Robust Optimal Metabolic Factories.

Perhaps the most fundamental model in synthetic and systems biology for inferring pathways in metabolic reaction networks is a metabolic : a system of reactions that starts from a set of source compounds and produces a set of target molecules, while conserving or not depleting intermediate metabolites. Finding a shortest factory-that minimizes a sum of real-valued weights on its reactions to infer the most likely pathway-is NP-complete. The current state-of-the-art for shortest factories solves a mixed-integer linear program with a major drawback: it requires the user to set a critical parameter, where too large a value can make optimal solutions infeasible, while too small a value can yield degenerate solutions due to numerical error.

Shortest Hyperpaths in Directed Hypergraphs for Reaction Pathway Inference.

Mmunin.

Computing optimal factories in metabolic networks with negative regulation.

Motivation: A factory in a metabolic network specifies how to produce target molecules from source compounds through biochemical reactions, properly accounting for reaction stoichiometry to conserve or not deplete intermediate metabolites. While finding factories is a fundamental problem in systems biology, available methods do not consider the number of reactions used, nor address negative regulation.

Methods: We introduce the new problem of finding optimal factories that use the fewest reactions, for the first time incorporating both first- and second-order negative regulation.

Heuristic shortest hyperpaths in cell signaling hypergraphs.

Background: Cell signaling pathways, which are a series of reactions that start at receptors and end at transcription factors, are basic to systems biology. Properly modeling the reactions in such pathways requires directed hypergraphs, where an edge is now directed between two sets of vertices. Inferring a pathway by the most parsimonious series of reactions corresponds to finding a shortest hyperpath in a directed hypergraph, which is NP-complete.

Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020.

In this meeting overview, we summarise the scientific program and organisation of the 16th International Society for Computational Biology Student Council Symposium in 2020 (ISCB SCS2020). This symposium was the first virtual edition in an uninterrupted series of symposia that has been going on for 15 years, aiming to unite computational biology students and early career researchers across the globe.

Separation and preconcentration of perrhenate from ionic solutions by ion exchange chromatography.

Technetium poses an environmental hazard because of its radioactivity and long half-life. It exists in the form of pertechnetate in the environment and can be modeled by the nonradioactive ion perrhenate, since pertechnetate and perrhenate have the same geometry and similar chemical properties. In this research, a new zinc cyclen resorcinarene cavitand (ZCR) column was used in ion chromatography (IC) to efficiently separate perrhenate.

Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization.

Motivation: Protein secondary structure prediction is a fundamental precursor to many bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary structure prediction do not explicitly leverage the vast number of proteins whose structure is known. Leveraging this additional information in a so-called template-based method has the potential to significantly boost prediction accuracy.