Benjamin Gyori
Research Associate, Laboratory of Systems Pharmacology, Harvard Medical School
Wednesday
09
January
2019
Download TALK slides
2:00 pm
177 Huntington Ave
11th floor
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Modeling complex biological systems with automated assembly from natural language and scientific literature

Building computational models of complex systems, including ones governing the behavior of biological cells, is a laborious process involving manual information gathering and model implementation. This makes it difficult to build models based on known causal mechanisms, creating a gap between the scope of typical models, and the scale of data they are meant to help interpret. We developed INDRA (the Integrated Network and Dynamical Reasoning Assembler), a system for automatically assembling mechanistic models directly from English language, including from the scientific literature. INDRA interfaces with natural language processing systems to extract descriptions of mechanisms from text, and uses knowledge assembly algorithms to fix certain errors, resolve redundant knowledge, infer missing information, and assess belief. INDRA then uses this knowledge base to produce models in different formalisms, including rule-based dynamical models, and causal graphs. We present applications of INDRA including (i) a model automatically built from an English language description of mechanisms which can reproduce resistance to a melanoma cancer drug (ii) a model of molecular signaling pathways built by machine-reading ~95,000 scientific publications, which can provide mechanistic explanations to drug perturbations in a cancer cell line (iii) a human-machine dialogue system in which a user can gather information and build a mechanistic hypothesis for an observed phenomenon by talking with a computer partner.

About the speaker
Benjamin M. Gyori, Ph.D. is a Research Associate in Therapeutic Science at the Laboratory of Systems Pharmacology, Harvard Medical School. His research is at the intersection of systems biology and artificial intelligence, and aims to understand, using computational approaches, how biological cells and other complex systems function and react to interventions. Ben developed INDRA, a software tool which automatically assembles biochemical mechanisms extracted from the scientific literature into explanatory models. He is also working on a human-machine communication system which allows scientists to interact with a computer partner to construct and test hypotheses about molecular systems. Ben was selected as a DARPA Riser in 2018, and has been an active performer in several DARPA programs including Big Mechanism, Communicating with Computers, World Modelers, Automated Scientific Discovery Frameworks, and Automating Scientific Knowledge Extraction. Ben obtained his Ph.D. in computational systems biology from the National University of Singapore, where he focused on the statistics and computation required to represent and reason about uncertainty in dynamical models of complex systems. Website: https://scholar.harvard.edu/bgyori
Visiting Speaker
Benjamin Gyori
Research Associate, Laboratory of Systems Pharmacology, Harvard Medical School
Modeling complex biological systems with automated assembly from natural language and scientific literature
Wed
Jan 9, 2019
Download TALK slides
2:00 pm
177 Huntington Ave
11th floor
ADD to calendar

Building computational models of complex systems, including ones governing the behavior of biological cells, is a laborious process involving manual information gathering and model implementation. This makes it difficult to build models based on known causal mechanisms, creating a gap between the scope of typical models, and the scale of data they are meant to help interpret. We developed INDRA (the Integrated Network and Dynamical Reasoning Assembler), a system for automatically assembling mechanistic models directly from English language, including from the scientific literature. INDRA interfaces with natural language processing systems to extract descriptions of mechanisms from text, and uses knowledge assembly algorithms to fix certain errors, resolve redundant knowledge, infer missing information, and assess belief. INDRA then uses this knowledge base to produce models in different formalisms, including rule-based dynamical models, and causal graphs. We present applications of INDRA including (i) a model automatically built from an English language description of mechanisms which can reproduce resistance to a melanoma cancer drug (ii) a model of molecular signaling pathways built by machine-reading ~95,000 scientific publications, which can provide mechanistic explanations to drug perturbations in a cancer cell line (iii) a human-machine dialogue system in which a user can gather information and build a mechanistic hypothesis for an observed phenomenon by talking with a computer partner.

about the speaker
Benjamin M. Gyori, Ph.D. is a Research Associate in Therapeutic Science at the Laboratory of Systems Pharmacology, Harvard Medical School. His research is at the intersection of systems biology and artificial intelligence, and aims to understand, using computational approaches, how biological cells and other complex systems function and react to interventions. Ben developed INDRA, a software tool which automatically assembles biochemical mechanisms extracted from the scientific literature into explanatory models. He is also working on a human-machine communication system which allows scientists to interact with a computer partner to construct and test hypotheses about molecular systems. Ben was selected as a DARPA Riser in 2018, and has been an active performer in several DARPA programs including Big Mechanism, Communicating with Computers, World Modelers, Automated Scientific Discovery Frameworks, and Automating Scientific Knowledge Extraction. Ben obtained his Ph.D. in computational systems biology from the National University of Singapore, where he focused on the statistics and computation required to represent and reason about uncertainty in dynamical models of complex systems. Website: https://scholar.harvard.edu/bgyori

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