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Biologiyada mexanizmlar va sabab -oqibat aloqalarining isboti

Biologiyada mexanizmlar va sabab -oqibat aloqalarining isboti


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Biologiyada statistik usullar rag'batlantiriladi: ular miqdoriy, xolis va odatda dalil sifatida qabul qilinadigan yagona usullardir.

Biroq, bu butun rasmga o'xshamaydi.

Aytaylik, kimdir qanotlarning parvoz uchun ishlatilgani haqidagi farazni isbotlamoqchi. U tajriba o'tkazadi: ikkita qush guruhini oladi, bir guruhdagi barcha qushlarning qanotlarini bog'laydi, keyin ularni derazadan uloqtiradi va qanotlari bog'langan qushlar uchmaydi degan statistik ma'lumotlarni oladi.

Biroq, qat'iy aytganda, u qushlar uchish uchun qanotlardan foydalanishini isbotlamadi: faqat qanotlari bog'langan qushlar derazadan uloqtirilganda uchmaydi. Yoki, ular aytganidek, korrelyatsiya sababni anglatmaydi.

Gipotezani natijalar bilan bog'lash uchun u muhokamaga "qanotlarning maydoni katta, ularni qoqib qo'yadigan mushaklar bor va hokazo" degan paragrafni kiritishi mumkin - boshqacha qilib aytganda, qanotlardan qanday qilib uchish mumkinligini tushuntiring. Biroq, bu tushuntirish statistik yoki o'lchovli emas: u shunchaki "hikoya" yozgan, balki aql -idrok ishlatgan bo'lishi mumkin - lekin oxirgi marta tekshirganimda, bu dalil emas.

Biologiya bo'yicha har bir maqolada shunday bo'ladi: ular qandaydir mexanizmni isbotlashga harakat qilganda, ular eksperiment natijalarini va mexanizm bilan bog'liq bo'lgan bir jihat uchun statistikani o'z ichiga oladi (bu mexanizmni o'z -o'zidan tushuntirib bera olmaydi). , lekin keyin, munozara haqida gap ketganda, ular hamma raqamlarni tashlab, hodisani "mantiqan" tushuntirishni boshlaydilar va oxir -oqibat xulosaga keladilar. Rostini aytsam, ular hech qanday tajriba o'tkaza olmadilar va darhol tushuntirishga kirishdilar.

Xo'sh, men nimani so'ramoqchi edim:

  1. Biologiyada "tushuntirish yo'li bilan isbotlash" kabi shunga o'xshash usul bormi, yoki muallif, masalan, fizika nuqtai nazaridan kelib chiqadigan oqibatlarini tushuntirib, xulosaga kelganida? Ehtimol, hatto matematik dalilga o'xshash narsa bormi? Bu ishonchli dalil bo'la oladimi?
  2. Bunday dalil uchun rasmiy talablar bormi? Ehtimol, uni miqdoriy aniqlash yoki qandaydir "formula" ga kamaytirishning qandaydir yo'li?
  3. Aytaylik, men testosteron ishlab chiqarishga ta'sir qiluvchi yangi protein haqida maqola nashr qilmoqchi edim. Agar men umuman statistika qilmagan bo'lsam yoki ba'zi odamlardan oqsil genini olib tashlash va nazorat guruhiga nisbatan nima sodir bo'lishini ko'rish kabi tajribalar o'tkazgan bo'lsam, lekin buning o'rniga oqsilning tuzilishini ajratib ko'rsatgan bo'lsam, ba'zilari bilan bog'lanish joylarini aniqladim. testosteron kashfiyotchisi va u testosteron ishlab chiqarishga kimyoviy ta'sir ko'rsatishi mumkinligini tushuntirsa, bu to'g'ri qog'oz bo'larmidi?

Sizda juda oddiy va keng tarqalgan usul yo'q. parvoz qilayotgan qushlarni plyonka qiling, keyin qanotlari parvozni ta'minlaydigan liftni ishlatishini matematik tarzda isbotlang. Bu sizning farazingiz uchun to'g'ridan -to'g'ri dalil. Siz buni qanotlarni kesish yoki qushlarni vakuumga qo'yish yoki boshqa yuzlab g'alati tajribalar orqali sinab ko'rishingiz mumkin. Siz biomexanikani ko'rib chiqishni xohlashingiz mumkin, siz izlayotgan tadqiqotlar turlari ancha keng tarqalgan. A'zolarning qanday ishlashi statistik savol emas, balki ko'proq dinamikadir.

Statistika testning ikkinchi darajali usuli bo'lib, to'g'ridan-to'g'ri usullar mumkin bo'lmaganda yoki juda amaliy bo'lmaganda qo'llaniladi. Ha, oxirgi misolni ishlatish uchun, bu oqsillar funktsiyasini aniqlashning juda to'g'ri usuli bo'lardi, cheklovlar mavjud, chunki biz odatda oqsil funktsiyalarini shunday aniqlamaymiz, chunki biz bu funktsiyani oqsilni ajratishdan oldin tez -tez kashf etamiz), hatto Qachonki siz ham buni boshqa yo'llar bilan sinab ko'rishni xohlasangiz, bu fandagi hamma narsaga to'g'ri keladi. Bu fanning bir nechta dalillar qatorini afzal ko'rishining bir sababi, chunki bitta chiziq boshqa qatordagi kamchiliklarni qoplashi mumkin.

"Tushuntirish orqali isbotlash" biologiyada o'ziga xos ma'noga ega, bu siz so'ragan narsaga to'g'ri kelmasligi mumkin*. Ko'pincha bu fanda qabul qilinishi mumkin bo'lmagan tasdiqlanmagan rivoyat tushuntirishga ishora qiladi. Bundan tashqari, siz hech qachon ilmda hech narsani isbotlamaysiz, (bundan ham ko'proq statistikada), biz isbot atamasidan qochamiz, chunki u mavjud bo'lmagan mutlaq dalilni esga oladi. Ilmda siz biror narsani namoyish qilishingiz yoki buni eng mashhur tushuntirish ekanligini ko'rsatishingiz mumkin, lekin siz buni "isbotlamaysiz". Tadqiqot - bu birdaniga sodir bo'ladigan voqealar emas, balki kumulyativ va konstruktiv, qanchalik ko'p dalillar bo'lsa, kontseptsiya shunchalik mustahkam bo'ladi.

  • Menimcha, siz haqiqatan ham so'rayapsiz, bu fan qanday ishlashi va empirizm va tekshirish nima ekanligini bilish uchun muhimroq narsadir. Ilm -fan sohasida emas, balki ilm -fan munozarasi falsafasida sizga omad kulib boqishi mumkin, chunki siz ilm -fanning qanday ishlashi haqida ko'proq adashgansiz. fan ham eksperimental, ham tavsifiy tadqiqotlarga ega, ularning har biri o'z qiymatiga ega.

Sabab va korrelyatsiya

“Ayollar salomatligi tashabbusi” dasturida ishtirok etgan 140 000 ga yaqin ayollar o‘rtasida o‘tkazilgan tadqiqotga ko‘ra, chaqaloqlarini emizgan ayollarda, chaqaloqlarini emizmagan ayollarga qaraganda, keyinchalik yurak xastaliklari va diabetga chalinish xavfi kamroq bo‘lgan. . Nyu-York Tayms maqolasining sarlavhasi “Onalarga emizishning foydasi, tadqiqot natijalari va#8221. Bu tadqiqotning to'g'ri xulosasimi?

