A Guide to Integrating Transcriptional Regulatory and Metabolic Networks Using PROM (Probabilistic Regulation of Metabolism)

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 ▲]

Computing life: Add logos to biology and bios to physics

in Progress in Biophysics and 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 ▲]

Mechanistic modeling of aberrant energy metabolism in human disease.

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 ▲]