Changes in Gene Expression in Space and Time Orchestrate Environmentally Mediated Shaping of Root Architecture.

Shaping of root architecture is a quintessential developmental response that involves the concerted action of many different cell types, is highly dynamic, and underpins root plasticity. To determine to what extent the environmental regulation of lateral root development is a product of cell-type preferential activities, we tracked transcriptomic responses to two different treatments that both change root development in at an unprecedented level of temporal detail. We found that individual transcripts are expressed with a very high degree of temporal and spatial specificity, yet biological processes are commonly regulated, in a mechanism we term response nonredundancy.

Inferring transcriptional logic from multiple dynamic experiments.

Motivation: The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable.

Results: We propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcription factors (TFs) regulate the transcriptional process of a gene of interest. Our dynamic model links the mRNA transcription rate of the target gene to the activation states of the TFs assuming that these interactions are consistent across multiple experiments and over time.

ReTrOS: a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data.

Background: Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to researchers. The ReTrOS toolbox (Reconstructing Transcription Open Software) provides MATLAB-based implementations of two related methods, namely ReTrOS-Smooth and ReTrOS-Switch, for reconstructing the temporal transcriptional activity profile of a gene from given mRNA expression time series or protein reporter time series. The methods are based on fitting a differential equation model incorporating the processes of transcription, translation and degradation.

Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000.

Transcriptional reprogramming is integral to effective plant defense. Pathogen effectors act transcriptionally and posttranscriptionally to suppress defense responses. A major challenge to understanding disease and defense responses is discriminating between transcriptional reprogramming associated with microbial-associated molecular pattern (MAMP)-triggered immunity (MTI) and that orchestrated by effectors.

Wigwams: identifying gene modules co-regulated across multiple biological conditions.

Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g.

A temporal switch model for estimating transcriptional activity in gene expression.

Motivation: The analysis and mechanistic modelling of time series gene expression data provided by techniques such as microarrays, NanoString, reverse transcription-polymerase chain reaction and advanced sequencing are invaluable for developing an understanding of the variation in key biological processes. We address this by proposing the estimation of a flexible dynamic model, which decouples temporal synthesis and degradation of mRNA and, hence, allows for transcriptional activity to switch between different states.

Results: The model is flexible enough to capture a variety of observed transcriptional dynamics, including oscillatory behaviour, in a way that is compatible with the demands imposed by the quality, time-resolution and quantity of the data.

Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis.

Transcriptional reprogramming forms a major part of a plant's response to pathogen infection. Many individual components and pathways operating during plant defense have been identified, but our knowledge of how these different components interact is still rudimentary. We generated a high-resolution time series of gene expression profiles from a single Arabidopsis thaliana leaf during infection by the necrotrophic fungal pathogen Botrytis cinerea.

Evolution of resource and energy management in biologically realistic gene regulatory network models.

We describe the use of computational models of evolution of artificial gene regulatory networks to understand the topologies of biological gene regulatory networks. We summarize results from three complementary approaches that explicitly represent biological processes of transcription, translation, metabolism and gene regulation: a fine-grained model that allows detailed molecular interactions, a coarse-grained model that allows rapid evolution of many generations, and a fixed-architecture model that allows for comparison of different hypotheses. In the first two cases, we are able to evolve networks towards the biological fitness objectives of survival and reproduction.

High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation.

Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a high-resolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence.

De novo evolution of complex, global and hierarchical gene regulatory mechanisms.

Gene regulatory networks exhibit complex, hierarchical features such as global regulation and network motifs. There is much debate about whether the evolutionary origins of such features are the results of adaptation, or the by-products of non-adaptive processes of DNA replication. The lack of availability of gene regulatory networks of ancestor species on evolutionary timescales makes this a particularly difficult problem to resolve.

Stochasticity versus determinism: consequences for realistic gene regulatory network modelling and evolution.

Gene regulation is one important mechanism in producing observed phenotypes and heterogeneity. Consequently, the study of gene regulatory network (GRN) architecture, function and evolution now forms a major part of modern biology. However, it is impossible to experimentally observe the evolution of GRNs on the timescales on which living species evolve.

A new model for investigating the evolution of transcription control networks.

Biological systems show unbounded capacity for complex behaviors and responses to their environments. This principally arises from their genetic networks. The processes governing transcription, translation, and gene regulation are well understood, as are the mechanisms of network evolution, such as gene duplication and horizontal gene transfer.

Strong negative self regulation of prokaryotic transcription factors increases the intrinsic noise of protein expression.

Background: Many prokaryotic transcription factors repress their own transcription. It is often asserted that such regulation enables a cell to homeostatically maintain protein abundance. We explore the role of negative self regulation of transcription in regulating the variability of protein abundance using a variety of stochastic modeling techniques.