Javob: “YO'Q”! Bu korrelyatsiya va sabab o'rtasidagi munosabatlar haqidagi umumiy tushunmovchilikning klassik holati. Ha, tadqiqotga ko'ra, emizish va hayotning keyingi davrida diabet va yurak xastaligi xavfini kamaytirish o'rtasida aniq bog'liqlik mavjud. Ammo bu emizish xavfini kamaytiradigan narsa emas. Agar bolalarini emizgan ayollar umr bo'yi sog'lig'iga ko'proq e'tibor bersalar-chi? Agar ular tez-tez mashq qilsalar yoki sog'lom ovqatlanishsa-chi?

To'g'riroq sarlavha “Ko'krak suti bilan boqish onalar uchun foydali bo'lishi mumkin, tadqiqot takliflari”. Haqiqatan ham, maqolaning o'zida, ba'zi ekspertlar (emizish va sog'liq uchun foyda o'rtasidagi bog'liqlik) sababiy bog'liqlikni isbotlamasligi va bu ta'sirning aniq sababini aniqlash uchun ko'proq tadqiqotlar o'tkazish kerakligi haqida ogohlantirmoqda. xavf).


Asosiy xususiyatlar

  • 2013 yil Amerika nashriyotlari assotsiatsiyasining PROSE mukofotlari fan bo'yicha bir jildlik ma'lumotnoma uchun faxriy yorliq
  • Turli fanlar bo'yicha yetakchilarning hissasi bilan tizim biologiyasining fanlararo tabiatini ta'kidlaydi
  • Tarjima tadqiqotlariga yordam berish uchun odam va hayvon modellarining so'nggi tadqiqot ishlanmalari kiradi
  • Hisoblash va biologik tadqiqotchilar o'rtasidagi hamkorlikni osonlashtirish uchun fanning biologik va hisoblash jihatlarini yonma-yon taqdim etadi

2. Tarixiy ma'lumot: falsafiy va ilmiy

Shubhasiz, so'nggi yarim asrda molekulyar biologiyaning o'sishi va rivojlanishi biologiyada reduktsionizmni markaziy muammoga aylantirdi (va DNK - uy so'zlari). Biroq, reduksionizmning turli jihatlari faqat mantiqiy empirik nuqtai nazardan qisqarish munozaralari bilan yonma-yon turgan biologiyaning molekulyarlashuvi ortidan e'tiborga sazovor bo'ladi, deb taxmin qilish noto'g'ri bo'lar edi (3.1-bo'limga qarang). Hayotni nimadan ajratib turadiganligi haqidagi doimiy tashvishdan tashqari, biz hayot haqidagi fanlar va uning falsafasi bilan bog'liq bo'lgan kamida ikkita reduksionistik mavzuni ajratib ko'rsatishimiz mumkin: (1) bilimning turli sohalari yoki sohalari o'rtasidagi munosabatlar va (2) uning qismlari va qismlari o'rtasidagi munosabatlar. yaxlit (Gren va Depyu 2004, Magner 1994). Ushbu ikki mavzu epistemik va ontologik qisqarish bilan murakkab tarzda bog'lanadi. (Metodik qisqartirish haqidagi savollar mikroskop yordamida past darajadagi kuzatuvlarni amalga oshirish kabi reduktsionist tadqiqot usullarini qo'llash imkoniyatini ochadigan yangi texnologiyalar atrofida birlashishga moyildir.) Bundan tashqari, bu mavzular doimiy qiziqishning o'ziga xos sohalari kontekstida paydo bo'ladi: ( a) turli xil hayvonlar va o'simliklar o'rtasidagi tabiiy muhitdagi murakkab munosabatlar, ya'ni & ldquoekologiya va rdquo, (b) organizm qismlari va butunligi o'rtasidagi yaxlit munosabatlar, ya'ni & ldquofiziologiya/funktsional anatomiya va rdquo, va (c) bir jinsli dinamik munosabatlar Embrionning boshlang'ich bosqichidagi komponentlar, natijada mos keladigan va bog'langan holda heterojen qismlarni o'z ichiga olgan yaxlit butun organizmni tug'diradi, ya'ni.

Aristotel reduksionizmga taalluqli ikkala falsafiy mavzuning asosidir. U o'zining yuqori va bo'ysunuvchi fanlar kontseptsiyalarida ilmiy bilimlar sohalari o'rtasidagi munosabatni ko'rib chiqdi (Orqa tahlil I). Har bir fanning oʻziga xos predmet turi, birinchi tamoyillari va predmet turkumiga mos keladigan predikatlar toʻplami mavjud (McKirahan 1978, 1992, 4-bob&ndash5). Garchi fanlar o'rtasidagi munosabatlar tizimli bo'lsa -da, ular umuman kamaytiruvchi emas, chunki fundamental fan yo'q. Bu aloqalar qismlar va butunlar o'rtasidagi munosabatlarga taalluqlidir, chunki Aristotel o'z elementlari va ularning potentsiallari haqidagi fanidan foydalangan (masalan, rasmda ko'rinib turganidek). Avlod va korruptsiya haqida va Meteorologiya) organizmning tarkibiy qismlarini tavsiflash va tahlil qilish Hayvonlar tarixi va Hayvonlarning qismlari. U uchta kompozitsion va yuqori darajali elementlarni va ularning potentsiallarini qon va suyak kabi bir xil qismlarni va yuz yoki qo'llar kabi bir xil bo'lmagan qismlarni o'z ichiga olgan bir xil bo'lmagan qismlarni davoladi. Bu erda asosiy domen - bu fiziologiya/funktsional anatomiya, lekin rivojlanish/ko'payish har doim ko'zda tutiladi va to'g'ridan -to'g'ri bo'limda ko'rib chiqiladi Hayvonlarning avlodi. & Ldquopotentials va rdquo haqidagi da'volar bir xil qismlarni tashkil etuvchi moddiy xususiyatlarga qo'shgan hissalari bilan tasdiqlanadi (Meteorologiya IV Lennox 2001, bobga qarang. 8 Popa 2005). Murakkab holatlar (masalan, qizilo'ngachning go'shtli, egiluvchan egiluvchanligi) ushbu moddiy substratlardan kelib chiqadi, lekin ularning tabiatini aniqlamaydi, chunki funktsional qat'iylik ularni oxirigacha boshqaradi. qizilo'ngach uchun ovqatni iste'mol qilish paytida takroriy kengayish).

Aristotel va biologiyaning teleologik yo'nalishi shuni ko'rsatadiki, u ko'pincha ontologik anti-reduktsionist sifatida tavsiflanadi (masalan, tashkilotning quyi darajadagi elementar potentsiali tashkilotning yuqori pog'onalarida bir xil bo'laklarni aniqlamaydi). Bu ma'lum sohalar bilan bog'liq epistemik muammolar bilan uyg'unlashadi, masalan, uning rivojlanish davridagi hayvonlar morfologiyasining kelib chiqishi haqidagi Sokratik davrgacha bo'lgan materialistik (ya'ni, sof &ldquomechanistic&rdquo) tushuntirishlarini rad etishi. (Empedokllarning ta'kidlashicha, umurtqalar ontogenez jarayonida faqat birlashgan silindrsimon ustunning umurtqa pog'onasining kuzatilishi mumkin bo'lgan takrorlangan birliklariga jismoniy sinishi natijasida paydo bo'ladi. Hayvonlarning qismlari I.1 [640 a ].) Shunga qaramay, Aristotel hayvonlarning qismlari tomonidan namoyon bo'ladigan xususiyatlarning tabiatidan kelib chiqadigan moddiy tarkib va ​​cheklovlarga e'tibor qaratgan, bu ko'pincha ontologik va epistemik qisqarish turlariga majburiyatlarni belgilaydi. Uning faraziy zarurat haqidagi tushunchasi (Fizika II Qarang: Cooper 1987) organizmning turli qismlarida bir turdagi materialdan foydalanishga ruxsat berdi. Galen, Aristoteldan keyingi ko'plab boshqa narsalar qatorida, bu masalalarni, moddiy komplekslar yuqori darajadagi xususiyatlarni qanday yaratishi nuqtai nazaridan ham o'rgangan.Aralashmalar) yoki rivojlanish kontekstidagi qismlar va butunlar o'rtasidagi munosabatlar (Embrionning qurilishi).

