SUMMARY The target profiles of many drugs are established early in their development and are not systematically revisited at the time of FDA approval. Thus, it is often unclear whether therapeutics with the same nominal targets but different chemical structures are functionally equivalent. In this paper we use five different phenotypic and biochemical assays to compare approved inhibitors of cyclin-dependent kinases 4/6 – collectively regarded as breakthroughs in the treatment of hormone receptor-positive breast cancer. We find that transcriptional, proteomic, and phenotypic changes induced by palbociclib, ribociclib, and abemaciclib differ significantly; abemaciclib in particular has advantageous activities partially overlapping those of alvocidib, an older polyselective CDK inhibitor. In cells and mice, abemaciclib inhibits kinases other than CDK4/6 including CDK2/Cyclin A/E – implicated in resistance to CDK4/6 inhibition – and CDK1/Cyclin B. The multi-faceted experimental and computational approaches described here therefore uncover under-appreciated differences in CDK4/6 inhibitor activities with potential importance in treating human patients.

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation, and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage, (ii) adaptive drug resistance in BRAF‐V600E‐mutant melanomas, and (iii) the RAS signaling pathway. The use of natural language makes the task of developing a model more efficient and it increases model transparency, thereby promoting collaboration with the broader biology community.

Graphical Abstract Highlights d Implementing FAIR data standards requires identification of experimental confounders d Five labs performed the same experiment on mammalian cells and compared results d Several factors affecting reproducibility were explored d Biological context had an unexpected impact on the robustness of cell-based assays

Stem cell-derived hepatocytes may be an alternative cell source to treat liver diseases or to be used for pharmacological purposes. We developed a protocol that mimics mammalian liver development, to differentiate cells with pluripotent characteristics to hepatocyte-like cells. The protocol supports the stepwise differentiation of human embryonic stem cells (ESC) to cells with characteristics of primitive streak (PS)/mesendoderm (ME)/definitive endoderm (DE), hepatoblasts, and finally cells with phenotypic and functional characteristics of hepatocytes. Remarkably, the same protocol can also differentiate rat multipotent adult progenitor cells (rMAPCs) to hepatocyte-like cells, even though rMAPC are isolated clonally from cultured rat bone marrow (BM) and have characteristics of primitive endoderm cells. A fraction of rMAPCs can be fated to cells expressing genes consistent with a PS/ME/DE phenotype, preceding the acquisition of phenotypic and functional characteristics of hepatocytes. Although the hepatocyte-like progeny derived from both cell types is mixed, between 10–20% of cells are developmentally consistent with late fetal hepatocytes that have attained synthetic, storage and detoxifying functions near those of adult hepatocytes. This differentiation protocol will be useful for generating hepatocyte-like cells from rodent and human stem cells, and to gain insight into the early stages of liver development.

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources.We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage; (ii) adaptive drug resistance in BRAF-V600E mutant melanomas; and (iii) the RAS signaling pathway. The use of natural language for modeling makes routine tasks more efficient for modeling practitioners and increases the accessibility and transparency of models for the broader biology community. Keywords: computational modeling, natural language processing, signaling pathwaysRunning title: From word models to executable models Standfirst text: INDRA uses natural language processing systems to read descriptions of molecular mechanisms and assembles them into executable models. Highlights:• INDRA decouples the curation of knowledge as word models from model implementation • INDRA is connected to multiple natural language processing systems and can draw on information from curated databases • INDRA can assemble dynamical models in rule-based and reaction network formalisms, as well as Boolean networks and visualization formats • We used INDRA to build models of p53 dynamics, resistance to targeted inhibitors of BRAF in melanoma, and the Ras signaling pathway from natural language . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a

Stem cell-derived hepatocyte-like cells hold great potential for the treatment of liver disease and for drug toxicity screening. The success of these applications hinges on the generation of differentiated cells with high liver specific activities. Many protocols have been developed to guide human embryonic stem cells (hESCs) to differentiate to the hepatic lineage. Here we report cultivation of hESCs as three-dimensional aggregates that enhances their differentiation to hepatocyte-like cells. Differentiation was first carried out in monolayer culture for 20 days. Subsequently cells were allowed to self-aggregate into spheroids. Significantly higher expression of liver-specific transcripts and proteins, including Albumin, phosphoenolpyruvate carboxykinase, and asialoglycoprotein receptor 1 was observed. The differentiated phenotype was sustained for more than 2 weeks in the three-dimensional spheroid culture system, significantly longer than in monolayer culture. Cells in spheroids exhibit morphological and ultrastructural characteristics of primary hepatocytes by scanning and transmission electron microscopy in addition to mature functions, such as biliary excretion of metabolic products and cytochrome P450 activities. This three-dimensional spheroid culture system may be appropriate for generating high quality, functional hepatocyte-like cells from ESCs.

Multipotent adult progenitor cells (MAPCs) are adult stem cells derived from the bone marrow of mouse and rat and were described for the first time in 2002 (Jiang et al., Nature 418:41-49, 2002), and subsequently (Breyer et al., Exp Hematol 34:1596-1601, 2006; Jiang et al., Exp Hematol 30:896-904, 2002; Ulloa-Montoya et al., Genome Biol 8:R163, 2007). The capacity of rodent MAPC to differentiate at the single-cell level into some of the cell types of endoderm, mesoderm, and neuroectoderm germ layer lineages makes them promising candidates for the study of developmental processes. MAPC are isolated using adherent cell cultures and are selected based on morphology after a period of about 8-18 weeks. Here, we describe a step-by-step reproducible method to isolate rat MAPC from fetal and adult bone marrow. We elaborate on several aspects of the isolation protocol including, cell density and medium components, and methods for selecting and obtaining potential MAPC clones and their characterization.

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