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R. L. Colasanti

Researcher at Argonne National Laboratory

Publications -  23
Citations -  1636

R. L. Colasanti is an academic researcher from Argonne National Laboratory. The author has contributed to research in topics: Spirometry & Biofilm. The author has an hindex of 14, co-authored 22 publications receiving 1299 citations. Previous affiliations of R. L. Colasanti include University of South Wales & University of Chicago.

Papers
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Journal ArticleDOI

KBase: The United States Department of Energy Systems Biology Knowledgebase.

Adam P. Arkin, +86 more
- 06 Jul 2018 - 
TL;DR: Author(s): Arkin, Adam P; Cottingham, Robert W; Henry, Christopher S; Harris, Nomi L; Stevens, Rick L; Maslov, Sergei; Dehal, Paramvir; Ware, Doreen; Perez, Fernando; Canon, Shane; Sneddon, Michael W; Henderson, Matthew L; Riehl, William J; Murphy-Olson, Dan; Chan, Stephen Y; Kamimura, Roy T.
Journal ArticleDOI

A unifying hypothesis for the structure of microbial biofilms based on cellular automaton models

TL;DR: Experimental research with bacterial colonies and models of the latter using cellular automata give results which strongly suggest that biofilm structure is largely determined by substrate concentration.
Journal ArticleDOI

Resource dynamics and vegetation processes: a deterministic model using two-dimensional cellular automata.

R. L. Colasanti, +1 more
- 01 Jan 1993 - 
TL;DR: Rules of resource capture, utilization and release derived from CSR theory have been used to construct a cellular automaton model simulating secondary succession and spatial patterns of vegetation development on gradients in resource concentration and intensity of disturbance.
Posted ContentDOI

The DOE Systems Biology Knowledgebase (KBase)

TL;DR: The KBase platform has extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling, and a software development kit allowing the community to add functionality to the system.
Patent

Method and system for interpreting and validating experimental data with automated reasoning

TL;DR: In this article, a semantic representation of domain specific knowledge is created that meets a desired set of criteria and a set of reasons are used to help interpret the classified pharmaceutical data to remove errors such as "physical errors" (e.g., pipettor errors, common microplate preparation errors, microplate variances within runs, bio-chip errors, gel-electrophoresis errors, etc.).