Journal of Biomedical Discovery and Collaboration, 2006; 1: 11-11 (más artículos en esta revista)

La aparición y difusión de la tecnología de microarrays de ADN

BioMed Central
Tim Lenoir (lenoir@duke.edu) [1], Eric Giannella (eric.giannella @ duke.edu) [1]
[1] Jenkins colaboratorio de Nuevas Tecnologías en la sociedad, la Universidad de Duke, John Hope Franklin Center, 2204 Erwin Road, Durham, Carolina del Norte 27708-0402, EE.UU.

Este es un artículo de acceso abierto distribuido bajo los términos de la licencia Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0], que permite el uso ilimitado, distribución y reproducción en cualquier medio, siempre que la obra original es debidamente citados.

Resumen

El modelo de red de innovación adoptado ampliamente entre los investigadores en la economía de la ciencia y la tecnología plantea relativamente porosas fronteras entre las empresas y los programas de investigación académica y una bi-direccional de flujo de las invenciones, personal y conocimiento tácito entre los sitios de la universidad y la innovación industrial. Por otra parte, el modelo sugiere que estos bi-direccional de los flujos deben ser considerados como estímulo recíproco de la investigación y la invención en la industria y la academia, funciona como un bucle de retroalimentación positiva. Una parte de este bi-direccional de flujo - es decir, el flujo de las invenciones en la industria a través de la concesión de licencias de universidad basado en tecnologías - ha sido bien estudiado, pero a la inversa fenómeno de la estimulación de la investigación universitaria a través de la absorción de las nuevas orientaciones que emanan de la industria Aún no se ha investigado mucho en los detalles. Se discute el papel de la financiación federal de investigaciones académicas en el campo de microarrays, y las múltiples vías a través de la cual el gobierno federal apoya el desarrollo comercial de tecnologías de microarrays han transformado núcleo académico campos de investigación.

Nuestro estudio confirma la imagen presentada por varios estudiosos que el carácter abierto de las economías en red es lo que hace verdaderamente innovador. En un sistema abierto innovaciones que emergen de la red. La aparición y difusión de las tecnologías de microarrays hemos trazado aquí un excelente ejemplo de un sistema abierto de innovación en acción. Ya sea que se originó en un entorno de arranque empresa que funcionaba como un grupo de reflexión, como Affymax, los laboratorios de investigación de una gran empresa, como Agilent, o dentro de una universidad de investigación, los inventores que hemos seguido ha inspirado en gran medida en el conocimiento de todos los recursos partes de la red en plataformas de microarrays con lo que a la luz.

La financiación federal de alta tecnología y las nuevas empresas de desarrollo industrial era importante en varias fases en la historia temprana de microarrays, y la financiación federal de los investigadores académicos utilizando microarrays es fundamental para la transformación de los programas de investigación de varios campos en el mundo académico. La típica historia sobre el papel de la financiación federal hace hincapié en las repercusiones de fondos federales la investigación académica a la industria. Nuestro estudio muestra que los efectos secundarios conocimiento trabajado en ambos sentidos, con la financiación federal de la no investigación de las universidades proporcionan el impulso necesario para la remodelación de investigación de varios campos académicos.

Fondo

Dado que la actuación de Kline y Rosenberg [1], von Hippel [2], Jaffe [3, 4], Trajtenberg [5], y otros, los economistas han abandonado el modelo lineal de innovación que la foto, un flujo directo de la innovación científica de líder descubrimiento para el desarrollo de productos, que terminó con la introducción en el mercado de nuevos productos. El modelo lineal ha sido sustituido por un modelo que destaca la función de vinculación, la retroalimentación y la co-evolución entre las distintas etapas del proceso de innovación de descubrimiento a través del desarrollo de la comercialización, las características y las interdependencias y el aprendizaje a través de las distintas etapas del proceso de innovación . Según esta imagen, la innovación es un proceso dinámico tomando como base los conocimientos científicos y técnicos, así como de experiencia en la fabricación, y las ideas de los servicios a las empresas que proporcionan financiación, comercialización, reglamentarias y comerciales de conocimientos.

