OBJECTIVE: In biomedical research, knowledge about the relationships between entities, including genes, proteins, and drugs, is vital for elucidating complex biological processes and intracellular pathway mechanisms. While natural language processing (NLP) methods have shown great success in biomedical relation extraction (RE), extracted relations often lack contextual information such as cell type, cell line, and intracellular location. Previous studies treated this problem as a post hoc context-relation association task, limited by the absence of a golden standard corpus and prone to error propagation. To address these challenges, we propose CELESTA (Context Extraction through LEarning with Semi-supervised multi-Task Architecture), an open-set semi-supervised multi-task learning (OSSL-MTL) framework for biomedical context classification. METHODS: We designed a multi-task learning (MTL) architecture that integrates with the semi-supervised learning (SSL) strategies to leverage unlabeled data containing both in-distribution (ID) and out-of-distribution (OOD) examples. We created a large-scale dataset consisting of five context classification tasks by curating two large Biological Expression Language (BEL) corpora and annotating them with our new entity span annotation method. Additionally, we developed an OOD detector to distinguish between ID and OOD instances within the unlabeled data and applied data augmentation with an external database to enrich our dataset. RESULTS: Extensive experiments show that our framework significantly improves context classification performance. Our best OSSL-MTL models achieve F1 scores of 77.75% and 82.87% on location and disease classification tasks, and the SSL-MTL models without OOD detection perform best for cell line and cell type classification. The OOD detection experiment confirms that the OOD detector effectively identifies unknown categories while maintaining ID accuracy. Qualitative analysis shows improved extraction of implicit contexts compared to baseline models. CONCLUSION: Our analysis demonstrates the effectiveness of the framework CELESTA in improving context classification and extracting contextual information with high accuracy. The newly created dataset and code are publicly available on GitHub (https://github.com/pitt-miskov-zivanov-lab/CELESTA).

Computational modeling is important for understanding biological systems, however models of biological signaling networks are often constructed manually, limiting their size and complexity. Machine reading can extract molecular interactions of cellular networks described in the scientific literature and collected in databases to provide extensive knowledge that could be incorporated automatically into executable models if reliable methods for the assembly, extension and evaluation of models were available. Here, we evaluated the utility of breadth-first addition (BFA) and depth-first addition (DFA) algorithms when assembling or extending models with different structures: 100 randomly generated networks of Erdös-Rényi and Barabási-Albert types, and two published executable models of intracellular signaling. We simulated the assembled Candidate Models with stochastic simulator DiSH and computed their steady-state total model error (TME). The BFA and DFA methods led to maximum 65% recall, indicating that information is still missing in assembled models even when their TME is low. We examined the impact that target network structure, available baseline model information, and evaluation method have on the ability to automatically assemble and extend synthetic and published models using the breadth-first or depth-first approaches. Although achieving desired TME values, our results highlight that these methods produce simplified cellular signaling models, suggesting that more sophisticated methods are needed for the assembly, extension and evaluation of complex, dynamic, biological networks.

Development of fast methods to conduct in silico experiments using computational models of cellular signaling is a promising approach toward advances in personalized medicine. However, software-based cellular network simulation has run-times plagued by wasted CPU cycles and unnecessary processes. Hardware-based simulation affords substantial speedup, but prior attempts at hardware-based biological simulation have been limited in scope and have suffered from inaccuracies due to poor random number generation. In this work, we propose several hardware-based simulation schemes utilizing novel random update index generation techniques for step-based and round-based stochastic simulations of cellular networks. Our results show improved runtimes while maintaining simulation accuracy compared to software implementations.

Although there have been several recent attempts to automate steps of the process of model development and analysis in cell signaling networks, closing the overall cycle between information extraction, model assembly and analysis, and design of questions to guide new information search and experiments still requires a significant amount of human intervention. In this paper, we give an overview of challenges in this process, and outline our approaches to tackle these challenges.

In this work, we developed a methodology to analyze a bacteria model that mimics the stages through which bacteria change when phage therapy is applied. Due to the widespread misuse and overuse of antibiotics, drug resistant bacteria now pose significant risks to health, agriculture and the environment. Therefore, we were interested in an alternative to conventional antibiotics, a phage therapy. Our model was designed according to an experimental procedure to engineer a temperate phage, Lambda (λ), and then kill bacteria via light-activated production of superoxide. We applied formal analysis to our model and the results show that such an approach can speed up evaluation of the system, which would be impractical or possibly not even feasible to study in a wet lab.

Recent work has presented logical models and showed the benefits of applying logical approaches to studying the dynamics of biological networks. In this work, we develop a methodology for automating the design of such models by utilizing methods and algorithms from the field of electronic design automation. We anticipate that automated discrete model development will greatly improve the efficiency of qualitative analysis of biological networks.

Circuit designers are increasingly more drawn to challenges in modeling and designing biological circuits and systems. While the principles of biological organization and architecture resemble those in systems that engineers are designing, the complexity of biological systems still seems to be beyond the designed ones. This session discusses state-of-the-art in tackling such challenges, and presents existing methods for automation of model development, design and analysis of biological circuits and systems. The speakers are experts from systems biology, synthetic biology, and design automation fields. The three talks will cover a range of topics that include rule-based modeling approach to model cell signaling networks, automation of genetic circuit design, and the importance and development of standards in synthetic biology.