Ilk zamonaviy davrda Uilyam Xarvi ham fiziologiya, ham rivojlanish kontekstida Aristotelcha pozitsiyani qabul qildi (Lennox 2006). Ontogenezdagi qismlar va butunlar o'rtasidagi munosabatlar, shu jumladan tegishli moddiy xususiyatlar doimiy ravishda ko'rib chiqiladi (Harvey 1981 [1651]). Garvi, "tishli qulflar" yoki yurakning o'zi nasos sifatida yurakning harakatiga ishora qilib, "ldquomechanistic" va "rdquo" o'xshashliklarini ishlatgan bo'lsa -da, "Ren va eacute Descartes" mashhur "Xarvi" va "rsquos" da'volarini "mexanik falsafa" nuqtai nazaridan bahslashdi. Dekart yurakning harakatini funktsiya nuqtai nazaridan emas, balki faqat aylanib yuruvchi qonni tashkil etuvchi harakatdagi materiyaga murojaat qilish orqali tushuntirishga harakat qildi, u qizdiriladi va shuning uchun fermentatsiyaga o'xshaydi (Inson tanasining tavsifi). Shuningdek, u embriologiyani faqat materiya va harakat nuqtai nazaridan tushuntirishga harakat qildi, masalan, to'qimalar va organlarning paydo bo'lishi embrionning turli mintaqalarida materiyaning sekinlashishi va to'planishi orqali hisobga olinadi (Des Chene 2001, 2-chi Smit 2006, II qism). Robert Boyl anatomiya va fiziologiyadagi teleologik tushuntirishlarni (xususan, butun organizmning qismlari va butun qismlari o'rtasidagi integratsiyalashgan munosabatlar) mexanik tushuntirishlar bilan qanday mos kelishini va ularni qo'llab-quvvatlaganligini ko'rsatib himoya qildi (Lennox 1983). Shunday qilib, u mexanik falsafa kontseptsiyasiga muvofiq epistemik qisqarish versiyasini taklif qildi, unga heterojen metodologiya (reduktsionist va reduktsionist bo'lmagan) va ontologik reduktsiya haqidagi bir vaqtning o'zida aytilgan fikrlar kiritilgan. Baruch Spinoza 1665 yilda Genrix Oldenburgga yozgan maktubida butun munosabatlar va ularning murakkabligi mavzusini o'rganish uchun qon oqimidagi qurt tasvirini ishlatgan. [3]

Immanuel Kant organizm faoliyatining teleologik qarashlari (shu jumladan, ekologik munosabatlar, fiziologiya va rivojlanish) va Nyuton mexanikasida modellashtirilgan sabab bog'lanishning mexanik tushunchasi o'rtasidagi dialektikani aniq ifodalagan.Hukmni tanqid qilish, II qismga qarang: Mensch 2013, Zukert 2010). Kant va uning tanqidiy falsafasida oqlangan chiziqli-mexanik nedensellik ("ldquoteleologiya va rdquo") qismlarining o'zaro sababiy munosabatlari o'rtasidagi bu taranglikni saqlab qolish epistemologik va metafizik masalalarni ajratish usuli edi. U epistemik antireduksionizm (organizm jarayonlarini ularning tizimli maqsadga o'zaro hissasi va mexanizmning teleologiyaga gnoseologik bo'ysunishi nuqtai nazaridan tushunish kerak) va ontologik reduksionizm (barcha sabablar oxir-oqibat chiziqli-mexanik) elementlari bilan gibrid pozitsiya sifatida tavsiflanishi mumkin. ). Ammo Kant chiziqli-mexanik sababni ong dunyoning tajribalarini qamrab oladigan toifa sifatida tushungan, shuning uchun uning metafizikani tushunishi ontologik pasayish haqidagi eng zamonaviy hisob-kitoblardan farq qiladi. Qismlar va butunlar o'rtasidagi reduktiv munosabatlar mavzusiga qo'shimcha ravishda, Kant o'zining alohida tushunchalari va predmetiga ega bo'lgan fanlarni muhokama qilishda turli ilmiy sohalar o'rtasidagi munosabatlarni ham ko'rib chiqdi (Hukmning tanqidi, 68 -bo'lim, 79 -bob).

Kantning ishi biologik hodisalarni o'rganuvchilar orasida, hatto turli yo'llar bilan noto'g'ri talqin qilingan bo'lsa ham, ta'sir ko'rsatdi va 19-asr boshlarida teleologiya va mexanizm masalasini shakllantirishga yordam berdi (Lenoir 1982). Uning munozarasi, agar biz ularni ilmiy tadqiq qilmoqchi bo'lsak, organizmlarni tabiiy maqsad sifatida qabul qilish uchun metodologik xulosalarning ahamiyatini ta'kidladi. Bu Nyuton mexanikasida topilganlarga o'xshash, biologik hodisalarni &ldquomechanisticically&rdquo tushuntirib beradigan yangi kuchlar haqidagi taxminlarni rag'batlantirdi (masalan, embrion rivojlanishini tushuntiruvchi shakllantiruvchi kuchga qarang. Look 2006, Richards 2000). Nyutonning ilhomi, Kant izlari bilan yoki bo'lmasdan, 19-asr va undan keyin ham saqlanib qoldi. Taqqoslash anatomiyasi (funktsional anatomiya va embriologiyani o'z ichiga olgan holda keng tushunilgan) muvaffaqiyatli fizika fanining uslubiy takliflaridan foydalangan. Masalan, Geoffroy Sent -Xileyr Nyuton va rsquosga hayvonlar mexanikada bo'lgani kabi o'xshash tamoyillar yoki qonunlar bilan boshqarilishini so'radi (Le Guyader 2004). Richard Ouenning tirik organizmlar ichidagi materiya va uning xossalarini muhokama qilish odatiy hol bo'lib, u aniq antireduksionistik pozitsiyani namoyon etadi va paydo bo'ladigan xususiyatlarga o'xshash narsalarni postulat qiladi. [4] Bu nuqtai nazarni & ldquoanaliz: sintez va rdquo fikrlash uslubidan foydalangan ko'plab qiyosiy anatomistlar ham bo'lishgan (Elvik 2007). Reduksionizm bilan bog'liq bo'lgan yo'nalish 19-asrdagi fiziologik tadqiqotlar orqali o'z yo'lini oladi. Bu erda Kartezian mexanik-reduksionistik dastur hayvonlar mashinasiga urg'u berish bilan qayta qo'shiladi (Canguilhem in Delaporte 1994, 5, 8, 10, 12 Coleman 1977, 6-b). Ontologik qisqartirish shakliga aniq metafizik majburiyatlarga qaramay, reduksionizmning uslubiy turlari eksperimental dizaynga ta'siri tufayli malakali edi (masalan, Bernard 1957 [1865]). Umuman olganda, 19-asrda ontologik, epistemik va metodologik turlarga mos keladigan mexanik (va ldquoreduktionist va rdquo) va organik (& ldquonon-reduktsionist va rdquo) pozitsiyalar turlicha bo'lgan, bugungi kunda kuzatiladigan falsafiy xilma-xillikka o'xshaydi (quyida 3 va 5-bo'limlarga qarang). .

