AUTOMATIC MODELING OF SIGNALING PATHWAYS BY NETWORK FLOW MODEL

Signal transduction is an important process that controls cell proliferation, metabolism, differentiation, and so on. Effective computational models which unravel such a process by taking advantage of high-throughput genomic and proteomic data are highly demanded to understand the essential mechanisms underlying signal transduction. Since protein–protein interaction (PPI) plays an important role in signal transduction, in this paper, we present a novel method for modeling signaling pathways from PPI networks automatically. Given an undirected weighted protein interaction network, finding signaling pathways is treated as searching for optimal subnetworks according to some cost function. To cope with this optimization problem, a network flow model is proposed in this work to extract signaling pathways from protein interaction networks. In particular, the network flow model is formalized and solved as a mixed integer linear programming (MILP) model, which is simple in algorithm and efficient in computation. The numerical results on two known yeast MAPK signaling pathways demonstrate the efficiency and effectiveness of the proposed method.

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A Network Flow Model Approach to Determining Optimal Intervention Programs for Railway Infrastructure Networks

Infrastructures ◽

10.3390/infrastructures3030031 ◽

2018 ◽

Vol 3 (3) ◽

pp. 31 ◽

Cited By ~ 4

Author(s):

Marcel Burkhalter ◽

Bryan Adey

Keyword(s):

Network Flow ◽

Intervention Program ◽

Flow Model ◽

Intervention Programs ◽

Mixed Integer ◽

Infrastructure Networks ◽

Network Flow Model ◽

Optimal Intervention ◽

Railway Infrastructure ◽

Model Approach

The determination of the optimal interventions to execute on rail infrastructure networks is a challenging task, due to the many types of objects (e.g., bridges, tracks, and switches), how the objects work together to provide service, and the possible reductions in costs and service disruptions as obtained by grouping interventions. Although railway infrastructure managers are using computer systems to help them determine intervention programs, there are none that result in the highest net benefits while taking into consideration all of these aspects. This paper presents a network flow model approach that allows for determining the optimal intervention programs for railway infrastructure networks while taking into considerations different types of objects, how the objects work together to provide service, and object and object-traffic dependencies. The network flow models are formulated as mixed integer linear programs, where the optimal intervention program is found by using the simplex and branch and bound algorithms. The modelling approach is illustrated by using it to determine the optimal intervention program for a 2200 m multi-track railway line consisting of 11 track sections, 23 switches, and 39 bridges. It is shown that the proposed constrained network flow model can be used to determine the optimal intervention program within a reasonable amount of time, when compared to more traditional models and search algorithms.

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DISTANCE-WISE PATHWAY DISCOVERY FROM PROTEIN–PROTEIN INTERACTION NETWORKS WEIGHTED BY SEMANTIC SIMILARITY

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720014500048 ◽

2014 ◽

Vol 12 (01) ◽

pp. 1450004 ◽

Cited By ~ 3

Author(s):

SLAVKA JAROMERSKA ◽

PETR PRAUS ◽

YOUNG-RAE CHO

Keyword(s):

Signaling Pathways ◽

Semantic Similarity ◽

Protein Interaction ◽

Protein Pair ◽

Semantic Analysis ◽

Topological Analysis ◽

Cellular Mechanisms ◽

Semantic Similarity Measure ◽

Protein Protein Interaction ◽

Ppi Networks

Reconstruction of signaling pathways is crucial for understanding cellular mechanisms. A pathway is represented as a path of a signaling cascade involving a series of proteins to perform a particular function. Since a protein pair involved in signaling and response have a strong interaction, putative pathways can be detected from protein–protein interaction (PPI) networks. However, predicting directed pathways from the undirected genome-wide PPI networks has been challenging. We present a novel computational algorithm to efficiently predict signaling pathways from PPI networks given a starting protein and an ending protein. Our approach integrates topological analysis of PPI networks and semantic analysis of PPIs using Gene Ontology data. An advanced semantic similarity measure is used for weighting each interacting protein pair. Our distance-wise algorithm iteratively selects an adjacent protein from a PPI network to build a pathway based on a distance condition. On each iteration, the strength of a hypothetical path passing through a candidate edge is estimated by a local heuristic. We evaluate the performance by comparing the resultant paths to known signaling pathways on yeast. The results show that our approach has higher accuracy and efficiency than previous methods.

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Operation of a CCHP System Using an Optimal Energy Dispatch Algorithm

ASME 2008 2nd International Conference on Energy Sustainability, Volume 1 ◽

10.1115/es2008-54096 ◽

2008 ◽

Cited By ~ 4

Author(s):

Heejin Cho ◽

Sandra D. Eksioglu ◽

Rogelio Luck ◽

Louay M. Chamra

Keyword(s):

Energy Efficiency ◽

Energy Cost ◽

Network Flow ◽

Flow Model ◽

Energy Requirement ◽

Optimal Operation ◽

Operational Conditions ◽

Optimal Energy ◽

Network Flow Model ◽

The Cost

The Combined Cooling, Heating, and Power (CCHP) systems have been widely recognized as a key alternative for thermal and electric energy generation because of the outstanding energy efficiency, reduced environmental emissions, and relative independence from centralized power grids. Nevertheless, the total energy cost of CCHP systems can be highly dependent on the operation of individual components and load balancing. The latter refers to the process of fulfilling the thermal and electrical demand by partitioning or “balancing” the energy requirement between the available sources of energy supply. The energy cost can be optimized through an energy dispatch algorithm which provides operational/control signals for the optimal operation of the equipment. The algorithm provides optimal solutions on decisions regarding generating power locally or buying power from the grid. This paper presents an initial study on developing an optimal energy dispatch algorithm that minimizes the cost of energy (i.e., cost of electricity from the grid and cost of natural gas into the engine and boiler) based on energy efficiency constrains for each component. A deterministic network flow model of a typical CCHP system is developed as part of the algorithm. The advantage of using a network flow model is that the power flows and efficiency constraints throughout the CCHP components can be readily visualized to facilitate the interpretation of the results. A linear programming formulation of the network flow model is presented. In the algorithm, the inputs include the cost of the electricity and fuel and the constraints include the cooling, heating, and electric load demands and the efficiencies of the CCHP components. This algorithm has been used in simulations of several case studies on the operation of an existing micro-CHP system. Several scenarios with different operational conditions are presented in the paper to demonstrate the economical advantages resulting from optimal operation.

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