Using a synthetic biology approach to vary promoter gene combinations, we generated a total of 17 construct designs of the three pathways with and without the transporter RNAi construct. Initial screens for photoprotection by alternative pathway function under high–photorespiratory stress conditions identified three to five independent transformants of each design for further analysis. Gene and protein expression analyses confirmed expression of the introduced genes and suppression of the native transporter in RNAi plants. In greenhouse screens, pathway 1 increased biomass by nearly 13%. Pathway 2 showed no benefit compared to wild type.
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.
Biological carbon fixation requires several enzymes to turn CO2 into biomass. Although this pathway evolved in plants, algae, and microorganisms over billions of years, many reactions and enzymes could aid in the production of desired chemical products instead of biomass. Schwander et al. constructed an optimized synthetic carbon fixation pathway in vitro by using 17 enzymes--including three engineered enzymes--from nine different organisms across all three domains of life (see the Perspective by Gong and Li). The pathway is up to five times more efficient than the in vivo rates of the most common natural carbon fixation pathway. Further optimization of this and other metabolic pathways by using similar approaches may lead to a host of biotechnological applications.
The control of normal cellular homeostasis is a function of signaling by the RAS guanosine triphosphatases (GTPases) KRAS, NRAS, and HRAS and their downstream signaling proteins, such as the RAF kinases (BRAF and CRAF), mitogen-activated protein kinase (MAPK) kinase (MEK), and extracellular signal–regulated kinase (ERK), which together constitute the RAS-MAPK pathway (1). This pathway is dysregulated in human diseases, particularly cancer, in which mutations or other nongenetic events hyperactivate the pathway in more than 50% of cases (1). Activating mutations in RAS genes occur in more than 30% of all cancers and seemingly lock RAS into a constitutively active, GTP-bound state to signal autonomously without the need for upstream input (1). Therapeutic suppression of pathogenic RASMAPK signaling to maximize disease control in cancer patients remains an elusive goal. Multiple strategies targeting upstream [e.g., receptor tyrosine kinases (RTKs)] or downstream (e.g., MEK) RAS pathway proteins to limit RAS-GTP–mediated signaling in RAS-MAPK pathway–driven cancers are under evaluation (1–3).
In this paper, we develop a new human computation algorithm for speech-to-text transcription that can potentially achieve the high accuracy of professional transcription using only microtasks deployed via an online task market or a game. The algorithm partitions audio clips into short 10-second segments for independent processing and joins adjacent outputs to produce the full transcription. Each segment is sent through an iterative dual pathway structure that allows participants in either path to iteratively refine the transcriptions of others in their path while being rewarded based on transcriptions in the other path, eliminating the need to check transcripts in a separate process. Initial experiments with local subjects show that produced transcripts are on average 96.6% accurate.