Modeling genetic circuit behavior in transiently transfected mammalian cells

Binning cells by plasmid copy number is a common practice for analyzing transient transfection data. In many kinetic models of transfected cells, protein production rates are assumed to be proportional to plasmid copy number. The validity of this assumption in transiently transfected mammalian cells is not clear; models based on this assumption appear unable to reproduce experimental flow cytometry data robustly. We hypothesize that protein saturation at high plasmid copy number is a reason previous models break down and validate our hypothesis by comparing experimental data and a stochastic chemical kinetics model. The model demonstrates that there are multiple distinct physical mechanisms that can cause saturation. On the basis of these observations, we develop a novel minimal bin-dependent ODE model that assumes different parameters for protein production in cells with low versus high numbers of plasmids. Compared to a traditional Hill-function-based model, the bin-dependent model requires only one additional parameter, but fits flow cytometry input-output data for individual modules up to twice as accurately. By composing together models of individually fit modules, we use the bin-dependent model to predict the behavior of six cascades and three feed-forward circuits. The bin-dependent models are shown to provide more accurate predictions on average than corresponding (composed) Hill-function-based models and predictions of comparable accuracy to EQuIP, while still providing a minimal ODE-based model that should be easy to integrate as a subcomponent within larger differential equation circuit models. Our analysis also demonstrates that accounting for batch effects is important in developing accurate composed models.Accepted manuscrip

Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

Network estimation in State Space Model with L1-regularization constraint

Biological networks have arisen as an attractive paradigm of genomic science ever since the introduction of large scale genomic technologies which carried the promise of elucidating the relationship in functional genomics. Microarray technologies coupled with appropriate mathematical or statistical models have made it possible to identify dynamic regulatory networks or to measure time course of the expression level of many genes simultaneously. However one of the few limitations fall on the high-dimensional nature of such data coupled with the fact that these gene expression data are known to include some hidden process. In that regards, we are concerned with deriving a method for inferring a sparse dynamic network in a high dimensional data setting. We assume that the observations are noisy measurements of gene expression in the form of mRNAs, whose dynamics can be described by some unknown or hidden process. We build an input-dependent linear state space model from these hidden states and demonstrate how an incorporated $L_{1}$ regularization constraint in an Expectation-Maximization (EM) algorithm can be used to reverse engineer transcriptional networks from gene expression profiling data. This corresponds to estimating the model interaction parameters. The proposed method is illustrated on time-course microarray data obtained from a well established T-cell data. At the optimum tuning parameters we found genes TRAF5, JUND, CDK4, CASP4, CD69, and C3X1 to have higher number of inwards directed connections and FYB, CCNA2, AKT1 and CASP8 to be genes with higher number of outwards directed connections. We recommend these genes to be object for further investigation. Caspase 4 is also found to activate the expression of JunD which in turn represses the cell cycle regulator CDC2.Comment: arXiv admin note: substantial text overlap with arXiv:1308.359