John A. Bachman

BID Preferentially Activates BAK while BIM Preferentially Activates BAX, Affecting Chemotherapy Response

Apoptosis is a highly regulated form of cell death that controls normal homeostasis as well as the antitumor activity of many chemotherapeutic agents. Commitment to death via the mitochondrial apoptotic pathway requires activation of the mitochondrial pore-forming proteins BAK or BAX. Activation can be effected by the activator BH3-only proteins BID or BIM, which have been considered to be functionally redundant in this role. Herein, we show that significant activation preferences exist between these proteins: BID preferentially activates BAK while BIM preferentially activates BAX. Furthermore, we find that cells lacking BAK are relatively resistant to agents that require BID activation for maximal induction of apoptosis, including topoisomerase inhibitors and TRAIL. Consequently, patients with tumors that harbor a loss of BAK1 exhibit an inferior response to topoisomerase inhibitor treatment in the clinic. Therefore, BID and BIM have nonoverlapping roles in the induction of apoptosis via BAK and BAX, affecting chemotherapy response.

Programming biological models in Python using PySB

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule‐based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule‐based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high‐level, action‐oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open‐source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

New approaches to modeling complex biochemistry

Combining rule-based descriptions of biochemical reactions with agent-based computer simulation opens new avenues for exploring complex cellular processes.

Optimizing ring assembly reveals the strength of weak interactions

Most cellular processes rely on large multiprotein complexes that must assemble into a well-defined quaternary structure in order to function. A number of prominent examples, including the 20S core particle of the proteasome and the AAA+ family of ATPases, contain ring-like structures. Developing an understanding of the complex assembly pathways employed by ring-like structures requires a characterization of the problems these pathways have had to overcome as they evolved. In this work, we use computational models to uncover one such problem: a deadlocked plateau in the assembly dynamics. When the molecular interactions between subunits are too strong, this plateau leads to significant delays in assembly and a reduction in steady-state yield. Conversely, if the interactions are too weak, assembly delays are caused by the instability of crucial intermediates. Intermediate affinities thus maximize the efficiency of assembly for homomeric ring-like structures. In the case of heteromeric rings, we find that rings including at least one weak interaction can assemble efficiently and robustly. Estimation of affinities from solved structures of ring-like complexes indicates that heteromeric rings tend to contain a weak interaction, confirming our prediction. In addition to providing an evolutionary rationale for structural features of rings, our work forms the basis for understanding the complex assembly pathways of stacked rings like the proteasome and suggests principles that would aid in the design of synthetic ring-like structures that self-assemble efficiently.

Fundamental trade-offs between information flow in single cells and cellular populations

Significance The function of signal transduction networks is to detect changes in the extracellular environment and combine this with information on intracellular state and thereby control cell-fate decisions. Recent evidence suggests that high levels of biochemical noise in eukaryotic signaling networks interfere with information transmission, making it unclear how cell fate is correctly controlled. Here, we show that high noise levels are advantageous when a system needs to regulate the behavior of populations of cells using noisy biological signals. In contrast, when the key biological unit is a single cell, we show that the impact of noise on cellular responses is much less pronounced. Understanding how noise is generated and exploited advances our understanding of information processing in cells. Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3–4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases.

From word models to executable models of signaling networks using automated assembly

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.

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining

AbstractBackgroundFor automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or “grounding.” Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers.ResultsIn a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., “AKT”) and complexes with multiple subunits (e.g.“NF- κB”). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings.ConclusionFamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.

Bioentities: a resource for entity recognition and relationship resolution in biomedical text mining

Background For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or “grounding.” Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers. Results In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., “AKT”) and complexes with multiple subunits (e.g.”NF-κB”). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15% to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings. Conclusion FamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.

Abstract 1713: BAX and BAK are preferentially activated by BIM and BID, respectively, affecting clinical chemotherapy response.

