MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS: The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION: The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Patient-specific biodegradable grafts target to enhance surgical repairs of complex congenital heart defects (CHD). This study reports the design, simulation, and creation of bifurcated right ventricle-pulmonary artery (RVPA) conduit grafts for patients with CHD. The original right ventricle outflow tract and RVPA conduit-anatomies of two patients (n=2) who previously underwent Rastelli type surgical repair for their CHD were created using medical image segmentation software based on magnetic resonance imaging data. The pulsatile RVPA flow was simulated utilizing computational fluid dynamics (CFD) to calculate important hemodynamic parameters. The re-designed RVPA geometries for the patients were created by varying the radius and angle of the pulmonary artery bifurcation. The wall shear stress and power loss results of the re-designed RVPA models were compared to identify the best performing graft. The hemodynamic results demonstrated that the designed optimized grafts outperformed the original grafts. To test the feasibility of designed grafts in vivo, the bifurcated RVPA conduit of a pig was manufactured using a 3D printed mandrel and electrospinning technique before the implantation. The implanted graft allowed new tissue formation within weeks. The results of our study and simulations provide an insight into the creation of optimal performing tissue-engineered bifurcated grafts for the patients with CHD in the surgical planning process. Integration of flow simulations to support design and electrospinning technique to manufacture patient-specific biodegradable grafts has the potential to improve surgical outcomes in CHD.