Objective Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate, comprehensive models of complex cells. Methods We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in SBML. Results Our analysis revealed several challenges to representing WC models using the current standards. Conclusion We, therefore, propose several new WC modeling standards, software, and databases. Significance We anticipate that these new standards and software will enable more comprehensive models.

Toll-like receptors (TLRs) recognize pathogen-associated molecular patterns (PAMPs) and stimulate the innate immune response through the production of cytokines. The innate immune response depends on the timing of encountering PAMPs, suggesting a short-term “memory.” In particular, activation of TLR3 appears to prime macrophages for the subsequent activation of TLR7, which leads to synergistically increased production of cytokines. By developing a calibrated mathematical model for the kinetics of TLR3 and TLR7 pathway crosstalk and providing experimental validation, we demonstrated the involvement of the Janus-activated kinase (JAK)–signal transducer and activator of transcription (STAT) pathway in controlling the synergistic production of cytokines. Signaling through this pathway played a dual role: It mediated the synergistic production of cytokines, thus boosting the immune response, and it also maintained homeostasis to avoid an excessive inflammatory response. Thus, we propose that the JAK-STAT pathway provides a cytokine rheostat mechanism, which enables macrophages to fine-tune their responses to multiple, temporally separated infection events involving the TLR3 and TLR7 pathways.

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The source code is freely available at http://www.comp.nus.edu.sg/~rpsysbio/pada-gpu/

In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful implicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted from databases of biomedical publications. Most prior studies of this problem focused on the use of static information of terms and largely ignored the temporal dynamics of scientific term relations. Even when the dynamics were considered in a few recent studies, they learned the representations for the scientific terms, rather than focusing on the term-pair relations. Since the HG problem is to predict term-pair connections, it is not enough to know with whom the terms are connected, it is more important to know how the connections have been formed (in a dynamic process). We formulate this HG problem as a future connectivity prediction in a dynamic attributed graph. The key is to capture the temporal evolution of node-pair (term-pair) relations. We propose an inductive edge (node-pair) embedding method named T-PAIR, utilizing both the graphical structure and node attribute to encode the temporal node-pair relationship. We demonstrate the efficiency of the proposed model on three real-world datasets, which are three graphs constructed from Pubmed papers published until 2019 in Neurology, Immunotherapy, and Virology, respectively. Evaluations were conducted on predicting future term-pair relations between millions of seen terms (in the transductive setting), as well as on the relations involving unseen terms (in the inductive setting). Experiment results and case study analyses show the effectiveness of the proposed model.