Apoptosis is a highly regulated form of cell death that is essential for maintenance of homeostasis and is controlled by the BCL-2 family of proteins. Two key members of this family, the effectors BAX and BAK, can be activated by BIM or BID, which triggers their oligomerization and permeabilization of the mitochondrial membrane, a crucial event during apoptosis. Despite their essential and distinct roles in maintaining homeostasis, BIM and BID are considered to be functionally redundant in their ability to activate BAX and BAK. However, by separately examining the activation of BAX and BAK by BIM and BID we find that these apoptosis-regulating proteins exhibit differing interaction preferences. Specifically, BIM preferentially activates BAX while BID preferentially activates BAK in murine and human cells. The preferential activation is evident when treating cells with BIM and BID BH3 peptides or recombinant, full-length proteins. Calculated EC50 values demonstrate a nearly twenty-fold decrease in the BID BH3 concentration required to activate BAK rather than BAX (0.54μM for BAK versus 9.3μM for BAX) and a three-fold decrease in the BIM BH3 concentration required to activate BAX rather than BAK (6.7μM for BAK versus 2.3μM for BAX). Based on these findings, we hypothesized that cells lacking BAK would be resistant to commonly used topoisomerase inhibitors including topotecan, etoposide and doxorubicin which can induce apoptosis via activation of BID. We confirm that BAK-/-, but not BAX-/-, cells are in fact resistant to these agents but not chemotherapies that induce apoptosis independently of BID activation including the alkylating agent cisplatin, the microtubule inhibitor paclitaxel, or the nucleoside analogue gemcitabine. Topoisomerase inhibitors are frequently used to treat ovarian cancer patients who have failed initial therapy with carboplatin and paclitaxel, yet clinical responses to these second-line therapies are varied. Importantly, we found that 23.8% of ovarian primary tumors possess a heterozygous or homozygous loss of BAK1, which encodes BAK. These patients exhibit an vastly inferior overall survival when treated with topoisomerase inhibitors in the clinic (p=0.0048) while loss of BAX had no effect on survival (p=0.9534). In addition, BAK1 loss had no effect on response to treatment with agents that were not topoisomerase inhibitors including carboplatin and paclitaxel. Thus, BIM and BID have non-overlapping roles in the induction of apoptosis via BAX and BAK which affect clinical responses to chemotherapy. Citation Format: Kristopher A. Sarosiek, John Bachman, Joan Montero, Luv Patel, Joshua J. Sims, Xiaoke Chi, David W. Andrews, Peter Sorger, Anthony Letai. BAX and BAK are preferentially activated by BIM and BID, respectively, affecting clinical chemotherapy response. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1713. doi:10.1158/1538-7445.AM2013-1713

Abstract SY17-03: Inferring cell death networks from biochemical data

Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL The responses of cells to extracellular ligands results in a series of complex spatiotemporal changes in protein levels and state that vary considerably from one cell to the next. I will discuss our attempts to understand the biochemical mechanisms underlying these phenomena with a focus on TRAIL and Fas ligand in human tumor cells. These molecules are prototypical inducers of receptor-mediated (extrinsic) apoptosis and TRAIL is a first-in-class investigational therapeutic. I will discuss the development and application of computational methods for constructing, managing, calibrating and validating detailed kinetic models of core apoptotic process. With these methods we aim to go beyond the informal pictorial models that currently dominating molecular biology and create probabilistic mathematical constructs that assign rigorous “degrees of belief” to specific biochemical hypothesis given prior knowledge and a specific set of empirical data. I will discuss why some TRAIL-treated human cells die within ∼40 min, some only after 12 hr and yet others live indefinitely. We have explored three explanations for these differences: (i) genetic or epigenetic variation (ii) the involvement of one or more biochemical processes subject to stochastic fluctuation (iii) transient but deterministic differences in cell state. I will illustrate how all three interact on different time scales to determine those aspects of cellular physiology that are highly invariant and those that are variable and I will present a modeling framework for understanding the impact of specific protein factors in cell-to-cell variability. A key conclusion from this work is that variability in the responses of cells to drugs is inherent to the stochastic operation of the biochemical pathways that anti-cancer drugs target. Fractional killing is one inevitable consequence. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr SY17-03. doi:10.1158/1538-7445.AM2011-SY17-03