Transient faults in logic circuits are an important reliability concern for future technology nodes. In order to guide the design process and the choice of circuit optimization techniques, it is important to accurately and efficiently model transient faults and their propagation through logic circuits, while evaluating the error rates resulting from transient faults. To this end, we give an overview of the existing formal methods for modeling and reasoning about transient faults. We describe the main aspects of transient fault propagation and the advantages and drawbacks of different approaches to modeling them.

Transient faults in logic circuits are becoming an important reliability concern for future technology nodes. Radiation-induced faults have received significant attention in recent years, while multiple transients originating from a single radiation hit are predicted to occur more often. Furthermore, some effects, like reconvergent fanout-induced glitches, are more pronounced in the case of multiple faults. Therefore, to guide the design process and the choice of circuit optimization techniques, it is important to model multiple faults and their propagation through logic circuits, while evaluating the changes in error rates resulting from multiple simultaneous faults. In this paper, we show how output error probabilities change with increasing number of simultaneous faults and we also analyze the impact of multiple errors in state flip-flops, during the cycles following the cycle when fault(s) occurred. The results obtained using the proposed framework show that output error probability resulting from multiple-event transient or multiple-bit upsets can vary across different outputs and different circuits by several orders of magnitude. The results also show that the impact of different masking factors also varies across circuits and this information can be valuable for customizing protection techniques.

Due to reduction in device feature size and supply voltage, the sensitivity of digital systems to transient faults is increasing dramatically. As technology scales further, the increase in transistor integration capacity also leads to the increase in process and environmental variations. Despite these difficulties, it is expected that systems remain reliable while delivering the required performance. Reliability and variability are emerging as new design challenges, thus pointing to the importance of modeling and analysis of transient faults and variation sources for the purpose of guiding the design process. This work presents a symbolic approach to modeling the effect of transient faults in digital circuits in the presence of variability due to process manufacturing. The results show that using a nominal case and not including variability effects, can underestimate the SER by 5% for the 50% yield point and by 10% for the 90% yield point.

Due to reduction in device feature size and supply voltage, the sensitivity to radiation-induced transient faults of digital systems dramatically increases. In this paper, we present two approaches to evaluating the susceptibility of sequential circuits to soft errors. The first approach uses the Markov chain theory but can only provide steady-state behavior information. The second approach uses symbolic modeling based on binary decision diagrams/algebraic decision diagrams and circuit unrolling. The soft-error rate (SER) evaluation using this approach is demonstrated by the set of experimental results, which show that, for most of the benchmarks used, the SER decreases well below a given threshold (10-7FIT) within ten clock cycles after the hit. The results obtained with the proposed symbolic framework are within 4% average error and up to 11 000x faster when compared to HSPICE detailed circuit simulation. The framework can be used for selective gate sizing targeting radiation hardening, leading up to 80% SER reduction when applied to a subset of ISCAS'89 benchmarks.

Due to the shrinking of feature size and reduction in supply voltages, nanoscale circuits have become more susceptible to radiation induced transient faults. In this paper, we present a symbolic framework based on BDDs and ADDs that enables analysis of combinational circuit reliability from different aspects: output susceptibility to error, influence of individual gates on individual outputs and overall circuit reliability, and the dependence of circuit reliability on glitch duration, amplitude, and input patterns. This is demonstrated by the set of experimental results, which show that the mean output error susceptibility can vary from less than 0.1%, for large circuits and small glitches, to about 30% for very small circuits and large enough glitches. The results obtained with the proposed symbolic framework are within 7% average error and up to 5000X speedup when compared to HSPICE detailed circuit simulation. The framework can be used for selective gate sizing targeting radiation hardening which is done only for gates with error impact exceeding a certain threshold. Using such a technique, soft error rate (SER) can be reduced by 25-67% for various threshold values, when applied to a subset of ISCAS'85 and mcnc'91 benchmarks.

Due to the shrinking of feature size and the significant reduction in noise margins, nanoscale circuits have become more susceptible to manufacturing defects, noise-related transient faults, and interference from radiation. Traditionally, soft errors have been a much greater concern in memories than in logic circuits. However, as technology continues to scale, logic circuits are becoming more susceptible to soft errors than memories. To estimate the susceptibility to errors in combinational logic, the use of binary decision diagrams (BDDs) and algebraic decision diagrams (ADDs) for the unified symbolic analysis of circuit reliability is proposed. A framework that uses BDDs and ADDs and enables the analysis of combinational circuit reliability from different aspects, e.g., output susceptibility to error, influence of individual gates on individual outputs and overall circuit reliability, and the dependence of circuit reliability on glitch duration, amplitude, and input patterns, is presented. This is demonstrated by the set of experimental results, which show that the mean output error susceptibility can vary from less then 0.1% for large circuits and short glitches (20% cycle time) to about 30% for very small circuits and long enough glitches (50% cycle time)