A pesar del apoyo de la red modelo de innovación, ha habido pocos (si procede) el examen de las repercusiones de la industria basada en I + D o de la más amplia infraestructura tecnológica de una región en el entorno de investigación de las universidades. La mayoría de los exámenes de la función de los efectos externos sobre el medio ambiente universitario de investigación se han centrado en el impacto del Departamento de Defensa de financiación en la ciencia y la investigación en ingeniería durante la época de la guerra fría, o sobre las posibilidades (casi en su totalidad negativo) los efectos del patrocinio corporativo de programas de investigación académica en biomedicina. El modelo en red de innovación se ha descrito anteriormente, sin embargo, postula relativamente porosas fronteras entre las empresas y los programas de investigación académica como un elemento clave de una nueva región. El modelo sugiere un bi-direccional de flujo de entrada entre la universidad y la innovación industrial, en forma de licencias a las invenciones, personal y conocimiento tácito se derivan de (en su mayoría con fondos federales) los programas de investigación académica, así como una corriente de la industria a las universidades de las nuevas tecnologías y la investigación direcciones. Por otra parte, el modelo sugiere que estos bi-direccional de los flujos no deben considerarse como secuencial, es decir, originarios de la universidad el medio ambiente y difundir hacia el exterior para estimular las innovaciones comerciales que posteriormente remodelar el entorno de la investigación académica. Por el contrario, el modelo sugiere la posibilidad de estímulo recíproco de la investigación y la invención en la industria y la academia, funciona como un bucle de retroalimentación positiva.

El flujo de las invenciones en la industria a través de la concesión de licencias de universidad basado en las tecnologías ha sido bien estudiado, y nuestro papel va a contribuir a esa labor, pero a la inversa fenómeno de la estimulación de la investigación universitaria a través de la absorción de las nuevas orientaciones que emanan de la industria todavía no ha ser investigados en mayor detalle. Nuestro estudio aborda esta cuestión mediante el examen de las fuentes de apoyo, en particular el apoyo federal, y las múltiples rutas comerciales a través del cual las tecnologías de microarrays han transformado núcleo académico campos de investigación. El primer sistema de microarray, el Affymetrix GeneChip ® se originó más allá de los muros de la academia, pero dentro de una década que hizo importantes avances en la remodelación de los entornos de investigación programas universitarios, así como el lanzamiento de un espectro de empresas competitivas en varios sectores industriales en el Silicon Valley y otras regiones de alta tecnología. Los investigadores académicos que colaboran con Affymetrix los científicos se apresuraron a explorar la potencia de chips de genes. Se trata de mejorar y adaptar los chips de genes que se suministran por parte de las empresas como Affymetrix a sus preguntas de investigación. Por otra parte, varios investigadores académicos relacionados con la Iniciativa Genoma Humano proseguir activamente el desarrollo de otros tipos de microarrays de DNA, sobre todo manchado de tinta y de chorro de microarrays, como competidor a los sistemas de GeneChip ®. Si bien muchos de los universitarios basada en los sistemas de microarrays se reunieron en la casa-hogar como sistemas de cerveza, varias encontrado su camino en el desarrollo industrial. Desde mediados del decenio de 1990 la vida - a veces en disputa legal - la competencia entre estas plataformas se consideran esenciales para el desarrollo de una comprensión más sistémica de la genética se ha encargado de atraer a cientos de millones de dólares en biotecnología y compañías farmacéuticas. Tras solicitud inicial en la síntesis combinatoria de los materiales orgánicos, más espectacular aplicado que el original Affymetrix GeneChip ® en 1994, microarrays de conceptos sobre la base del original biochips se elaboraron materiales de combinatoria para la síntesis de materiales inorgánicos como así. A principios de 2000, el cielo parece ser el límite para todas las ramas de la tecnología de microarrays.

El amplio impacto significativo rápida y continua mejora de microarrays de hacer la tecnología adecuada "sonda" para el seguimiento de las diversas funciones de los diferentes tipos de instituciones en la difusión de una importante tecnología. Estas instituciones incluyen el gobierno federal, las universidades y sin fines de lucro, instituciones de investigación, empresas de, empresas establecidas, y los servicios a las empresas como bufetes de abogados y empresas de capital de riesgo. Queremos comprender la naturaleza de las interacciones institucionales en el caso de los chips de ADN y que las relaciones son especialmente cruciales para el avance de la tecnología como una importante plataforma de descubrimiento biomédico. Nos centraremos en la historia de microarrays de una variedad de ángulos: examinar el impulso para las organizaciones o grupos de investigadores a involucrarse con microarrays, los factores contextuales que permitieron su participación, y la forma en que aplican sus conocimientos y colaboró con otros para uso microarrays o sistemas relacionados con la construcción. Y finalmente, nos traza la forma en que estos innovadores de trabajo contribuyó a cambiar el paisaje general de la investigación.