19 -asr oxiri - 20 -asr boshlarida rivojlanish mavzusi hayotiylik (ba'zan "ldquoorganicism & rdquo" deb nomlangan) mojarosida qasos bilan qaytdi. Bu vaqtda tirik tizimlarni ko'proq mexanik yoki materialistik talqin qilish uchun umumiy harakat paydo bo'ldi (masalan, Loeb 1912, Allen 1975 yil) va rivojlanishni tushuntirish borasida kelishmovchiliklar katta edi (Maienschein 1991). Biroq, Hans Drieschning organizmning rivojlanish va avtonomiyasini talqin qilishidagi mashhur vitalizm misoli metafizika kabi epistemologiya masalasi sifatida ko'rilishi kerak (Maienschein 2000). Vitalizmga qarshi materializm (ya'ni, ontologik qisqartirish savollari) o'rniga tushuntirish mojarosi erta ontogenezdagi differentsiatsiyaning mohiyatini va u qanday darajada oldindan belgilab qo'yilganligini o'z ichiga oladi. Tirik tizimlardagi tartib va ​​tashkilot mavzusi hozirgi vaqtda reduksionizmning metodologik, gnoseologik va ontologik turlari haqida ko'plab asarlarni qamrab oladi (fizika va biologiya o'rtasidagi, ham qismlar va butunlar o'rtasidagi munosabatlarga urg'u beriladi). Misollar, Jozef Needham va rsquos Tartib va ​​hayot (1936), Kurt Goldshteyn va rsquos Organizm (1934/1963), E.S. Rassell va rsquos Rivojlanish va irsiyatning talqini (1930), D&rsquoArcy Tompson &rsquos O'sish va shakl haqida (1917), Lyudvig fon Bertalanffi Zamonaviy rivojlanish nazariyalari (1933) va J.H. Woodger va rsquos Biologik tamoyillar (1929). Zamonaviy ingliz-amerika falsafasiga kam ta'sir ko'rsatgan fan faylasuflari bu munozaralardan xabardor edilar. Masalan, Ernst Kassirer (1950, II qism) rivojlanishning sababli tushuntirishlari bilan bog'liq bo'lgan reduktsionistik mavzularni, shu jumladan Drish atrofidagi mexanizm va hayotiylik haqidagi munozaralarni, shuningdek fizika va biologiya o'rtasidagi munosabatlarni o'rganib chiqdi. Ushbu mavzular sahifalarida ham paydo bo'ldi Fan falsafasi (masalan, Lilli 1934, 1942, 1948, Singer 1934, 1946), garchi aniq qisqartirish terminologiyasini ishlatmasa ham.

Ernest Nagelning 11 va 12-boblarida reduksionizmga umumiy munosabat Fanning tuzilishi (1961) bu ko'p yillik munozaralarning turli jihatlarini o'rganib chiqdi. Odatda nazariyani qisqartirish bo'limlari nazarda tutilgan bo'lsa -da (bilim sohalari bilan bog'liq reduktsionistik mavzu bilan bog'lansa), Nagel biologik adabiyotlar bilan o'zaro aloqada bo'lib, uning qismlari, yaxlitligi va paydo bo'lishi haqidagi da'volarni ko'rib chiqdi va shu bilan mashhur bo'lgan ikkinchi reduktsionistik mavzuni ko'rib chiqdi. Aristoteldan beri (qarang: Nagel 1961, 366ff). U muvaffaqiyat yoki muvaffaqiyatsizlikka oid da'volarga xos bo'lgan &ldquowholes&rdquo, &ldquoparts&rdquo va &ldquosums&rdquo ning ko'p ma'noliligini ta'kidlab, butunning faqat uning qismlari yig'indisi ekanligini ko'rsatdi (380-bet). Bundan tashqari, Nagel evolyutsion paydo bo'lish masalasini ko'rib chiqadi, ya'ni bizning koinotimiz va Yerdagi hayot tarixida chinakam yangi mavjudotlar paydo bo'ladimi (qarang: Goudge 1961). U yuqorida aytib o'tilgan nazariy biologlar (masalan, Bertalanffy, Russell va Woodger) bilan tirik tizimlarning ierarxik tashkil etilishi va qisqarishi mavzusida, ayniqsa ontogenezda (Nagel 1961, s. 432ff) ko'rib chiqilgan. Nagel va rsquos zamondoshlari ham xuddi shunday reduktsionizm mavzularini ko'rib chiqishdi, lekin ularning ishlari deyarli e'tiborga olinmagan (masalan, Morton Bekner & rdquo & ldquoorganization & rdquo va & ldquolevels tahlillari & rdquo munozarasi Beckner 1959, ch. 9).

Nagel & rsquos keng ko'lamli tahliliga qaramay, keyingi falsafiy munozaralar uning nazariyasini qisqartirish (3.1-bo'limga qarang) va ilmiy tadqiqotning barcha sohalari uchun reduktsionizmning etarli umumiy tavsifi bo'lib xizmat qiladimi-yo'qmi, unga bag'ishlangan. [5] Eng ko'p o'rganilgan biologik holat - bu klassik va molekulyar genetika o'rtasidagi munosabatlar, chunki qisman bu tarixiy davrda genetikaning molekulyarizatsiyasi sezildi. Nagelian nazariyasini genetikaga qo'llashda yuzaga kelgan ko'p qiyinchiliklar (4 -bo'limga qarang) biologiya falsafasining mustaqil intizomiy mutaxassislik sifatida o'sishiga turtki bo'ldi, chunki bu qiyinchiliklar takrorlanuvchi naqshga va biologik mulohazalarni fizikada yaratilgan fanning falsafiy bayonlariga mos keladiganga o'xshardi. fan misollari (Hüttemann and Love 2011, Love and Hüttemann 2011). Biologiyadagi falsafiy masalalar biologiyada empirik tadqiqotlarga sezgirroq bo'lgan aniq tahlillarni talab qilganday tuyuldi (Brigandt 2011). Bundan tashqari, sotsiobiologiya va gen-markazli evolyutsion tushuntirishlarning yuksalishi biologlar orasida ijtimoiy va siyosiy majburiyatlar asosida reduksionizmga qarshi qarashlarni keltirib chiqardi (masalan, Levins va Levontin 1985).

Reduksionizmga so'nggi e'tibor joyida turli xil hayotiy fanlar kontekstlari ko'plab faylasuflarni reduktsionizmning ko'p yillik aspektlarini qayta ko'rib chiqishga olib keldi, ularning ko'pchiligi nazariyani qisqartirish borasidagi bahs -munozaralarga tangensial edi. Bir qator hozirgi munozaralar, aslida, turli yo'llar va turli kontekstlarda bo'lsa ham, e'tibordan chetda qolgan masalalarga ongsiz ravishda qaytishdir. Rivojlanish biologiyasi munozaralar markaziga qaytgani ajablanarli emas, chunki u biologik mavzu sifatida reduktsionist mavzularda aks ettirishni taklif qiladi. Xuddi shu narsani ekologiya va funktsional anatomiya haqida ham aytish mumkin, garchi ular hozirgi paytda ko'plab faylasuflar uchun ko'proq periferikdir. Ijtimoiy va siyosiy jihatdan biologik tadqiqotlar, masalan, irsiy toifalarga asoslangan irqiy tasniflar, falsafiy tahlillarni qo'zg'atishda davom etmoqda (5 -bo'limga qarang).


