Efficient parameter search for qualitative models of regulatory networks using symbolic model checking

Investigating the relation between the structure and behavior of complex biological networks often involves posing the following two questions: Is a hypothesized structure of a regulatory network consistent with the observed behavior? And can a proposed structure generate a desired behavior? Answering these questions presupposes that we are able to test the compatibility of network structure and behavior. We cast these questions into a parameter search problem for qualitative models of regulatory networks, in particular piecewise-affine differential equation models. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark experimental data sets. We test the consistency between the IRMA network structure and the time-series data, and search for parameter modifications that would improve the robustness of the external control of the system behavior

A yeast synthetic network for in-vivo assessment of reverse engineering and modelling.

Systems biology approaches are extensively used to model and reverse engineer gene regulatory networks from experimental data. Conversely, synthetic biology allows ‘‘de novo’’ construction of a regulatory network to seed new functions in the cell. At present, the usefulness and predictive ability of modeling and reverse engineering cannot be assessed and compared rigorously. We built in the yeast Saccharomyces cerevisiae a synthetic network, IRMA, for in vivo ‘‘benchmarking’’ of reverse-engineering and modeling approaches. The network is composed of five genes regulating each other through a variety of regulatory interactions; it is negligibly affected by endogenous genes, and it is responsive to small molecules. We measured time series and steady-state expression data after multiple perturbations. These data were used to assess state-of-the-art modeling and reverse-engi- neering techniques. A semiquantitative model was able to capture and predict the behavior of the network. Reverse engineering based on differential equations and Bayesian networks correctly inferred regulatory interactions from the experimental data

A yeast synthetic network for In-vivo Reverse-engineering and Modelling Assessment (IRMA)

Systems Biology approaches aim to reconstruct gene regulatory networks from experimental data. Conversely, Synthetic Biology aims at using mathematical models to design novel biological ‘circuits’ (synthetic networks) in order to seed new functions inside the cell. These disciplines require quantitative mathematical models and reverse-engineering techniques. A plethora of modelling strategies and reverse-engineering approaches has being proposed during the last years. Even if successful applications have being demonstrated, at present their usefulness and predictive ability cannot still be assessed and compared rigorously. There is the pressing and yet unsatisfied need for a ‘benchmark’: a perfectly known biological circuit that can be used to evaluate pro and cons of such techniques when applied at in vivo networks. In order to address this aim, we constructed in the simplest eukaryotic organism, the yeast Saccharomyces cerevisiae, a novel synthetic network for In-vivo Reverse-engineering and Modelling Assessment (IRMA). IRMA is composed of five well-studied genes that have been assembled to regulate each other in such a way to include a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. It was designed to be isolated from the cellular environment, and to respond to galactose by triggering transcription of its genes. To demonstrate that IRMA is a unique resource to validate the System and Synthetic biology approaches, we analysed the transcriptional response of IRMA genes following two different perturbation strategies: by performing a single perturbation and measuring mRNA changes at different time points, or by performing multiple perturbations and collecting mRNA measurements at steady state. We used these data as a ‘gold standard’ to assess either the predictive ability of mathematical modelling based on differential equations and, to compare four well-established reverse engineering algorithms, NIR, TSNI, BANJO and ARACNE. We thus showed the usefulness of IRMA as the first simplified model of eukaryotic gene networks built “ad hoc” to test the power of network modelling and reverse-engineering strategies

Reversal of X chromosome inactivation: lessons from pluripotent reprogramming of mouse and human somatic cells

X chromosome inactivation (XCI) is a strategy used by mammals to silence genes along one of the two female X chromosomes and equilibrate expression dosage between XY males and XX females. This epigenetically-inherited silencing is established during early embryonic development and maintained thereafter through cell divisions. Seeding of multiple repressive epigenetic marks along the inactive X chromosome (Xi) makes inactivation extremely robust and difficult to reverse upon single genetic perturbations. Reversal of XCI has, however, been observed when somatic cells are reprogrammed towards pluripotency, and in vitro reprogramming techniques have been used in recent years to dissect Xi gene reactivation mechanisms. These studies pave the way for developing novel therapeutic approaches for X-linked diseases. Here, the author reviews Xi reactivation during pluripotent reprogramming of mouse and human somatic cells, highlight recent advances and species-specific differences, and discuss the relevance for human diseases

Replication Timing of Gene Loci in Different Cell Cycle Phases

: Replication of distinct genomic loci occurs at different times during cell cycle. The replication timing correlates with chromatin status, three-dimensional folding, and transcriptional potential of the genes. In particular, active genes tend to replicate early in S phase, whereas inactive replicate late. In embryonic stem cells, some early replicating genes are not yet transcribed reflecting their potential to be transcribed upon differentiation. Here, I describe a method for evaluating the proportion of gene loci that is replicated in different phases of cell cycle thus reflecting the replication timing

Nascent RNA-FISH of X-linked genes in human fibroblast clones

Image examples of nascent RNA-FISH for HDAC8, WWC3 and RP11-706O15.1 in human fibroblast clones derived from the same female. The analysis is reported in Genome Biology paper " <p>Allele-specific analysis of cell fusion-mediated pluripotent reprograming reveals distinct and predictive susceptibilities of human X-linked genes to reactivation" authored by Irene Cantone, Gopuraja Dharmalingam, Yi-Wah Chan, Anne-Celine Kohler, Boris Lenhard, Matthias Merkenschlager and Amanda G Fisher</p

Allele-specific Taqman analysis of human X linked genes during cell fusion-mediated pluripotent reprogramming.xlsx

Allele-specific Taqman analysis of a subset of X-linked genes in hF clones and upon mESC fusion-mediated reprogramming.<div>Details of the dataset and figure plots are reported in Genome Biology paper entitled "Allele-specific analysis of cell fusion-mediated pluripotent reprograming reveals distinct and predictive susceptibilities of human X-linked genes to reactivation" authored by Irene Cantone, Gopuraja Dharmalingam, Yi-Wah Chan, Anne-Celine Kohler, Boris Lenhard, Matthias Merkenschlager and Amanda G Fisher</div> <p> </p