scholarly journals Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling

2019 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Strong Coupling ◽  
Metabolic Networks ◽  
Search Space ◽  
Anaerobic Conditions ◽  
Case Scenario ◽  
Worst Case ◽  
Metabolic Network Analysis ◽  
Network Topologies ◽  
Genome Scale ◽  
Parameter Values

AbstractBackgroundMetabolic network analysis through flux balance is an established method for the computational redesign of production strains in metabolic engineering. The computational redesign is often based on reaction deletions from the original wild type networks. A key principle often used in this method is the production of target metabolites as by-products of cell growth. From a viewpoint of bioinformatics, it is very important to prepare a set of algorithms that can determine reaction deletions that achieve growth coupling whatever network topologies, target metabolites and parameter values will be considered in the future. Recently, the strong coupling-based method was used to demonstrate that the coupling of growth and production is possible for nearly all metabolites through reaction deletions in genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae under aerobic conditions. However, when growing S. cerevisiae under anaerobic conditions, deletion strategies using the strong coupling-based method were possible for only 3.9% of all metabolites. Therefore, it is necessary to develop algorithms that can achieve growth coupling by reaction deletions for the conditions that the strong coupling-based method was not efficient.ResultsWe developed an algorithm that could calculate the reaction deletions that achieve the coupling of growth and production for 91.3% metabolites in genome-scale models of S. cerevisiae under anaerobic conditions. This analysis was conducted for the worst-case-scenario using flux variability analysis. To demonstrate the feasibility of the coupling, we derived appropriate reaction deletions using the new algorithm for target production in which the search space was divided into small cubes (CubeProd).ConclusionsWe developed a novel algorithm, CubeProd, to demonstrate that growth coupling is possible for most metabolites in S.cerevisiae under anaerobic conditions. This may imply that growth coupling is possible by reaction deletions for most target metabolites in any genome-scale constraint-based metabolic networks. The developed software, CubeProd, implemented in MATLAB, and the obtained reaction deletion strategies are freely available.

scholarly journals Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling

2020 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Strong Coupling ◽  
Metabolic Networks ◽  
Search Space ◽  
Anaerobic Conditions ◽  
Case Scenario ◽  
Worst Case ◽  
Metabolic Network Analysis ◽  
Network Topologies ◽  
Genome Scale ◽  
Parameter Values

Abstract Background: Metabolic network analysis through flux balance is an established method for the computational redesign of production strains in metabolic engineering. The computational redesign is often based on reaction deletions from the original wild type networks. A key principle often used in this method is the production of target metabolites as by-products of cell growth. From a viewpoint of bioinformatics, it is very important to prepare a set of algorithms that can determine reaction deletions that achieve growth coupling whatever network topologies, target metabolites and parameter values will be considered in the future. Recently, the strong coupling-based method was used to demonstrate that the coupling of growth and production is possible for nearly all metabolites through reaction deletions in genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae under aerobic conditions. However, when growing S. cerevisiae under anaerobic conditions, deletion strategies using the strong coupling-based method were possible for only 3.9% of all metabolites. Therefore, it is necessary to develop algorithms that can achieve growth coupling by reaction deletions for the conditions that the strong coupling-based method was not efficient. Results: We developed an algorithm that could calculate the reaction deletions that achieve the coupling of growth and production for 91.3% metabolites in genome-scale models of S. cerevisiae under anaerobic conditions. This analysis was conducted for the worst-case scenario using flux variability analysis. To demonstrate the feasibility of the coupling, we derived appropriate reaction deletions using the new algorithm for target production in which the search space was divided into small cubes (CubeProd). Conclusions: We developed a novel algorithm, CubeProd, to demonstrate that growth coupling is possible for most metabolites in S.cerevisiae under anaerobic conditions. This may imply that growth coupling is possible by reaction deletions for most target metabolites in any genome-scale constraint-based metabolic networks. The developed software, CubeProd, implemented in MATLAB, and the obtained reaction deletion strategies are freely available.

