Determining Interconnections in Chemical Reaction Networks

Papachristodoulou, Antonis; Recht, Benjamin

doi:10.1109/acc.2007.4283084

Cited by 26 publications

(26 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But Bayesian networks typically do not accommodate cycles and hence, can not handle feedback motifs that are common in genetic regulatory networks. Both causality and feedback motifs are no longer an issue when the network is modeled as a set of differential equations [8][9][10][11][12][13][14][15][16][17][18]. Identification is then typically optimization based, while approaches depend on whether the data is obtained from steady-state measurements [8][9][10] or dynamic time-series [11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Inferring stable genetic networks from steady-state data

et al. 2011

View full text Add to dashboard Cite

Gene regulatory networks capture the interactions between genes and other cell substances, resulting from the fundamental biological process of transcription and translation. In some applications, the topology of the regulatory network is not known, and has to be inferred from experimental data. The experimental data consist of expression levels of the genes, which are typically measured as mRNA concentrations in micro-array experiments. In a so called genetic perturbation experiment, small perturbations are applied to equilibrium states and the resulting changes in expression activity are measured. This paper develops novel algorithms that identify a sparse and stable genetic network that explains data obtained from noisy genetic perturbation experiments. Our identification algorithm is based on convex relaxations of the sparsity and stability constraints and can also incorporate a variety of prior knowledge of the network structure. Such knowledge can be either qualitative, specifying positive, negative or no interactions between genes, or quantitative, specifying a range of interaction strengths. Our approach is applied to both synthetic and experimental data, obtained for the SOS pathway in Escherichia coli, and the results show that the stability specification not only ensures consistency with the steady-state assumptions, but also significantly increases the identification performance. Since the method is based on convex optimization, it can be efficiently applied to large scale networks.

show abstract

Section: Introductionmentioning

confidence: 99%

Inferring stable genetic networks from steady-state data

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Alternatively, inferences about the topology of the network can be made by introducing pulse changes in concentration of a chemical species in the network, and observing the networks response, concluding causal chemical connectivities [10]. In [11], an approach was presented to apply linear programming to minimize the L 1 -norm such as to obtain the sparsest interaction structure in the case of chemical reaction networks. In [3], a linear dynamical system was considered to represent a gene regulatory networks, and an approach proposed to minimize the L 1 -norm in order to obtain the sparsest network structure form genetic perturbation experiments.…”

Section: Introductionmentioning

confidence: 99%

Determining interconnections in biochemical networks using linear programming

August

Papachristodoulou

Recht

et al. 2008

2008 47th IEEE Conference on Decision and Control

Self Cite

View full text Add to dashboard Cite

Follow this and additional works at: http://repository.upenn.edu/ese_papers August, E.; Papachristodoulou, A.; Recht, B.; Roberts, M.; Jadbabaie, A., "Determining interconnections in biochemical networks using linear programming," 47th IEEE Conference on Decision and Control, 2008. (CDC 2008), pp.3311-3316, 9-11 Dec. 2008 Decision and Control, 2008(CDC 2008), pp.3311-3316, 9-11 Dec. 2008 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Recommended CitationElias August, Antonius Papachristodoulou, Ben Recht, Mark Roberts, and Ali Jadbabaie, "Determining interconnections in biochemical networks using linear programming", . December 2008. Determining interconnections in biochemical networks using linear programming AbstractWe present a methodology for efficient, robust determination of the interaction topology of networked dynamical systems using time series data collected from experiments, under the assumption that these networks are sparse, i.e., have much less edges than the full graph with the same vertex set. To achieve this, we minimize the 1-norm of the decision variables while keeping the data in close Euler fit, thus putting more emphasis on determining the interconnection pattern rather than the closeness of fit. First, we consider a networked system in which the interconnection strength enters in an affine way in the system dynamics. We demonstrate the ability of our method to identify a network structure through numerical examples. Second, we extend our approach to the case of gene regulatory networks, in which the system dynamics are much more complicated. Decision and Control, 2008(CDC 2008), pp.3311-3316, 9-11 Dec. 2008 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/ese_papers/482 Determining Interconnections in Biochemical Networks Using Linear ProgrammingElias August, Antonis Papachristodoulou, Ben Recht, Mark Roberts and Ali JadbabaieAbstract-We present a methodo...

show abstract

“…We have succesfully applied the relaxation technique in another systems biology problem, the identification of sparse biomolecular networks [25], [26]. Similar program has been carried out independently by Papachristodoulou and Recht [27] and Han et al [28].…”

Section: A Minimal Network Problemmentioning

confidence: 99%

Metabolic networks analysis using convex optimization

Julius

Imieliński

Pappas

2008

2008 47th IEEE Conference on Decision and Control

View full text Add to dashboard Cite

Metabolic networks map the biochemical reactions in a living cell to the flow of various chemical substances in the cell, which are called metabolites. A standard model of a metabolic network is given as a linear map from the reaction rates to the change in metabolites concentrations. We study two problems related to the analysis of metabolic networks, the minimal network problem and the minimal knockout problem. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/grasp_papers/34Metabolic Networks Analysis using Convex Optimization A. Agung Julius, Marcin Imielinski and George J. Pappas Abstract-Metabolic networks map the biochemical reactions in a living cell to the flow of various chemical substances in the cell, which are called metabolites. A standard model of a metabolic network is given as a linear map from the reaction rates to the change in metabolites concentrations. We study two problems related to the analysis of metabolic networks, the minimal network problem and the minimal knockout problem.The minimal network problem amounts to finding the smallest set of reactions that can sustain the production of a metabolite. The minimal knockout problem deals with the question of finding the smallest set of knockouts (reactions with zero rates) that renders the production of a metabolite infeasible.In this paper we present a convex relaxation technique that results in a very fast computation for the solution to both problems. We also demonstrate that the minimal knockout problem is related to the dual of the minimal network problem.

show abstract

Determining Interconnections in Chemical Reaction Networks

Cited by 26 publications

References 23 publications

Inferring stable genetic networks from steady-state data

Inferring stable genetic networks from steady-state data

Determining interconnections in biochemical networks using linear programming

Metabolic networks analysis using convex optimization

Contact Info

Product

Resources

About