Via [Migrations][migrations], I’ve found out about a really beautiful computational biology paper that very elegantly demonstrates how, contrary to the [assertions of bozos like Dembski][dembski-nfl], an evolutionary process can adapt to a fitness landscape. The paper was published in the PLOS journal “Computational Biology”, and it titled [“Evolutionary Potential of a Duplicated Repressor-Operator Pair: Simulating Pathways Using Mutation Data”][plos].
\nHere’s their synopsis of the paper:
\n>The evolution of a new trait critically depends on the existence of a path of
\n>viable intermediates. Generally speaking, fitness decreasing steps in this path
\n>hamper evolution, whereas fitness increasing steps accelerate it.
\n>Unfortunately, intermediates are hard to catch in action since they occur only
\n>transiently, which is why they have largely been neglected in evolutionary
\n>studies.
\n>
\n>The novelty of this study is that intermediate phenotypes can be predicted
\n>using published measurements of Escherichia coli mutants. Using this approach,
\n>the evolution of a small genetic network is simulated by computer. Following
\n>the duplication of one of its components, a new protein-DNA interaction
\n>develops via the accumulation of point mutations and selection. The resulting
\n>paths reveal a high potential to obtain a new regulatory interaction, in which
\n>neutral drift plays an almost negligible role. This study provides a
\n>mechanistic rationale for why such rapid divergence can occur and under which
\n>minimal selective conditions. In addition it yields a quantitative prediction
\n>for the minimum number of essential mutations.
\nAnd one more snippet, just to show where they’re going, and to try to encourage you to make the effort to get through the paper. This isn’t an easy read, but it’s well worth the effort.
\n>Here we reason that many characteristics of the adaptation of real protein-DNA
\n>contacts are hidden in the extensive body of mutational data that has been
\n>accumulated over many years (e.g., [12-14] for the Escherichia coli lac
\n>system). These measured repression values can be used as fitness landscapes, in
\n>which pathways can be explored by computing consecutive rounds of single base
\n>pair substitutions and selection. Here we develop this approach to study the
\n>divergence of duplicate repressors and their binding sites. More specifically,
\n>we focus on the creation of a new and unique protein-DNA recognition, starting
\n>from two identical repressors and two identical operators. We consider
\n>selective conditions that favor the evolution toward independent regulation.
\n>Interestingly, such regulatory divergence is inherently a coevolutionary
\n>process, where repressors and operators must be optimized in a coordinated
\n>fashion.
\nThis is a gorgeous paper, and it shows how to do *good* math in the area of search-based modeling of evolution. Instead of the empty refrain of “it can’t work”, this paper presents a real model of a process, shows what it can do, and *makes predications* that can be empirically verified to match observations. This, folks, is how it *should* be done.
\n[migrations]: http:\/\/migration.wordpress.com\/2006\/07\/12\/duplication_and_coevolutionary_modeling\/
\n[dembski-nfl]: http:\/\/scienceblogs.com\/goodmath\/2006\/06\/dembski_and_no_free_lunch_with_2.php
\n[plos]: http:\/\/compbiol.plosjournals.org\/perlserv\/?request=get-document&doi=10.1371\/journal.pcbi.0020058<\/p>\n","protected":false},"excerpt":{"rendered":"
Via [Migrations][migrations], I’ve found out about a really beautiful computational biology paper that very elegantly demonstrates how, contrary to the [assertions of bozos like Dembski][dembski-nfl], an evolutionary process can adapt to a fitness landscape. The paper was published in the PLOS journal “Computational Biology”, and it titled [“Evolutionary Potential of a Duplicated Repressor-Operator Pair: Simulating […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[24],"tags":[],"class_list":["post-74","post","type-post","status-publish","format-standard","hentry","category-goodmath"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4lzZS-1c","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/74","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/comments?post=74"}],"version-history":[{"count":0,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/74\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/media?parent=74"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/categories?post=74"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/tags?post=74"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}