scholarly journals Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling

2020 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Escherichia Coli ◽  
Metabolic Network ◽  
Metabolic Networks ◽  
Search Space ◽  
Anaerobic Conditions ◽  
Aerobic Conditions ◽  
Metabolic Network Analysis ◽  
Balance Analysis ◽  
Target Production ◽  
Genome Scale

Abstract BackgroundMetabolic engineering strategies enabling the production of specific target metabolites by host strains can be identified in silico through the use of metabolic network analysis such as flux balance analysis. This type of metabolic redesign is based on the computation of reactions that should be deleted from the original network representing the metabolism of the host strain to enable the production of the target metabolites while still ensuring its growth (the concept of growth coupling). In this context, it is important to use algorithms that enable this growth-coupled reaction deletions identification for any metabolic network topologies and any potential target metabolites. A recent method using a strong growth coupling assumption has been shown to be able to identify such computational redesign for nearly all metabolites included in the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae when cultivated under aerobic conditions. However, this approach enables the computational redesign of S. cerevisiae for only 3.9% of all metabolites if under anaerobic conditions. Therefore, it is necessary to develop algorithms able to perform for various culture conditions.ResultsThe author developed an algorithm that could calculate the reaction deletions that achieve the coupling of growth and production for 91.3% metabolites in genome-scale models of S. cerevisiae under anaerobic conditions. Computational experiments showed that the proposed algorithm is efficient also for aerobic conditions and Escherichia coli. In these analyses, the least target production rates were evaluated using flux variability analysis when multiple fluxes yield the highest growth rate. To demonstrate the feasibility of the coupling, the author derived appropriate reaction deletions using the new algorithm for target production in which the search space was divided into small cubes (CubeProd).ConclusionsThe author developed a novel algorithm, CubeProd, to demonstrate that growth coupling is possible for most metabolites in S.cerevisiae under anaerobic conditions. This may imply that growth coupling is possible by reaction deletions for most target metabolites in any genome-scale constraint-based metabolic networks. The developed software, CubeProd, implemented in MATLAB, and the obtained reaction deletion strategies are freely available.

scholarly journals rMTA: robust metabolic transformation analysis

2019 ◽  
Vol 35 (21) ◽  
pp. 4350-4355
Author(s):  
Luis V Valcárcel ◽  
Verónica Torrano ◽  
Luis Tobalina ◽  
Arkaitz Carracedo ◽  
Francisco J Planes
Keyword(s):  
Drug Targets ◽  
Metabolic Networks ◽  
Gene Knockout ◽  
Supplementary Information ◽  
Case Scenario ◽  
Worst Case ◽  
Healthy State ◽  
Metabolic Transformation ◽  
Relevant Topic ◽  
Genome Scale

Abstract Motivation The development of computational tools exploiting -omics data and high-quality genome-scale metabolic networks for the identification of novel drug targets is a relevant topic in Systems Medicine. Metabolic Transformation Algorithm (MTA) is one of these tools, which aims to identify targets that transform a disease metabolic state back into a healthy state, with potential application in any disease where a clear metabolic alteration is observed. Results Here, we present a robust extension to MTA (rMTA), which additionally incorporates a worst-case scenario analysis and minimization of metabolic adjustment to evaluate the beneficial effect of gene knockouts. We show that rMTA complements MTA in the different datasets analyzed (gene knockout perturbations in different organisms, Alzheimer’s disease and prostate cancer), bringing a more accurate tool for predicting therapeutic targets. Availability and implementation rMTA is freely available on The Cobra Toolbox: https://opencobra.github.io/cobratoolbox/latest/. Supplementary information Supplementary data are available at Bioinformatics online.