Tibbiyot va fan uchun talqin qilinadigan sun'iy intellekt (AIMS)

Bizning maqsadimiz kontseptual jihatdan va asosan Yangi, istiqbolli va rag'batlantiruvchi savollarni hal qilish orqali AIni biotibbiyot fanlari bilan qanday qilib integratsiya qilish mumkinligini oldinga suring. Misol uchun, bizning yaqinda olib borgan tadqiqotlarimiz uchun usullar ishlab chiqilgan tushuntiring Asosiy biologiya fanlaridan kasallik biologiyasigacha, yotoqxonadagi ilovalargacha bo'lgan keng ko'lamli muammolar uchun sun'iy intellekt asosidagi bashorat yoki xulosalar. Chuqur neyron tarmoqlar kabi sun'iy intellekt modellari biotibbiyot fanlarini o'zgartirmoqda, ammo ularning qora qutisi tabiati AIning biotibbiyotda keng qo'llanilishiga to'sqinlik qiladigan taniqli muammo bo'lib kelgan. Masalan, bu modellar biologiya va tibbiyotdagi asosiy savollarga javob bermaydi sababiy munosabatlar yoki yashirin o'zgaruvchi biologik jihatdan nimani anglatadi.

Biologiya va tibbiyotda sun'iy intellektni qo'llashning asosiy yo'nalishi bemorning natijalarini yoki individual fenotipini aniq bashorat qilish (masalan, bemorning gen ekspresyon profili asosida ma'lum kimyoterapiyaga javobini bashorat qilish) bo'lganida, biz alohida e'tibor qaratdik. nima uchun ma'lum bir bashorat qilingan, bu tibbiyot mutaxassislariga tegishli klinik harakatlar to'g'risida tashxis qo'yish yoki qaror qabul qilishga yoki individual fenotipga asoslangan molekulyar mexanizmlarga ishora qilishga yordam beradi. Bizning ishimizning bu yo'nalishi juda ko'p havola qilingan nashrlarga olib keldi: (1) ning muqovali maqolasi Tabiat biotibbiyot muhandisligi , 2018 yil oktyabr (2 yil davomida 17 marta 4 marta havola qilingan), (2) muqovasi maqolasi Tabiat mashinasi intellekti , 2020 yil yanvar (1 yil davomida 19 marta 6 marta havola qilingan, 289 va 51 marta oldingi bosma versiyasi keltirilgan) va (3) dagi maqola Tabiat bilan aloqa , 2018 yil yanvar (F1000 tomonidan tavsiya etilgan 3 yil davomida 75 marta havola qilingan). AIning bashoratini talqin qilish bo'yicha AI asosiy tadqiqotlari olib keldi (4) og'zaki taqdimot uchun tanlangan SHAP tizimi bo'yicha maqola (eng yuqori 1%) NeurIPS (Neyron axborotni qayta ishlash tizimlari ) 2017 yil dekabr oyida (3 yil davomida 2 048 marta iqtibos keltirildi), uni tibbiyot, biologiya, moliya, informatika va boshqalar olimlari keng qo'llashadi (ODSC Open Data Science Award ཏ).

Ba'zi ta'kidlangan tadqiqotlarimiz orasida (i) Altsgeymer kasalligi uchun terapevtik maqsadlarni topish (matbuotdagi maqolalar), (ii) bemorning o'z molekulyar profiliga asoslangan saraton kasalligini davolash ( Tabiat aloqalari 2018 F1000 tomonidan tanlangan), (iii) asosiy ML tushuntiriladigan AIda ishlaydi ( NeurIPS 2017 yil dekabr , to'liq Oral (yuqori 1%) va 2019 yil iyun holatiga 233 marta havola qilingan Tabiatning mashina razvedkasi , muqovali maqola) va (iv) operatsiya paytida asoratlarning oldini olish ( Tabiat BME 2018 , muqovali maqola), (v) buyrak kasalliklarini bashorat qilish ( Tabiatning mashina razvedkasi , muqovali maqola), (vi) travma bilan og'rigan bemorlar uchun gospitalgacha bashorat qilish va (vii) saraton biologiyasi, (viii) inson genomi va (ix) genlarni tartibga solish tarmoqlari haqidagi tushunchamizni yaxshilash. Li Lab UW tibbiyot maktabi, Allen institutlari, Garvard tibbiyot maktabi va boshqalarda biomedikal tadqiqotchilar bilan hamkorlik qilmoqda.

Hisoblash biologiyasi va tibbiyot (bioinformatika)- aniq dori , tarmoq biologiyasi va amp genetika

Mashinani o'rganish - tushuntiriladigan AI, izohlanadigan ML , xususiyatlarni tanlash va ehtimollik grafik modellari


Usullari

Bizning tahlilimizning sxematik ko'rinishi 5 -rasmda keltirilgan. Agar boshqacha ko'rsatilmagan bo'lsa, barcha tahlillar R v3.4.1 46 bilan o'tkazilgan.

Biz Milieu Intérieur loyihasidagi 164 ta immunitetli hujayra fenotipini BLUEPRINT konsortsiumining monotsitlar, neytrofillar va T hujayralaridagi gen ekspression xususiyatlariga qiyosladik. Biz birinchi navbatda bir xil genetik lokusdagi assotsiatsiyalarga ega bo'lgan immunitet belgilari va gen ekspresyon xususiyatlarining juftlarini tanladik, so'ngra qaysi belgi juftlari genetik assotsiatsiyaga ega ekanligini va qaysilari yaqin joylashgan turli genetik variantlar bilan bog'liqligini aniqladik. Poligenik xavf skorlari (PRS) yordamida biz umumiy genetik assotsiatsiyalarga ega bo'lgan belgilar juftligi kengroq genetik korrelyatsiyaga ega bo'lishini aniqladik. Bundan tashqari, ikkita turli xil Mendelian randomizatsiya yondashuvlaridan foydalanib, biz genetik assotsiatsiyalarga ega bo'lgan belgilar juftliklari sabab-oqibat munosabatlarini bo'lishish ehtimoli ko'proq ekanligini aniqladik. Qisqartmalar: eQTL = ifoda miqdoriy xususiyat joyi, JLIM = Birgalikda ehtimollik xaritasi.

