References of "Simeonidis, Evangelos 50003096"
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See detailA community-driven global reconstruction of human metabolism.
Thiele, Ines UL; Swainston, N.; Fleming, Ronan MT UL et al

in Nature Biotechnology (2013), 31

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus ‘metabolic reconstruction’, which ... [more ▼]

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus ‘metabolic reconstruction’, which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. [less ▲]

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See detailA Guide to Integrating Transcriptional Regulatory and Metabolic Networks Using PROM (Probabilistic Regulation of Metabolism)
Simeonidis, Evangelos UL; Chandrasekaran, Sriram; Price, Nathan D.

in Methods in Molecular Biology (2013), 985

The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we ... [more ▼]

The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms. [less ▲]

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See detailComputing life: Add logos to biology and bios to physics
Kolodkin, Alexey UL; Simeonidis, Evangelos UL; Westerhoff, Hans V.

in Progress in Biophysics & Molecular Biology (2012), 111(2-3), 69-74

This paper discusses the interrelations between physics and biology. Particularly, we analyse the approaches for reconstructing the emergent properties of physical or biological systems. We propose ... [more ▼]

This paper discusses the interrelations between physics and biology. Particularly, we analyse the approaches for reconstructing the emergent properties of physical or biological systems. We propose approaches to scale emergence according to the degree of state-dependency of the system's component properties. Since the component properties of biological systems are state-dependent to a high extent, biological emergence should be considered as very strong emergence – i.e. its reconstruction would require a lot of information about state-dependency of its component properties. However, due to its complexity and volume, this information cannot be handled in the naked human brain, or on the back of an envelope. To solve this problem, biological emergence can be reconstructed in silico based on experimentally determined rate laws and parameter values of the living cell. According to some rough calculations, the silicon human might comprise the mathematical descriptions of around 105 interactions. This is not a small number, but taking into account the exponentially increase of computational power, it should not prove to be our principal limitation. The bigger challenges will be located in different areas. For example they may be related to the observer effect – the limitation to measuring a system's component properties without affecting the system. Another obstacle may be hidden in the tradition of "shaving away" all “unnecessary” assumptions (the so-called Occam's razor) that, in fact, reflects the intention to model the system as simply as possible and thus to deem the emergence to be less strong than it possibly is. We argue here that that Occam's razor should be replaced with the law of completeness. [less ▲]

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See detailMechanistic modeling of aberrant energy metabolism in human disease.
Sangar, Vineet; Eddy, James A.; Simeonidis, Evangelos UL et al

in Frontiers in Physiology (2012), 3

Dysfunction in energy metabolism-including in pathways localized to the mitochondria-has been implicated in the pathogenesis of a wide array of disorders, ranging from cancer to neurodegenerative diseases ... [more ▼]

Dysfunction in energy metabolism-including in pathways localized to the mitochondria-has been implicated in the pathogenesis of a wide array of disorders, ranging from cancer to neurodegenerative diseases to type II diabetes. The inherent complexities of energy and mitochondrial metabolism present a significant obstacle in the effort to understand the role that these molecular processes play in the development of disease. To help unravel these complexities, systems biology methods have been applied to develop an array of computational metabolic models, ranging from mitochondria-specific processes to genome-scale cellular networks. These constraint-based (CB) models can efficiently simulate aspects of normal and aberrant metabolism in various genetic and environmental conditions. Development of these models leverages-and also provides a powerful means to integrate and interpret-information from a wide range of sources including genomics, proteomics, metabolomics, and enzyme kinetics. Here, we review a variety of mechanistic modeling studies that explore metabolic functions, deficiency disorders, and aberrant biochemical pathways in mitochondria and related regions in the cell. [less ▲]

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See detailUnderstanding complexity in neurodegenerative diseases: in silico reconstruction of emergence.
Kolodkin, Alexey UL; Simeonidis, Evangelos UL; Balling, Rudi UL et al

in Frontiers in Physiology (2012), 3

Healthy functioning is an emergent property of the network of interacting biomolecules that comprise an organism. It follows that disease (a network shift that causes malfunction) is also an emergent ... [more ▼]

Healthy functioning is an emergent property of the network of interacting biomolecules that comprise an organism. It follows that disease (a network shift that causes malfunction) is also an emergent property, emerging from a perturbation of the network. On the one hand, the biomolecular network of every individual is unique and this is evident when similar disease-producing agents cause different individual pathologies. Consequently, a personalized model and approach for every patient may be required for therapies to become effective across mankind. On the other hand, diverse combinations of internal and external perturbation factors may cause a similar shift in network functioning. We offer this as an explanation for the multi-factorial nature of most diseases: they are "systems biology diseases," or "network diseases." Here we use neurodegenerative diseases, like Parkinson's disease (PD), as an example to show that due to the inherent complexity of these networks, it is difficult to understand multi-factorial diseases with simply our "naked brain." When describing interactions between biomolecules through mathematical equations and integrating those equations into a mathematical model, we try to reconstruct the emergent properties of the system in silico. The reconstruction of emergence from interactions between huge numbers of macromolecules is one of the aims of systems biology. Systems biology approaches enable us to break through the limitation of the human brain to perceive the extraordinarily large number of interactions, but this also means that we delegate the understanding of reality to the computer. We no longer recognize all those essences in the system's design crucial for important physiological behavior (the so-called "design principles" of the system). In this paper we review evidence that by using more abstract approaches and by experimenting in silico, one may still be able to discover and understand the design principles that govern behavioral emergence. [less ▲]

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See detailBuilding a kinetic model of trehalose biosynthesis in Saccharomyces cerevisiae.
Smallbone, Kieran; Malys, Naglis; Messiha, Hanan L. et al

in Methods in Enzymology (2011), 500

In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway based on kinetic data from experimental measurements and literature review. Our methodology is presented by ... [more ▼]

In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway based on kinetic data from experimental measurements and literature review. Our methodology is presented by utilizing the example of trehalose metabolism in yeast. The biology of the trehalose cycle is briefly reviewed and discussed. [less ▲]

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