Robust Blood-Glucose Control of Type I Diabetes Patients Under Intensive Care Using Mathematica

2008 ◽  
pp. 1210-1219 ◽  
Author(s):  
Béla Paláncz ◽  
Levente Kovács ◽  
Balázs Benyó ◽  
Zoltán Benyó
Keyword(s):  
Intensive Care ◽  
Type I Diabetes ◽  
Design Method ◽  
Blood Glucose Control ◽  
Model Parameters ◽  
Type I ◽  
Case Scenario ◽  
Worst Case ◽  
Robust Control Design ◽  
Parameter Values

This article presents a robust control design method on frequency domain using Mathematica for regularization of glucose level in Type I diabetes persons under intensive care. The method originally proposed under Mathematica by Helton and Merino (1998) is now improved with a disturbance rejection constraint inequality, and is tested on the three-state minimal model. Nonlinear closed loop simulation in state-space, in case of standard meal disturbances, demonstrates the robustness of the resulted high-order linear controller. The obtained results are compared with H8 design implemented with Matlab, proving that the controller (for the considered model parameters) can operate properly, even in case of parameter values of the worst-case scenario.

scholarly journals A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coast Population Regions

2020 ◽  
Vol 1 ◽  
Author(s):  
Yao-Yu Yeo ◽  
Yao-Rui Yeo ◽  
Wan-Jin Yeo
Keyword(s):  
East Coast ◽  
Current Data ◽  
Transmission Dynamics ◽  
Functional Testing ◽  
Case Scenario ◽  
Worst Case ◽  
The Us ◽  
Global Concern ◽  
Parameter Values ◽  
Us West

AbstractThe ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to estimate a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coast population regions by considering the different phases of response implemented by the US as well as transmission dynamics of COVID-19 in countries that were most affected. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April 2020 and begin to abate by July provided that appropriate COVID-19 measures are promptly implemented and followed, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model is also sensitive to assigned parameter values and reflects the importance of healthcare preparedness during pandemics.

scholarly journals Automated Reasoning for City Infrastructure Maintenance Decision Support

2018 ◽  
Author(s):  
Lijun Wei ◽  
Derek R. Magee ◽  
Vania Dimitrova ◽  
Barry Clarke ◽  
Heshan Du ◽  
...  
Keyword(s):  
Decision Support ◽  
Asset Management ◽  
Uncertain Data ◽  
Data Retrieval ◽  
Case Scenario ◽  
Worst Case ◽  
Domain Experts ◽  
Incomplete And Uncertain Data ◽  
Infrastructure Maintenance ◽  
Parameter Values

We present an interactive decision support system for assisting city infrastructure inter-asset management. It combines real-time site specific data retrieval, a knowledge base co-created with domain experts and an inference engine capable of predicting potential consequences and risks resulting from the available data and knowledge. The system can give explanations of each consequence, cope with incomplete and uncertain data by making assumptions about what might be the worst case scenario, and making suggestions for further investigation. This demo presents multiple real-world scenarios, and demonstrates how modifying assumptions (parameter values) can lead to different consequences.

scholarly journals A New Constructive Heuristic Driven by Machine Learning for the Traveling Salesman Problem

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 267
Author(s):  
Umberto Junior Mele ◽  
Luca Maria Gambardella ◽  
Roberto Montemanni
Keyword(s):  
Machine Learning ◽  
Traveling Salesman Problem ◽  
Scale Up ◽  
Search Space ◽  
Optimization Techniques ◽  
Traveling Salesman ◽  
Case Scenario ◽  
Worst Case ◽  
Constructive Heuristic ◽  
The Traveling Salesman Problem