Milie Intérieur loyihasi immunofenotipli ma'lumotlarni qayta ishlash

Biz Milieu Intérieur loyihasidan G'arbiy Evropaning 816 nafar sog'lom, qarindoshi bo'lmagan odamlar uchun 166 ta immun fenotipi (75 ta tug'ma immun hujayra parametrlari, 91 ta adaptiv immun hujayra parametrlari 1-jadvalga qarang) uchun hisoblangan genotip ma'lumotlari va oqim sitometriyasi o'lchovlarini oldik. Nol bo'lmagan qiymatlar sonining kamligi tufayli ikkita fenotipni olib tashladik (Qo'shimcha 12a, b-rasm). Qolgan fenotiplarning bittasidan tashqari hammasi normal taqsimlanmaganligini aniqladik (Shapiro-Uilk testi), shuning uchun biz barcha fenotiplarda daraja-teskari transformatsiyani amalga oshirdik. Ushbu transformatsiyadan so'ng, to'rtta fenotip hali ham noan'anaviy taqsimotni ko'rsatdi. Vizual tekshiruvdan so'ng, bu fenotiplarning ikkitasi-B-hujayralar soni va HLA-DR + /CD4 + EMRA T-hujayralari soni (qo'shimcha 12c, d-rasm), odamlarning katta guruhida aniqlanmaydi. Shuning uchun biz ularni ikkilik aniqlangan/aniqlanmagan fenotiplarga qisqartirdik. Har bir fenotip uchun biz aniqlash chegarasini eng yuqori qiyalikka ega bo'lgan kvant-kvantil uchastkasining nuqtasi sifatida aniqladik (qo'shimcha 13-rasm). Boshqa ikkita fenotip Shapiro-Vilk testiga qaramay (taxminan 12e, f) taxminan normal taqsimotga ega edi, shuning uchun biz ularni boshqa o'zgartirmadik.

HumanOmniExpress-24 BeadChip yordamida 816 kishining hammasiga genotip qilingan va ularning ko'pchiligi (kohortadagi birinchi 1.000 kishidan 966 tasi) ham HumanExome-12 Beach Chip yordamida genotip qilingan va sifat nazorati (QC) va genotip imputatsiyasi o'tkazilgan. 17 -nashrda tasvirlanganidek, IMPUTE Score & gt0.8 va kichik allel chastotasi (MAF) va gt 0,05 bo'lgan 5 699 237 SNPlarning yakuniy ma'lumotlar to'plamini beradi. Biz qo'shimcha QC o'tkazdik, ortiqcha heterozigotlik namunaviy o'rtacha qiymatdan beshdan ortiq standart og'ishlarga ega bo'lgan barcha shaxslarni olib tashladik (n = 6), har bir juftlikdan bittadan namuna o'zaro bog'liqlikni ko'rsatadi (kelib chiqishi bo'yicha bir xil (IBD) va gt 0.1875, n = 3) va 4 dan ortiq standart og'ishning dastlabki to'rtta asosiy komponentlaridagi masofaga ega bo'lgan populyatsiyaning chegaralari (n = 10). Ushbu QC bosqichlarining barchasi MAF & gt 0.05, genotiplanish tezligi va gt98% va Hardy -Weinberg muvozanati (HWE) testiga ega bo'lgan bir nechta variantlarga asoslangan. p-qiymat & gt 1 × 10 -3 va juftlikdagi bog'lanish muvozanati (LD) & lt 0,2. To'liq ma'lumotlar to'plamidan biz barcha variantlarni Hardy-Vaynberg muvozanatidan olib tashladik (p < 1 × 10 −5 ) va MAF < 0,05 va kiritishlar, oʻchirishlar va multiallel variantlari. Bizning yakuniy ma'lumotlar to'plamimiz 797 kishidan iborat 5,231,477 ta variant edi.

BLUEPRINT ifodasi QTL ma'lumotlarini qayta ishlash

Biz kasalliksiz va Buyuk Britaniya vakili ekanligi aniqlangan BLUEPRINT konsorsiumidan sodda CD4 + T hujayralari (169 kishi), CD14 + monotsitlar (193 kishi) va CD16 + neytrofillar (196 kishi) uchun RNK-sek ma'lumotlarini oldik. Buyuk Britaniya) aholisi 18 . Biz FASTQ fayllarini yukladik va GTEx quvur liniyasini RNK-seqni tekislash, miqdor va sifat nazorati uchun ishlatdik (https://www.gtexportal.org/, V8 uchun tahlil usullari). Qisqacha aytganda, biz GENCODE 26 annotatsiyasi va RNK-SeQC v1.1.9 48 bilan gen darajasidagi miqdorni aniqlash asosida STAR v2.5.3a 47 yordamida GRCh38/hg38 inson maʼlumotnomasiga moslashtirishni amalga oshirdik. Biz GTEx quvur liniyasida tasvirlangan o'qish sonlari va "millionga transkript" (TPM) qiymatlarini ishlab chiqardik. Keyin biz ekspluatatsiya qiymatlari & gt0.1 TPM va ≥6 bo'lgan genlarni tanladik va namunalarning kamida 20% ini o'qib chiqdik va namunalar o'rtasida normallashtirdik. chekkaR 49. Biz teskari normal transformatsiyadan foydalanib, namunalar bo'yicha ifoda qiymatlarini normallashtirdik. Barcha namunalar kamida 10 million noyob o'qishga ega edi. BLUEPRINT ma'lumotlar relizidan biz butun genom ketma-ketligi orqali olingan 7 008 524 variant uchun barcha shaxslar uchun genotip ma'lumotlarini oldik. Ketma-ketlik, hizalama, variantni chaqirish va sifat nazorati 18-asl nashrda tasvirlanganidek amalga oshirildi. Biz qo'shimcha ravishda qo'shish/o'chirish va ko'p bayroqli variantlarni va MAF & lt 0,05 va Hardy-Weinberg muvozanatiga ega bo'lgan ikkita variantni filtrdan o'tkazdik. p-qiymati & lt1 × 10 -5. We performed sample QC as described above for the MIP dataset, which did not lead to the removal of any individuals, yielding a final genotype data set of 197 individuals and 4,853,096 single nucleotide polymorphisms (SNPs) (GRCh37 build). In total, we found 4,355,418 SNPs present in both the BLUEPRINT and the Milieu Intérieur project data sets.

Both the BLUEPRINT and the Milieu intéreur project genotype data sets were available in the GRCh37 build, but version 8 of the GTEx pipeline for RNA-seq alignment and quantification uses GRCh38. We reconciled the different genome builds by back-lifting the RNA-seq data to GRCh37, determining the transcription start site for each gene with R/BiomaRt v2.34.3 50,51 . Allele inconsistencies between the two data sets were resolved by transforming the regression coefficients accordingly and ambiguous SNPs were removed for the PRS.

Association analyses

We performed all association regression analyses with plink v1.9 52 , assuming an additive model of inheritance for all variants. We adjusted all regression analyses on the immune phenotypes from the MIP data set for age, sex, as well as two environmental factors—smoking (0=Non-smoker, 1=Ex-Smoker, 2=Smoker) and latent CMV infection (CMV serology 0=negative, 1=positive)—as these have been identified as the main non-genetic factors affecting immune phenotype variation in the original study 17 . Additionally, we corrected the regression models for the top five principal components to adjust for population stratification. For the association analyses on gene expression data (eQTL analyses) we included age, sex, the first five principal components as well as 30 PEER factors 53 (calculated as described in the GTEx pipeline) as covariates. We used the same covariates to generate permutation data for JLIM.

Identifying immune and gene expression trait associations in the same locus

JLIM compares association data for a primary trait to association data for a secondary trait. In all analyses, we use the Milieu Intérieur immune phenotypes as primary traits and BLUEPRINT gene expression traits as secondary. We thus first identify potential associations in the immune phenotypes and then look for overlapping BLUEPRINT eQTLs.

