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.
Several factors contribute to the growing need for a formalization of experimental protocols in biology. First, better record-keeping of experimental operations is recognized as a step towards tackling the ‘reproducibility crisis’ in biology @cite_14 . Second, the emergence of ‘cloud labs’ @cite_22 creates a need for precise, machine-readable descriptions of the experimental steps to be executed. To address these needs, frameworks allowing protocols to be recorded, shared, and reproduced locally or in a remote lab have been proposed. These frameworks introduce different programming languages for experimental protocols including BioCoder @cite_16 , Autoprotocol, and Antha @cite_15 . These languages provide expressive, high-level protocol descriptions but consider each experimental sample as a labelled ‘black-box’. This makes it challenging to study a protocol together with the biochemical systems it manipulates in a common framework.
@cite_15 @cite_14 @cite_22 @cite_16
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Background Published descriptions of biology protocols are often ambiguous and incomplete, making them difficult to replicate in other laboratories. However, there is increasing benefit to formalizing the descriptions of protocols, as laboratory automation systems (such as microfluidic chips) are becoming increasingly capable of executing them. Our goal in this paper is to improve both the reproducibility and automation of biology experiments by using a programming language to express the precise series of steps taken. Protocol reverse engineering has often been a manual process that is considered time-consuming, tedious and error-prone. To address this limitation, a number of solutions have recently been proposed to allow for automatic protocol reverse engineering. Unfortunately, they are either limited in extracting protocol fields due to lack of program semantics in network traces or primitive in only revealing the flat structure of protocol format. In this paper, we present a system called AutoFormat that aims at not only extracting protocol fields with high accuracy, but also revealing the inherently “non-flat”, hierarchical structures of protocol messages. AutoFormat is based on the key insight that different protocol fields in the same message are typically handled in different execution contexts (e.g., the runtime call stack). As such, by monitoring the program execution, we can collect the execution context information for every message byte (annotated with its offset in the entire message) and cluster them to derive the protocol format. We have evaluated our system with more than 30 protocol messages from seven protocols, including two text-based protocols (HTTP and SIP), three binary-based protocols (DHCP, RIP, and OSPF), one hybrid protocol (CIFS SMB), as well as one unknown protocol used by a real-world malware. Our results show that AutoFormat can not only identify individual message fields automatically and with high accuracy (an average 93.4 match ratio compared with Wireshark), but also unveil the structure of the protocol format by revealing possible relations (e.g., sequential, parallel, and hierarchical) among the message fields. ∗Part of this research has been supported by the National Science Foundation under grants CNS-0716376 and CNS-0716444. The bulk of this work was performed when the first author was visiting George Mason University in Summer 2007. Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge. The programming of biological cells by genetic circuit engineering is enabling the development of man-made devices and systems in the biochemical environment, with applications in the areas of biomedicine, security, and environmental sensing and control, amongst others. The exchange of information through biochemical reactions and molecule transport, i.e., Molecular Communication (MC), stands as one of the foundational paradigms for the design and characterization of these systems. In a previous work, the same authors developed an analog soft decoder design for MC based on biological circuits inspired by the analog information processing in biochemical reactions. While such a design was optimized for an MC channel affected by Gaussian noise, realistic noise models in molecule transport processes and biochemical reactions tend to deviate from this assumption. In this paper, these models are discussed, together with the validity of their Gaussian approximations, in light of the performance of the log-likelihood ratio calculation of the aforementioned design, numerically evaluated through biochemical simulation. These models, which are directly derived from the theory of molecular diffusion and stochastic chemical reaction analysis, are formulated with a general validity with respect to any future MC system design based on biological circuits.