Resultados y discusión
1. La invención de la GeneChip ®

Los microarrays de genes y chip surgió de los esfuerzos de un equipo de científicos interesados en la optimización de los métodos de descubrimiento de medicamentos. Este grupo fue montado por Alex Zaffaroni, el legendario director general de Syntex y más tarde fundador de varias empresas de biotecnología, incluyendo Alza y DNAX. En 1988 Zaffaroni se acercó Lubert Stryer, profesor de bioquímica en Stanford e inventor de numerosas fluorescencia de los métodos de etiquetado que permita la fluorescencia de células activado Clasificador (FACS) como una de las principales herramientas de investigación de células biológicas, para convertirse en el principal funcionario científico de la nueva empresa Zaffaroni, J. Lee Leighton, y Peter Schultz [Nota A] se llama Affymax fundación. El objetivo de Affymax era desarrollar nuevos enfoques de la química automatizada el descubrimiento de medicamentos. El enfoque tradicional en el descubrimiento de medicamentos se han de sintetizar o descubrir nuevos fármacos candidatos y, a continuación, prueba de sus actividades de una en una. Se trata de un tedioso, engorroso, y cada vez más costosa, por lo que la aceleración o la automatización de este proceso fue de gran interés para las compañías farmacéuticas.

En la construcción de la empresa, Zaffaroni, Schultz, y Lee no tienen una tecnología específica que la intención de seguir estudiando. Sin embargo, la sensación de que los acontecimientos recientes en materia de biotecnología estaban a punto de hacer que el problema de descubrimiento de medicamentos tractable, que reunió a un star-studded consejo consultivo científico de Stanford y otras universidades [Nota B]. Desde el principio el enfoque preconizado por Avram Goldstein del Departamento de Farmacología de Stanford parecía más atractiva. Goldstein instó a la búsqueda de la síntesis de péptidos como medio de generación de diversidad química para identificar lleva prometedor para moléculas de drogas. Goldstein argumentó que, dado que los receptores de cualquier ligando puede formarse a partir de secuencias cortas de péptidos en la combinación de sitios de anticuerpos, debe ser cierto que por el corto de péptidos, se podría hacer un ligando para cualquier receptor [6]. Affymax podría perseguir la generación de grandes bibliotecas de los pequeños péptidos con nuevas secuencias de proteínas contra diversos objetivos, análogo a la forma en que el sistema inmunológico actúa de cribado poblacional de su repertorio de anticuerpos, la identificación de los que funcionan mejor y hacer más de ellos.

Existen varios métodos para la generación de grandes bibliotecas de péptidos a través de lo que se llama "química combinatoria" estaban llegando a la escena a mediados del decenio de 1980. El campo tiene en realidad su inicio en 1963, cuando R. Bruce Merrifield (Premio Nobel de Química, 1984) introdujo el concepto de fase sólida síntesis de péptidos (SPPS) por el cual las cadenas polipeptídicas tan corto como dos aminoácidos (dipeptides), así como a más largo ( proteína) las cadenas podría hacerse en asamblea línea de moda el uso automatizado de péptido sintetizadores. En la década de 1980, Australia Mario Geysen investigador de la Universidad de Melbourne (más tarde en Glaxo Wellcome) mostró que SPPS podría ser la base de múltiples péptido de síntesis. Geysen "péptido en un alfiler" método genera una serie de breves fragmentos de proteínas mediante la combinación de varios aminoácidos (los bloques de construcción de péptidos y proteínas) en diferentes permutaciones [7, 8]. Cada péptido se hizo sobre el final de un alfiler en forma de polietileno de apoyo, sumergidas en un plato con un nuevo aminoácidos de cada paso de la reacción. Revestimiento de las clavijas en un arreglo con la (originalmente 96) pocillos de una placa de microtitulación docenas (incluso cientos) de reacciones podrían ser realizadas al mismo tiempo. Geysen del método fue el primer ejemplo de una biblioteca de compuestos sintetizados molecular donde la identidad puede ser conocido por basarse en la posición física del compuesto en la biblioteca [Nota C].

Otros candidatos para la generación de técnicas de combinatoria bibliotecas de péptidos estaban llegando a la escena casi al mismo tiempo el Affymax bordo estaba desarrollando su enfoque [Nota D]. Pero en lugar de cualquiera de estas opciones en la elaboración de síntesis combinatoria, Lee y Pirrung llegó con un brillante nuevo enfoque de su propia la que llamaron VLSIPS, por muy grande Very Large Scale Escala Inmovilizadas Inmovilizadas polímero de síntesis de polímeros de síntesis. En una de las reuniones de la junta científica Affymax, Leighton Read echado a cabo la idea de que sólo imitan los fabricantes de chips semiconductores, el uso que haces de luz para manipular las moléculas en superficies sólidas con el fin de crear azar diversidad química. Aunque él había pasado su carrera trabajando con la luz fluorescente y la activación de etiquetado, Stryer no había pensado en esta posibilidad. Leer Pirrung y se puso a trabajar en la idea y escribió un acta sobre la invención VLSIPS, modelando el nombre en la VLSI (integración a muy gran escala) la tecnología que conducía la industria de semiconductores en el momento [Nota E]. Leer y Pirrung define los conceptos y los principales parámetros de la luz dirigido por síntesis en los próximos días, que se detalla en una solicitud de patente presentada el 7 de junio de 1989.