Recent systems applying Machine Learning (ML) to solve the Traveling Salesman Problem (TSP) exhibit issues when they try to scale up to real case scenarios with several hundred vertices. The use of Candidate Lists (CLs) has been brought up to cope with the issues. A CL is defined as a subset of all the edges linked to a given vertex such that it contains mainly edges that are believed to be found in the optimal tour. The initialization procedure that identifies a CL for each vertex in the TSP aids the solver by restricting the search space during solution creation. It results in a reduction of the computational burden as well, which is highly recommended when solving large TSPs. So far, ML was engaged to create CLs and values on the elements of these CLs by expressing ML preferences at solution insertion. Although promising, these systems do not restrict what the ML learns and does to create solutions, bringing with them some generalization issues. Therefore, motivated by exploratory and statistical studies of the CL behavior in multiple TSP solutions, in this work, we rethink the usage of ML by purposely employing this system just on a task that avoids well-known ML weaknesses, such as training in presence of frequent outliers and the detection of under-represented events. The task is to confirm inclusion in a solution just for edges that are most likely optimal. The CLs of the edge considered for inclusion are employed as an input of the neural network, and the ML is in charge of distinguishing when such edge is in the optimal solution from when it is not. The proposed approach enables a reasonable generalization and unveils an efficient balance between ML and optimization techniques. Our ML-Constructive heuristic is trained on small instances. Then, it is able to produce solutions—without losing quality—for large problems as well. We compare our method against classic constructive heuristics, showing that the new approach performs well for TSPLIB instances up to 1748 cities. Although ML-Constructive exhibits an expensive constant computation time due to training, we proved that the computational complexity in the worst-case scenario—for the solution construction after training—is O(n2logn2), n being the number of vertices in the TSP instance.

Reducing Probabilistic Weather Forecasts to the Worst Case Scenario: Anchoring Effects

2008 ◽  
Author(s):  
Sonia Savelli ◽  
Susan Joslyn ◽  
Limor Nadav-Greenberg ◽  
Queena Chen
Keyword(s):  
Case Scenario ◽  
Worst Case ◽  
Weather Forecasts ◽  
Worst Case Scenario ◽  
Anchoring Effects

EXPEDITION TO MALI, 2020

2020 ◽  
pp. 12-28
Author(s):  
D. V. Vaniukova ◽  
◽  
P. A. Kutsenkov ◽  
Keyword(s):  
Mass Murder ◽  
The State ◽  
Case Scenario ◽  
Worst Case ◽  
Traditional Art ◽  
Peter The Great ◽  
State Museum ◽  
Bobo Dioulasso ◽  
Oriental Studies ◽  
The Village

The research expedition of the Institute of Oriental studies of the Russian Academy of Sciences has been working in Mali since 2015. Since 2017, it has been attended by employees of the State Museum of the East. The task of the expedition is to study the transformation of traditional Dogon culture in the context of globalization, as well as to collect ethnographic information (life, customs, features of the traditional social and political structure); to collect oral historical legends; to study the history, existence, and transformation of artistic tradition in the villages of the Dogon Country in modern conditions; collecting items of Ethnography and art to add to the collection of the African collection of the. Peter the Great Museum (Kunstkamera, Saint Petersburg) and the State Museum of Oriental Arts (Moscow). The plan of the expedition in January 2020 included additional items, namely, the study of the functioning of the antique market in Mali (the “path” of things from villages to cities, which is important for attributing works of traditional art). The geography of our research was significantly expanded to the regions of Sikasso and Koulikoro in Mali, as well as to the city of Bobo-Dioulasso and its surroundings in Burkina Faso, which is related to the study of migrations to the Bandiagara Highlands. In addition, the plan of the expedition included organization of a photo exhibition in the Museum of the village of Endé and some educational projects. Unfortunately, after the mass murder in March 2019 in the village of Ogossogou-Pel, where more than one hundred and seventy people were killed, events in the Dogon Country began to develop in the worst-case scenario: The incessant provocations after that revived the old feud between the Pel (Fulbe) pastoralists and the Dogon farmers. So far, this hostility and mutual distrust has not yet developed into a full-scale ethnic conflict, but, unfortunately, such a development now seems quite likely.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

Sign in / Sign up

Export Citation Format

Share Document