revealed dramatic changes in the transcriptome including the expression of many cold and stress related genes during winter. During the process of wood formation, cells originating in the vascular cambium go through an elaborate process of cell division, cell expansion, secondary wall formation and programmed cell death. Large-scale analysis of gene expression was used to create transcriptional maps of the differentiation process. This extensive dataset allowed us to confirm the proposed functions of various genes involved in wood formation, assign other known genes to specific stages along the developmental gradient and identify a large number of novel potential regulators of wood formation. The data further suggest that the cambial meristem shares regulatory mechanisms with other meristems in addition to its own, specific factors

Altered expression of maize PLASTOCHRON1 enhances biomass and seed yield by extending cell division duration

Maize is the highest yielding cereal crop grown worldwide for grain or silage. Here, we show that modulating the expression of the maize PLASTOCHRON1 (ZmPLA1) gene, encoding a cytochrome P450 (CYP78A1), results in increased organ growth, seedling vigour, stover biomass and seed yield. The engineered trait is robust as it improves yield in an inbred as well as in a panel of hybrids, at several locations and over multiple seasons in the field. Transcriptome studies, hormone measurements and the expression of the auxin responsive DR5(rev): mRFPer marker suggest that PLA1 may function through an increase in auxin. Detailed analysis of growth over time demonstrates that PLA1 stimulates the duration of leaf elongation by maintaining dividing cells in a proliferative, undifferentiated state for a longer period of time. The prolonged duration of growth also compensates for growth rate reduction caused by abiotic stresses

A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks

This paper proposes a new method to reverse engineer gene regulatory networks from experimental data. The modeling framework used is time-discrete deterministic dynamical systems, with a finite set of states for each of the variables. The simplest examples of such models are Boolean networks, in which variables have only two possible states. The use of a larger number of possible states allows a finer discretization of experimental data and more than one possible mode of action for the variables, depending on threshold values. Furthermore, with a suitable choice of state set, one can employ powerful tools from computational algebra, that underlie the reverse-engineering algorithm, avoiding costly enumeration strategies. To perform well, the algorithm requires wildtype together with perturbation time courses. This makes it suitable for small to meso-scale networks rather than networks on a genome-wide scale. The complexity of the algorithm is quadratic in the number of variables and cubic in the number of time points. The algorithm is validated on a recently published Boolean network model of segment polarity development in Drosophila melanogaster.Comment: 28 pages, 5 EPS figures, uses elsart.cl