A set of chemical reactions occurring spontaneously in Earth's early chemical environments could have provided the foundations upon which life evolved. The discovery that a version of the Krebs cycle, which occurs in most living cells, can proceed in the absence of cellular proteins called enzymes suggests that metabolism is older than life itself. Metabolism describes the fiendishly complex network of reactions that enable organisms to generate energy and the molecules they need to survive, grow and reproduce. The Krebs cycle – also known as the tricarboxylic acid (TCA) cycle – is at the heart of this network. It describes a circular chain of reactions that generates precursors of amino acids and lipids used to build proteins and membranes, and molecules that help the cell to produce its energy.
As genomic and proteomic data is collected from highthroughput methods on a daily basis, subcellular components are identified and their in vitro behavior is characterized. However, much less is known of their in vivo activity because of the complex subcellular milieu they operate within. A component's milieu is determined by the biological pathways it participates in, and hence, the mechanisms by which it is regulated. We believe AI planning technology provides a modeling formalism for the task of biological pathway discovery, such that hypothetical pathways can be generated, queried and qualitatively simulated. The task of signal transduction pathway discovery is recast as a planning problem, one in which the initial and final states are known and cellular processes captured as abstract operators that modify the cellular environment. Thus, a valid plan that transforms the initial state into a goal state is a hypothetical pathway that prescribes the order of signaling events that must occur to effect the goal state. The planner is driven by data that is stored within a knowledge base and retrieved from heterogeneous sources (including gene expression, protein-protein interaction and literature mining) by a multi-agent information gathering system. We demonstrate the combined technology by translating the well-known EGF pathway into the planning formalism and deploying the Fast-Forward planner to reconstruct the pathway directly from the knowledge base.
We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is d-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2′-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites.
A makeup mirror that simulates various lighting conditions is among the high-tech beauty developments. A makeup mirror that simulates various lighting conditions is among the high-tech beauty developments. We live in an on-demand world -- with the expectation that our meals, entertainment and transportation will be available in a few minutes -- or an instant. That "right-now" mantra is also transforming the way we approach skin care and anti-aging treatments as we continue to spend big on cosmetic plastic surgery and in-office procedures. The American Society of Plastic Surgeons estimates that we shelled out $16 billion on liposuction, tummy tucks, fillers and laser treatments, among other procedures, in 2016.