The phrase "a suite of computational" is correct and usable in written English.
It can be used when referring to a collection or set of computational tools, software, or resources that work together for a specific purpose.
Example: "The research team developed a suite of computational models to analyze climate change data."
Alternatives: "a collection of computational" or "a set of computational".
Our theoretical findings are exemplified through a suite of computational examples focused on block-heterogeneous systems with increased complexity level.
Ilana L. Brito, Biomedical Engineering, and her team are developing a suite of computational tools for examining the mobile gene content in multiple datasets to identify those genes that may shape the response of microbial communities to stress.
"We want to shift the emphasis and classify tumors based on the genes that truly regulate tumor state and survival". Over the past 10 years, the Califano Lab has developed a suite of computational methods for modeling cell regulatory interaction networks (also called interactomes), demonstrating that interactomes of particular cancer subtypes become "rewired" in consistent and predictable ways.
We then examine available high resolution structural data and use a suite of computational methods to account for missing residues at the subunit and/or domain level.
The AgBase database includes a suite of computational tools to use GO annotations.
We describe a suite of computational screens which assesses sequence lengths over whole protein and terminal regions, sequence similarity, domain composition, and closest neighbors in other species.
A suite of useful computational tools is now available for data analysis such as MLST and detection of genetic variations.
CHARm is a program which provides a large suite of computational tools that encompass numerous conformational and path sampling methods, free energy estimates, molecular minimization, dynamics, and analysis techniques, and model-building capabilities (http://www.charmm.org/).
We have developed a complementary suite of computational tools for two-parameter screening of dynamics in neuronal models.
BMElib provides an extensive suite of computational functions with which to model the space/time global trend m Y(p ) and space/time residual covariance c X p, p ´) functions [see Supplemental Material, Equation S1 (http://dx.doi.org/10.1289/ehp.1205006)] and to derive kriging estimates.
The applied dynamical systems community has developed a universal and versatile suite of computational tools and techniques for comprehensive examination of diverse nonlinear systems of various origins including life sciences.
