A machine learning and directed network optimization approach to uncover TP53 regulatory patterns.

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.

A new non-monotonic infeasible simplex-type algorithm for Linear Programming.

This paper presents a new simplex-type algorithm for Linear Programming with the following two main characteristics: (i) the algorithm computes basic solutions which are neither primal or dual feasible, nor monotonically improving and (ii) the sequence of these basic solutions is connected with a sequence of monotonically improving interior points to construct a feasible direction at each iteration. We compare the proposed algorithm with the state-of-the-art commercial CPLEX and Gurobi Primal-Simplex optimizers on a collection of 93 well known benchmarks. The results are promising, showing that the new algorithm competes versus the state-of-the-art solvers in the total number of iterations required to converge.

A Nexus Approach for Sustainable Urban Energy-Water-Waste Systems Planning and Operation.

Energy, water, and waste systems analyzed at a nexus level are important to move toward more sustainable cities. In this paper, the "resilience.io" platform is developed and applied to emphasize on waste-to-energy pathways, along with the water and energy sectors, aiming to develop waste treatment capacity and energy recovery with the lowest economic and environmental cost. Three categories of waste including wastewater (WW), municipal solid waste (MSW), and agriculture waste are tested as the feedstock for thermochemical treatment via incineration, gasification, or pyrolysis for combined heat and power generation, or biological treatment such as anaerobic digestion (AD) and aerobic treatment. A case study is presented for Ghana in sub-Saharan Africa, considering a combination of waste treatment technologies and infrastructure, depending on local characteristics for supply and demand. The results indicate that the biogas generated from waste treatment turns out to be a promising renewable energy source in the analyzed region, while more distributed energy resources can be integrated. A series of scenarios including the business-as-usual, base case, naturally constrained, policy interventions, and environmental and climate change impacts demonstrate how simulation with optimization models can provide new insights in the design of sustainable value chains, with particular emphasis on whole-system analysis and integration.

An integrated platform for intuitive mathematical programming modeling using LaTeX.

This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work.