We first identified all independent immune phenotype associations by selecting lead SNPs that (i) had suggestive levels of association (p < 1 × 10 −5 ) (ii) are not within 100 kilobases from another lead SNP and (iii) are not within 500 kilobases from another lead SNP and in LD (r 2 > 0.2) with another lead SNP. To identify conditionally independent associations, we performed stepwise conditional association analyses for all markers within 200 kilobases of each lead SNP. At each step, we identified the most associated SNP not in LD with any other lead SNP (r 2 < 0.2) if this SNP had p < 1 × 10 −3 , we added it to the model and repeated the analysis until no independent SNP satisfied the p-value threshold. For each conditionally independent signal, we then calculated residual association statistics, where we condition on all other independent effects in a locus. All association signals with a lead SNP with an association p < 1 × 10 −5 were carried forward to subsequent analyses. These represent strong independent associations, with any residual weak effects removed (identified by the more lenient p < 1 × 10 −3 threshold). We deliberately chose lenient thresholds for inclusion to maximize our chances of identifying associations which may be shared across traits.

We next identified cis-eQTLs overlapping immune phenotype associations. We adopted the GTEx definition of a cis-eQTL being within 1 megabase of the transcription start site of the gene. We looked for conditionally independent immune phenotype associations within 200 kilobases of each lead SNP above we therefore identified all genes with a transcription start site (TSS) within 1 megabase of each lead SNP (R/BiomaRt v2.34.3, Ensembl build 37). We then looked for cis-eQTL associations for each such gene in T cells, monocytes and neutrophils, independently. For each identified eQTL (p < 1 × 10 −3 ), we then performed stepwise conditional association analyses as described above, for all SNPs within 1.2 megabases of the TSS (Supplementary Fig. 14). We chose this distance so any effects overlapping the lead SNP window are conditionally independent.

Due to the smaller sizes of the gene expression traits we limited iterations to a maximum of three independent signals per locus. As above, we then calculated residual association statistics for each independent eQTL effect in each of the three cell types. For each of the genetic loci associated with immune phenotypes we then selected all gene expression association statistics with a lead SNP with an association p < 1 × 10 −3 at the respective genetic locus.

Identifying shared associations between immune and gene expression traits

We tested for shared effects between immune and gene expression trait pairs with JLIM v2 12 . Given genotype-phenotype associations for two phenotypes in different cohorts in the same locus, JLIM assesses the likelihood of the joint model that variant i is causal in one trait and variant j in another trait, over some number of variants observed in two distinct cohorts. If this joint likelihood is maximal when i = j we can infer the presence of a single, shared effect driving both associations. Conversely, when the likelihood is maximal when ij we can infer that the observed associations are due to different underlying effects. JLIM assumes that only one causal variant for each of the tested traits is present in the analyzed window.

For each trait pair, we used the immune phenotype as the primary trait and the gene expression trait as secondary. We used the 404 non-Finnish European samples from the 1000 Genomes Project (phase 3, release 2013/05/02) as an external LD reference panel. We permuted the secondary trait for each pairwise comparison 100,000 times to obtain empirical significance levels, and used a false discovery rate (FDR) < 0.05 as a significance threshold.

We compared 16,652 unique combinations of immune phenotype and gene expression trait in one of three cell types across 1,199 genetic loci. These pairs encompassed all 164 immune traits and 7,060 genes with eQTLs in at least one of the three BLUEPRINT cell types. As we considered up to three conditionally independent associations per gene and locus, we made a total of 22,379 comparisons.

Calculating polygenic risk scores within trait pairs

We used polygenic risk scores (PRS) to assess the global genetic overlap between immune/gene expression trait pairs, beyond the shared associations identified by JLIM. For each pair, we selected independent variants associated with the expression trait at some threshold and then calculated PRS for all individuals in the immune trait cohort, using PRSice v2.2.11.b 54 with default parameters for clumping and an additive genetic model. A PRS (hat S_i) for the ith individual over m independent SNPs is defined as:

qayerda Xj is the number of minor alleles carried at the jth SNP, and (hat eta _j) is the eQTL effect size for the jth SNP 26 . To account for shared effects between some trait pairs but not others, we condition eQTL traits on the main variant from the association signal with the strongest JLIM p-value (even if not significant), and use the now conditionally independent eQTL data for the PRS calculation. We used ten different significance thresholds to select these SNPs: 1 × 10 −2 , 1 × 10 −3 , 1 × 10 −4 , 5 × 10 −5 , 1 × 10 −5 , 5 × 10 −6 , 1 × 10 −6 , 5 × 10 −7 , 1 × 10 −7 , and 5 × 10 −8 . We then calculated the proportion of immune phenotype variance (R 2 ) explained by these PRS and their empirical significance, also using PRSice.

We then compared PRS results between trait pairs with a shared effect and trait pairs with no sharing, using two approaches. We compared the proportion of immune phenotype variance explained (i.e., the R 2 values) with the Mann–Whitney–Wilcoxon test, and the correlation between JLIM p-values and PRS R 2 with univariate linear regression.

Mendelian randomization analyses

We used two Mendelian randomization (MR) approaches to assess evidence that gene expression traits are causal for the immune phenotype traits for which they share an association. First, we used two-sample Mendelian randomization (TSMR) 27,28 , as implemented in the TwoSampleMR v0.4.25R package, using inverse variance weighting of effect sizes. As instruments, we selected all independent SNPs associated with the gene expression trait with an association p-value < 1 × 10 −5 .

We also used transcriptome-wide summary statistics-based Mendelian Randomization (TWMR), an extension of TSMR 33 . As described by Porcu et al, we first identified all variants associated with the gene expression trait in each trait pair in each cell type (conditional association p < 1 × 10 −3 ). We then identified all genes within 1 megabase of the gene’s TSS, and selected all SNPs associated with the expression levels of any of these genes. We then removed genes with highly correlated expression values to the original gene (r 2 > 0.2), and selected pairwise-independent SNPs from the remaining list (pairwise LD r 2 < 0.1). We used the resulting set of variants as instruments in a multivariate MR model to estimate the causal effect on the immune phenotype. Ambiguous SNPs were removed for both MR analyses.

We compared causality estimates from both methods between trait pairs with a shared effect and trait pairs with no sharing. We compared estimated causal effect sizes with the Mann–Whitney–Wilcoxon test and, as a continuous measure, the correlation between JLIM p-values (strength of evidence of shared effect) with the estimated causal effect sizes and corresponding p-values using univariate linear regression.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.


Asboblar

* We are in the process of migrating our old softwares to github. Contact us if you need help.

RIMBANET (Reconstructing Integrative Molecular Bayesian Network): RIMBANet is a software package for reconstructing integrative molecular Bayesian networks. There are multiple sources of perturbations (eg. genetic mutations, copy number variations, methylations and etc.) that may contribute aberrant behaviors of biological systems such as cancer cells. Cells employ multiple levels of regulation that enable them to respond to genetic and environmental perturbations. At the transcriptional level, abundance of mRNA can be affected by the rate of transcription, a complex process regulated by transcription factors and enhancers, and by the rate of degradation of transcripts, a process regulated by RNA binding proteins and, in many organisms, microRNAs. Protein abundances are determined by protein degradation and protein synthesis rates, where protein synthesis can be regulated by translation initiation factors and microRNAs. Protein activity depends on a number of factors in addition to protein abundance, including protein localization, phosphorylation states and other post-translational modifications, and protein-protein interactions. In addition to transcript and protein levels, the abundance of small-molecule metabolites is also tuned in response to changes in a cell’s physiological state.

One of major goals of systems biology is to understand how these genetic and environment variations drive transcriptional networks, protein-protein interaction networks, metabolite networks and etc. to give arise to complex phenotypes. The integration of genetic variation and intermediate observations such as mRNA variations into probabilistic causal models that can dissect genetic pathways and provide mechanisms connecting DNA to clinical outcomes. We developed a computation framework centered around Bayesian network and implemented it in RIMBANet (BN4Distribution.tgz), which is freely available for download. We have previously used RIMBANet to discover causal relationships in complex human diseases such as diabetes and obesity and yeast model.

