No abstract available.

BACKGROUND: Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying networks associated with complex diseases and phenotypes. RESULTS: Here we introduce an integrated statistical framework to test network properties of single and multiple genesets under different interaction models. We implemented this framework as an open-source software, called Python Geneset Network Analysis (PyGNA). Our software is designed for easy integration into existing analysis pipelines and to generate high quality figures and reports. We also developed PyGNA to take advantage of multi-core systems to generate calibrated null distributions on large datasets. We then present the results of extensive benchmarking of the tests implemented in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrated to study biological networks. PyGNA is available at http://github.com/stracquadaniolab/pygna and can be easily installed using the PyPi or Anaconda package managers, and Docker. CONCLUSIONS: We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.

Synthetic biology has become a widely used technology, and expanding applications in research, education and industry require progress tracking for team-based DNA synthesis projects. Although some vendors are beginning to supply multi-kilobase sequence-verified constructs, synthesis workflows starting with short oligos remain important for cost savings and pedagogical benefit. We developed BioPartsDB as an open source, extendable workflow management system for synthetic biology projects with entry points for oligos and larger DNA constructs and ending with sequence-verified clones. AVAILABILITY AND IMPLEMENTATION: BioPartsDB is released under the MIT license and available for download at https://github.com/baderzone/biopartsdb Additional documentation and video tutorials are available at https://github.com/baderzone/biopartsdb/wiki An Amazon Web Services image is available from the AWS Market Place (ami-a01d07c8). CONTACT: [email protected].

UNLABELLED: Combinatorial assembly of DNA elements is an efficient method for building large-scale synthetic pathways from standardized, reusable components. These methods are particularly useful because they enable assembly of multiple DNA fragments in one reaction, at the cost of requiring that each fragment satisfies design constraints. We developed BioPartsBuilder as a biologist-friendly web tool to design biological parts that are compatible with DNA combinatorial assembly methods, such as Golden Gate and related methods. It retrieves biological sequences, enforces compliance with assembly design standards and provides a fabrication plan for each fragment. AVAILABILITY AND IMPLEMENTATION: BioPartsBuilder is accessible at http://public.biopartsbuilder.org and an Amazon Web Services image is available from the AWS Market Place (AMI ID: ami-508acf38). Source code is released under the MIT license, and available for download at https://github.com/baderzone/biopartsbuilder CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper we investigate plant photosynthesis and microbial fuel cells. We report the following: 1) we introduce and validate a novel multi-objective optimization algorithm, PMO2; 2) in photosynthesis we increase the yield of 135%, while in Geobacter sulfurreducens we determine the tradeoff for growth versus redox properties; 3) finally, we discuss Pareto-Front as an estimator of robust metabolic pathways.

We have studied the $\mathbf{C_3}$ photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the $\mathbf{C_3}$ photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model. (3) Maximize photosynthetic productivity rate through the choice of robust enzyme concentrations using a new precise definition of robustness. (4) Modeling photosynthetic carbon metabolism as a multi-objective problem of two competing biological selection pressures: light-saturated photosynthetic rate versus total protein-nitrogen requirement. Using the designed single-objective optimization algorithms, PAO and A-CMA-ES, we have obtained an increase in photosynthetic productivity of the $\mathbf{135\%}$ from 15.486 $\mathbf{\mu mol~m^{-2}s^{-1}}$ to $\mathbf{36.382~\mu mol~m ^{-2}s^{-1}}$, and improving the previous best-found photosynthetic productivity value ($\mathbf{27.261}$ $\mathbf{\mu mol~m ^{-2}s^{-1}}$, $\mathbf{76\%}$ of enhancement). Optimized enzyme concentrations express a maximal local robustness ($\mathbf{100\%}$) and a high global robustness ($\mathbf{97.2\%}$), satisfactory properties for a possible ``in vitro'' manufacturing of the optimized pathway. Morris sensitivity analysis shows that 11 enzymes over 23 are high sensitive enzymes, i.e., the most influential enzymes of the carbon metabolism model. Finally, we have obtained the trade-off between the maximization of the leaf $\mathbf{CO_2}$ uptake rate and the minimization of the total protein-nitrogen concentration. This trade-off search has been carried out for the three $\mathbf{c_i}$ concentrations referring to the estimate of $\mathbf{CO_2}$ concentration in the atmosphere characteristic of 25 million years ago, nowadays and in 2100 a.C. Remarkably, the three Pareto frontiers identify the highest photosynthetic productivity rates together with the fewest protein-nitrogen usage.

The concept of information gain has been adopted as tool to study the effectiveness of population-based optimizers; using this approach, it is possible to infer convergence properties for population-based optimizers. The experimental results have shown characteristic phase transition between exploration and exploitation phase during the evolutionary process and, moreover, the evidence that gain maximization offers a robust theoretical framework to study the convergence of stochastic optimizers.

Designing semiconductor devices is a complex problem, which requires accurate models and effective optimizers. We have introduced a new methodology based on derivative-free optimization evolutionary algorithms, which aim to achieve a near-optimal design. We have tested these algorithms on various semiconductor devices using a drift-diffusion model; the experimental results show that CMA-ES and DE outperform the current state-of-the-art design algorithms.

A crucial task in designing semiconductor devices is to provide a doping profile that assures specific electrical properties. This research work is focused in redesigning doping profiles of semiconductor devices in order to obtain an increased output current; however, large doping levels can degenerate the devices and hence a trade-off between doping profile deviation and output current should be found. The doping profile optimization in semiconductor has been tackled as a multi-objective optimization problem using the Non-dominated Sorting Genetic Algorithm (NSGA-II). We focus on silicon diodes and MOSFET devices; firstly, we redesign the doping profile of diodes in order to obtain a trade-off between doping profile deviation and output current. Secondly, we find a trade-off between current and temperature for a MOSFET device. The experimental results confirm the effectiveness of the proposed approach to face this class of problems in electronic design automation.