Experimental Biological Protocols with Formal Semantics View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2018-08-24

AUTHORS

Alessandro Abate , Luca Cardelli , Marta Kwiatkowska , Luca Laurenti , Boyan Yordanov

ABSTRACT

Both experimental and computational biology is becoming increasingly automated. Laboratory experiments are now performed automatically on high-throughput machinery, while computational models are synthesized or inferred automatically from data. However, integration between automated tasks in the process of biological discovery is still lacking, largely due to incompatible or missing formal representations. While theories are expressed formally as computational models, existing languages for encoding and automating experimental protocols often lack formal semantics. This makes it challenging to extract novel understanding by identifying when theory and experimental evidence disagree due to errors in the models or the protocols used to validate them. To address this, we formalize the syntax of a core protocol language, which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols. We present both a deterministic and a stochastic semantics to this language, both defined in terms of hybrid processes. In particular, the stochastic semantics captures uncertainties in equipment tolerances, making it a suitable tool for both experimental and computational biologists. We illustrate how the proposed protocol language can be used for automated verification and synthesis of laboratory experiments on case studies from the fields of chemistry and molecular programming. More... »

PAGES

165-182

Book

TITLE

Computational Methods in Systems Biology

ISBN

978-3-319-99428-4
978-3-319-99429-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-99429-1_10

DOI

http://dx.doi.org/10.1007/978-3-319-99429-1_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106343441


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