{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T01:08:14Z","timestamp":1791248894179,"version":"4.1.0"},"reference-count":59,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","license":[{"start":{"date-parts":[[2015,9,1]],"date-time":"2015-09-01T00:00:00Z","timestamp":1441065600000},"content-version":"tdm","delay-in-days":335,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["UH2TR000496"],"award-info":[{"award-number":["UH2TR000496"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 EB 010246"],"award-info":[{"award-number":["R01 EB 010246"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2014,10]]},"abstract":"<jats:sec>\n            <jats:title\/>\n            <jats:p>Interest in \u201cengineering liver\u201d arises from multiple communities: therapeutic replacement; mechanistic models of human processes; and drug safety and efficacy studies. An explosion of micro- and nanofabrication, biomaterials, microfluidic, and other technologies potentially affords unprecedented opportunity to create microphysiological models of the human liver, but engineering design principles for how to deploy these tools effectively toward specific applications, including how to define the essential constraints of any given application (available sources of cells, acceptable cost, and user-friendliness), are still emerging. Arguably less appreciated is the parallel growth in computational systems biology approaches toward these same problems\u2014particularly in parsing complex disease processes from clinical material, building models of response networks, and in how to interpret the growing compendium of data on drug efficacy and toxicology in patient populations. Here, we provide insight into how the complementary paths of engineering liver\u2014experimental and computational\u2014are beginning to interplay toward greater illumination of human disease states and technologies for drug development. 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