Bma: Visual Tool for Modeling and Analyzing Biological Networks

Benqué, David; Bourton, Sam; Cockerton, Caitlin; Cook, Byron; Fisher, Jasmin; Ishtiaq, Samin; Piterman, Nir; Taylor, Alex; Vardi, Moshe Y.

doi:10.1007/978-3-642-31424-7_50

Cited by 32 publications

(26 citation statements)

References 22 publications

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“…Construction and analysis of the biological QN model was achieved in BMA (17). By incorporating cellspecific-context switches in the model for four cell lines, we were able to accurately model response and resistance to a pan-PIM kinase inhibitor AZD1208 and the FLT3 inhibitor AC220 and validated experimentally our predictions.…”

Section: Introductionmentioning

confidence: 82%

“…Specifically, the graphical tool Bio Model Analyzer (BMA; ref. 17; available at http://biomodelanalyzer.org/) has previously been used to encapsulate chronic myeloid leukemia (CML) cell signaling information from >150 publications in a QN model (18) able to then successfully recapitulate multiple independent experimental results. Another extension to Boolean networks is provided by Quantitative Modeling approaches, allowing variables to range over nondiscrete values and so capturing more complex relationships, but only feasible for much smaller, well-studied systems (19).…”

Section: Introductionmentioning

confidence: 99%

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Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

et al. 2017

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Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in acute myeloid leukemia (AML), which exhibits striking heterogeneity in molecular segmentation. When calibrated to cell-specific data, executable network models can reveal subtle differences in signaling that help explain differences in drug response. Furthermore, they can suggest drug combinations to increase efficacy and combat acquired resistance. Here, we experimentally tested dynamic proteomic changes and phenotypic responses in diverse AML cell lines treated with pan-PIM kinase inhibitor and fms-related tyrosine kinase 3 (FLT3) inhibitor as single agents and in combination. We constructed cell-specific executable models of the signaling axis, connecting genetic aberrations in FLT3, tyrosine kinase 2 (TYK2), platelet-derived growth factor receptor alpha (PDGFRA), and fibroblast growth factor receptor 1 (FGFR1) to cell proliferation and apoptosis via the PIM and PI3K kinases. The models capture key differences in signaling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line. Furthermore, using cell-specific models, we tailored combination therapies to individual cell lines and successfully validated their efficacy experimentally. Specifically, we showed that cells mildly responsive to PIM inhibition exhibited increased sensitivity in combination with PIK3CA inhibition. We also used the model to infer the origin of PIM resistance engineered through prolonged drug treatment of MOLM16 cell lines and successfully validated experimentally our prediction that this resistance can be overcome with AKT1/2 inhibition.

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Section: Introductionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

et al. 2017

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“…2). (For details of the UI see [3]). Although some sticking points persist, some of which we will come to later, the first of these UI modes has reached a relatively mature stage of implementation.…”

Section: The Proof-mode Problemmentioning

confidence: 99%

At the interface of biology and computation

Taylor

Piterman

Ishtiaq

et al. 2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Representing a new class of tool for biological modeling, Bio Model Analyzer (BMA) uses sophisticated computational techniques to determine stabilization in cellular networks. This paper presents designs aimed at easing the problems that can arise when such techniques-using distinct approaches to conceptualizing networks-are applied in biology. The work also engages with more fundamental issues being discussed in the philosophy of science and science studies. It shows how scientific ways of knowing are constituted in routine interactions with tools like BMA, where the emphasis is on the practical business at hand, even when seemingly deep conceptual problems exist. For design, this perspective refigures the frictions raised when computation is used to model biology. Rather than obstacles, they can be seen as opportunities for opening up different ways of knowing.

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“…Samin Ishtiaq reported on the Bio Model Analyzer or BMA (Benque et al, 2012) (available online at http://biomodelanalyzer.research.microsoft.com/) and developed by an interdisciplinary team at Microsoft Research. The BMA analyzes models from System Biology, which are program-like descriptions of networks that describe systems like skin or blood.…”

Section: Using F# To Prove Stabilization Of Biological Networkmentioning

confidence: 99%

Commercial users of functional programming workshop report

Sperber¹,

Madhavapeddy

2013

J. Funct. Prog.

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OverviewCommercial Users of Functional Programming (CUFP) is an annual workshop that is aimed at the community of software developers who use functional programming in realworld settings. This scribe report covers the talks that were delivered at the 2012 workshop, which was held in association with International Conference on Functional Programming (ICFP) in Copenhagen, Denmark. The goal of the report is to give the reader a sense of what went on, rather than to reproduce the full details of the talks. Videos and slides from all the talks are available online at http://cufp.org. Keynote: adopting functional programmingKresten Krab Thorup, CTO of Trifork, Aarhus, Denmark delivered the keynote address. He took us on the voyage he had taken from being an "object head" to "Erlang land." Thorup's foundational training in software development was all in terms of object-oriented methodologies. He went on to work on Objective C for NeXT, then earned his PhD and subsequently founded Trifork, an IT services company that currently employs 250 people and develops software solutions, provides training, and organizes several well-respected conferences.While Trifork originally capitalized almost exclusively on its Java expertise, it now successfully applies Erlang in large-scale industrial projects. Taking cues from anthropology, Thorup described how many organizations have not been able to make such transitions easily. Groups tend to gather around an idea that keeps them together, and try to keep new ideas at bay. This makes it difficult for long-time OO developers to adopt functional programming.Trifork managed to stay flexible by making learning about new ideas and communicating them as part of their regular operation. Everyone at Trifork is encouraged to spend 10% of his/her time in the structured exchange of knowledge by giving presentations, organizing meetings, give training classes, or organizing conferences.Thorup reviewed object-oriented programming and the ecosystem around it to show how it had become successful through an intuitive idea -"an object is an independent encapsulated entity that interprets inputs on its own account" -but also because of the available at https://www.cambridge.org/core/terms. https://doi

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Bma: Visual Tool for Modeling and Analyzing Biological Networks

Cited by 32 publications

References 22 publications

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

At the interface of biology and computation

Commercial users of functional programming workshop report

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