We applied RIMBANet to investigate how genetic variations regulate transcriptional and metabolite level changes in yeast. The full data set used in the study is available here (Yeast_4_Distribution.tgz).

For questions related to the RIMBANet package or the yeast data set, please contact Dr. Jun Zhu. Some compiled tips can be found helps.

MODMacher (Multi-Omics Data Matcher): Errors in sample annotation or labeling occur frequently in large-scale genetic or genomic studies and are difficult to be completely avoided in the process of data generation and management. Identifying and correcting these errors are critical for integrative genomic studies. Different types of genetic and genomic data are inter-connected by cis-regulations. Based on these cis-regulations among different types of data, we develop a computational approach, named Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in the multiple types of molecular data that can be subsequently used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Application to a large lung genomic study identified greatly increased statistically significant genetic associations and genomic correlations, a more than two-fold improvement. A simulation study shows that MODMatcher using three types of omics data is more robust than MODMatcher using two types of omics data. Details are described in Yoo et al (PLoS Comp. Biol, 2014).

ActMiR (Activity of miRNAs): MicroRNAs post-transcriptionally regulate a large number of mRNAs and play a key role in regulating cell growth, differentiation, and apoptosis. However, miRNA expression level is not equivalent to its functional activity (Mullokandov et al., 2012). We developed a computational approach to explicitly infer the activity of miRNAs based on the change in the expression levels of target genes. We showed in multiple cancer types (such as breast cancers, ovarian cancers, and GBM) that our estimated miRNA activities were consistently associated with clinical data in multiple independent data sets while the associations based on miRNA expression level itself couldn’t be replicated. The result is published in Lee et al. (Bioinformatics, 2015).

DDSClassifier (Deconvoluted Disease-Specific Classifier): Diagnostic and prognostic models based on peripheral blood gene expression have been reported for various types of disease. However, whole blood gene expression represents a mixture of hematopoietic cells, and is greatly influenced by the cell type frequency. Multiple common pathological and physiological changes result in similar blood cell type frequency change, which affects blood-based biomarkers’ specificity.To address these issues, we carried out a meta-analysis of 46 whole blood gene expression datasets covering a wide range of diseases or physiological conditions. Our analysis shows a striking overlap of signature genes shared by multiple diseases, which is driven by the underlying common patterns of cell component change. These observations suggest the necessity to develop disease-specific classifiers that can distinguish different disease types as well as normal controls. To build such models, we develop a new classification strategy that can take into consideration of both cell component changes and cell molecular stage changes. Particularly, we deconvoluted the original gene expression profile into a cell component profile and a residual expression profile for each sample, and built classifiers based on these deconvoluted features. Testing independent datasets, we show that the classifiers with cell component profiles and residual expression profiles incorporated performed significantly better than those without. Both the assembled datasets and the algorithms developed can be found in the R package. A detailed document can be found here. The result is published in Wang et al. (Scientific Reports, 2016)

DeClust: A reference-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles

HBVIntegrationPipeline : A robust data analysis pipeline was developed by modifying several key steps in VirusFinder2 for identifying HBV integration sites in both DNA and RNA sequencing data. A detailed description of the pipeline and proof-of-concept study results are published in Yoo S, Wang W, et al (BMC Medicine, 2017).

Multi-polynomial Temporal Genetic Association (MPTGA) and Temporal Genetic Causality Test (TGCT)
Methods to leverage both temporal and genetic information in association and causality test.

HBVIntegrationPipeline_single_cell: A specific pipeline for single cell HBV-HCC

proMODMatcher (probabibilistic Multi-omics data matching method): Data errors, including sample swapping and mis-labeling are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged based on the annotated labels. We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data, identify and correct data annotation and errors in large databases. The proMODMathcer can be used to check potential labeling errors in profiles where the number of cis-relationships is small, such as miRNA and RPPA profiles.


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Assembly of large biochemical networks can be achieved by confronting new cell-specific experimental data with an interaction subspace constrained by prior literature evidence. The SIGnaling Network Open Resource, SIGNOR (available on line at http://signor.uniroma2.it), was developed to support such a strategy by providing a scaffold of prior experimental evidence of causal relationships between biological entities. The core of SIGNOR is a collection of approximately 12 000 manually-annotated causal relationships between over 2800 human proteins participating in signal transduction. Other entities annotated in SIGNOR are complexes, chemicals, phenotypes and stimuli. The information captured in SIGNOR can be represented as a signed directed graph illustrating the activation/inactivation relationships between signalling entities. Each entry is associated to the post-translational modifications that cause the activation/inactivation of the target proteins. More than 4900 modified residues causing a change in protein concentration or activity have been curated and linked to the modifying enzymes (about 351 human kinases and 94 phosphatases). Additional modifications such as ubiquitinations, sumoylations, acetylations and their effect on the modified target proteins are also annotated. This wealth of structured information can support experimental approaches based on multi-parametric analysis of cell systems after physiological or pathological perturbations and to assemble large logic models.


Convergent mechanism of aging discovered

Several different causes of ageing have been discovered, but the question remains whether there are common underlying mechanisms that determine ageing and lifespan. Researchers from the Max Planck Institute for Biology of Ageing and the CECAD Cluster of Excellence in Ageing research at the University Cologne have now come across folate metabolism in their search for such basic mechanisms. Its regulation underlies many known ageing signalling pathways and leads to longevity. This may provide a new possibility to broadly improve human health during ageing.

In recent decades, several cellular signalling pathways have been discovered that regulate the lifespan of an organism and are thus of enormous importance for ageing research. When researchers altered these signalling pathways, this extended the lifespan of diverse organisms. However, the question arises whether these different signalling pathways converge on common metabolic pathways that are causal for longevity.

The search begins in the roundworm

The scientists started their search in the roundworm Caenorhabditis elegans, a well-known model organism for ageing research. "We studied the metabolic products of several, long-lived worm lines. Our analyses revealed that, among other things, we observed clear changes in the metabolites and enzymes of the folate cycle in all worm lines. Since folate metabolism plays a major role in human health, we wanted to further pursue its role in longevity," explains Andrea Annibal, lead author of the study.

A common mechanism for longevity

Folates are essential vitamins important for the synthesis of amino acids and nucleotides -- the building blocks of our proteins and DNA. "We tuned down the activity of specific enzymes of folate metabolism in the worms. Excitingly, the result was an increase in lifespan of up to 30 percent," says Annibal. "We also saw that in long-lived strains of mice, folate metabolism is similarly tuned down. Thus, the regulation of folate metabolism may underlie not only the various longevity signalling pathways in worms, but also in mammals."

"We are very excited by these findings because they reveal the regulation of folate metabolism as a common shared mechanism that affects several different pathways of longevity and is conserved in evolution," adds Adam Antebi, director at the Max Planck Institute for Biology of Ageing. "Thus, the precise manipulation of folate metabolism may provide a new possibility to broadly improve human health during ageing." In future experiments, the group aims to find out the mechanism by which the folate metabolism affects longevity.



Izohlar:

  1. Nerian

    Bravo, the perfect sentence just engraved

  2. Makasa

    Menimcha siz haqsiz. Men aminman. Menga PM da yozing, gaplashamiz.

  3. Ruark

    Bravo, bu fikr ataylab bo'lishi kerak



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