Modeling of Optimal Networks by Means of Linkages

We evaluate the consequences of narrow hospital networks in commercial health care markets. We develop a bargaining solution, “Nash-in-Nash with Threat of Replacement,” that captures insurers’ incentives to exclude, and combine it with California data and estimates from Ho and Lee (2017) to simulate equilibrium outcomes under social, consumer, and insurer-optimal networks. Private incentives to exclude generally exceed social incentives, as the insurer benefits from substantially lower negotiated hospital rates. Regulation prohibiting exclusion increases prices and premiums and lowers consumer welfare without significantly affecting social surplus. However, regulation may prevent harm to consumers living close to excluded hospitals. (JEL C78, D85, G22, H75, I11, I13, I18)

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DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

10.1101/2020.07.17.208918 ◽

2020 ◽

Author(s):

Pablo Rodríguez-Mier ◽

Nathalie Poupin ◽

Carlo de Blasio ◽

Laurent Le Cam ◽

Fabien Jourdan

Keyword(s):

Metabolic Network ◽

Metabolic Networks ◽

Relevant Information ◽

Experimental Information ◽

Correct Identification ◽

Good Representation ◽

Metabolic State ◽

Whole Space ◽

Context Specific ◽

Optimal Networks

AbstractThe correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM, a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub1.

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Optimal networks for distributing nonsequential programs

Information Processing Letters ◽

10.1016/0020-0190(83)90027-3 ◽

1983 ◽

Vol 16 (2) ◽

pp. 71-74 ◽

Cited By ~ 1

Author(s):

Stefan Eichholz

Keyword(s):

Optimal Networks

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Optimal Networks with NOR-OR gates and wired-OR logic

Computer-Aided Design ◽

10.1016/0010-4485(75)90170-0 ◽

1975 ◽

Vol 7 (1) ◽

pp. 61

Keyword(s):

Optimal Networks

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Computer program for designing optimal networks with MOS gates

Computer-Aided Design ◽

10.1016/0010-4485(73)90213-3 ◽

1973 ◽

Vol 5 (1) ◽

pp. 62

Keyword(s):

Computer Program ◽

Optimal Networks

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REPARTITORS, SELECTORS AND SUPERSELECTORS

Journal of Interconnection Networks ◽

10.1142/s0219265906001752 ◽

2006 ◽

Vol 07 (03) ◽

pp. 391-415 ◽

Cited By ~ 3

Author(s):

FRÉDÉRIC HAVET

Keyword(s):

Lower Bounds ◽

Disjoint Paths ◽

Edge Disjoint ◽

Minimum Number ◽

Vertex Set ◽

Optimal Networks

An (n, p, f)-network G is a graph (V, E) where the vertex set V is partitioned into four subsets [Formula: see text] and [Formula: see text] called respectively the priorities, the ordinary inputs, the outputs and the switches, satisfying the following constraints: there are p priorities, n - p ordinary inputs and n + f outputs; each priority, each ordinary input and each output is connected to exactly one switch; switches have degree at most 4. An (n, p, f)-network is an (n, p, f)-repartitor if for any disjoint subsets [Formula: see text] and [Formula: see text] of [Formula: see text] with [Formula: see text] and [Formula: see text], there exist in G, n edge-disjoint paths, p of them from [Formula: see text] to [Formula: see text] and the n - p others joining [Formula: see text] to [Formula: see text]. The problem is to determine the minimum number R(n, p, f) of switches of an (n, p, f)-repartitor and to construct a repartitor with the smallest number of switches. In this paper, we show how to build general repartitors from (n, 0, f)-repartitors also called (n, n + f)-selectors. We then consrtuct selectors using more powerful networks called superselectors. An (n, 0, 0)-network is an n-superselector if for any subsets [Formula: see text] and [Formula: see text] with [Formula: see text], there exist in G, [Formula: see text] edge-disjoint paths joining [Formula: see text] to [Formula: see text]. We show that the minimum number of switches of an n-superselector S+ (n) is at most 17n + O(log(n)). We then deduce that [Formula: see text] if [Formula: see text], R(n, p, f) ≤ 18n + 34f + O( log (n + f)), if [Formula: see text] and [Formula: see text] if [Formula: see text]. Finally, we give lower bounds for R(n, 0, f) and S+ (n) and show optimal networks for small value of n.

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