Network Medicine Quotes

“An important attribute of metabolites is their close relationship to both the biological states of interest (i.e. disease status) and relevant genomic, transcriptomic, and proteomic variants causally related to the disease state. As such, metabo-profiles can be viewed as an intermediate measure that links pre-disposing genes and environmental exposures to a resulting disease state. Causal metabolites also typically have a stronger relationship (i.e. larger effect size) to the underlying genetics and the disease phenotype. Thus, the integration of metabolomic data into systems biology approaches may provide a missing link between genes and disease states.”
― Joseph Loscalzo, Network Medicine: Complex Systems in Human Disease and Therapeutics

“In network models of the interactome, these truncating mutations can be thought of as the removal of one node along with all its edges - a node removal. Nonconservative missense mutations of amino acids in the protein core that lead to major folding problems, protein aggregation, and premature protein degradation can also be modeled as node removals. At the other end of the mutational spectrum are small in-frame indels or missense mutations. These can preserve protein folding, but may modify the active site of an enzyme or affect the binding to another protein or macromolecule. In network models, these mutations, which specifically perturb a single molecular interaction, have been labeled as edge-specific or "edgetic". While investigation of the precise interaction defects associated with point mutations is of course not new, the term edgetic promotes a subtle yet meaningful archetype shift from conventional gene-centered models, which emphasize consideration of which specific edges are affected by a mutation, complement and extend classic gene-centric models, which ascertain only whether a gene product is present or not present and neglect less overt alterations of a given gene or gene product.”
― Joseph Loscalzo, Network Medicine: Complex Systems in Human Disease and Therapeutics

“Despite the advancements of systematic experimental pipelines, literature-curated protein-interaction data continue to be the primary data for investigation of focused biological mechanisms. Notwithstanding the variable quality of curated interactions available in public databases, the impact of inspection bias on the ability of literature maps to provide insightful information remains equivocal. The problems posed by inspection bias extend beyond mapping of protein interactions to the development of pharmacological agents and other aspects of modern biomedicine. Essentially the same 10% of the proteome is being investigated today as was being investigated before the announcement of completion of the reference genome sequence. One way forward, at least with regard to interactome mapping, is to continue the transition toward systematic and relatively unbiased experimental interactome mapping. With continued advancement of systematic protein-interaction mapping efforts, the expectation is that interactome 'deserts', the zones of the interactome space where biomedical knowledge researchers simply do not look for interactions owing to the lack of prior knowledge, might eventually become more populated. Efforts at mapping protein interactions will continue to be instrumental for furthering biomedical research.”
― Joseph Loscalzo, Network Medicine: Complex Systems in Human Disease and Therapeutics

“Metabolic networks remain the only class of biological network reconstructed reasonably comprehensively at the genome-scale in humans. Given that metabolic networks are ultimately based on directed chemical reactions that obey the laws of mass and energy balance, they can further serve the basis for calculations to predict reaction rates (metabolic flux). These fluxes can subsequently be used to compute productions and growth rates of metabolites. In flux balance analysis, the set of reactions is formulated as a stochiometric matrix, which enumerates the ratios of metabolite participation in each reaction. A set of physically possible reaction flux rates result by enforcing a steady-state mass balance (homeostasis) and additional constraints on reaction reversabilities and maximal conversion rates. From within the space of chemically feasible reaction flux combinations, the subset of biologically relevant reaction flux profiles can be solved by optimizing an objective function. The most commonly used objective function in microbes has been to maximize the production of biomass, which serves as a proxy for maximizing growth rate. Notably, while maximal growth may be an appropriate assumption for diseases such as cancer under certain conditions, the best cellular objective function to simulate many human tissues and cell types is unknown (and is likely condition-specific). Adjusting this objective function, which was developed based on microbial physiology, to better reflect human tissues is an area of active research.”
― Joseph Loscalzo, Network Medicine: Complex Systems in Human Disease and Therapeutics