El siguiente paso para el grupo estaba a punto de comenzar el trabajo sobre la aplicación de la idea de generar diversidad química en una matriz diseñada por un proceso de fotolitográfica. Pirrung estaba a punto de salir a la Universidad de Duke para asumir una nueva cátedra de bioquímica, por lo que comenzó a investigar Stryer entre colegas locales para el nombre de un joven bioquímico que podría ser apropiada para dirigir el proyecto de producir un prototipo y la reducción de la invención la práctica. Stryer's durante mucho tiempo colaborador de Berkeley Alexander Glazer propuso Stephen Fodor, un joven de Princeton Ph.D. NIH con una beca postdoctoral de trabajo a tiempo resolver-espectroscopia de bacterias y plantas pigmentos en su laboratorio. Glazer recomienda Fodor como un bioquímico de excepcional capacidad y, de hecho, él ya tenía la reputación de un visionario. A pesar de tomar una posición en la industria no era de interés para Fodor, la oportunidad de una lluvia de ideas con Zaffaroni, Stryer, Berg, Schultz, Lederberg y Davis fue una oportunidad que no se querrá perder. El nombramiento académico podría seguir. En julio de 1989 Fodor se unió al grupo en las oficinas de Affymax en Porter Drive en el Parque Industrial de Stanford.

En los próximos 18 meses Fodor trabajado intensamente con Stryer tanto en lo que describen como la más estimulante y productivo período de científicos invención imaginable. La invención que, junto con sus colegas científicos a Affymax, en última instancia producido - luz-dirigida espacialmente direccionable síntesis química - fue literalmente el matrimonio de la bioquímica y la fotolitografía técnicas utilizadas en el diseño de chips en los locales de industria de semiconductores. Lo que demostró en 1991 ahora en un clásico artículo publicado en Science es un proceso para depositar en un substrato de vidrio - literalmente un portaobjetos de microscopio cubrir en la primera versión de la invención - aminoácidos grupos - NH - que fueron bloqueados por un photolabile protección química grupo - X (Figura 1] [9]. Iluminación con un láser a través de una máscara llevado a photodeprotection, lo que permite, en el próximo paso a través de productos químicos de acoplamiento, la adición de un primer bloque de química un edificio que contenía un grupo de protección de photolabile X. En el siguiente paso, otra máscara se utiliza para un photoactivate región diferente del sustrato. Un segundo grupo B la etiqueta se adjunta a los grupos amino expuestos por la iluminación a través de la máscara. Este proceso se repite tantas veces como desee para obtener el deseado conjunto de productos. El lavado de un baño de cadenas de péptidos con un marcador fluorescente adjunta al final, es posible determinar la composición de aminoácidos que forman la cadena con la ayuda de un fotomultiplicador / escáner que funcionan de manera similar a la FACS (activado de fluorescencia de células Clasificador). La primera consistió microarray péptidos de 1024 en un 1,6 cm 2 área generados en diez etapas. Este fue el primer microarray diseñado específicamente para la síntesis de péptidos, y al mismo tiempo Fodor desarrollado un escáner para leer la salida.

Parte de la belleza de combinar la fotolitografía con la química combinatoria es la resultante de alta densidad de los compuestos en el sustrato. Teóricamente, la única limitación física sobre la densidad es el grado en que los compuestos se puede activar - en otras palabras, la difracción de la luz. Este prevé un increíblemente alto grado de miniaturización, y en 1991, en el momento de la publicación del documento, Fodor y sus colegas a Affymax escribió:

Nuestra actual capacidad de alto contraste photodeprotection es mejor que el 20 μ m, lo que da> 250000 síntesis sitios por centímetro cuadrado. No hay ninguna razón por la física, mayores densidades de sitios de síntesis no puede lograrse [10].

El uso de la fotolitografía esencialmente a la Ley de Moore a Affymax y a la nueva sociedad Fodor puesto en marcha en torno a él, Affymetrix. Al igual que la industria de semiconductores, Affymetrix ha aumentado la densidad de los sitios de síntesis, al tiempo que los chips más complejos y más difíciles de fabricación [11]. De hecho, cinco años después, Affymetrix ha elaborado un prototipo de chip con un millón de sondas [12].

Si bien se continuó trabajando en péptido microarrays, Stryer y el Affymax científico reconoció una pensión mucho más inmediata oportunidad en el desarrollo de microarrays de ácido nucleico. Fase sólida síntesis de ADN es considerada la más eficaz y fiable método de síntesis química conocida. Dada la base estricta de las normas de emparejamiento (Watson-Crick emparejamiento) obedecidas por los cuatro componentes básicos del ADN (adenina, citosina, guanina y citosina, o A, C, G, y T), una sección de un solo hundidos de ADN, que podría contener numerosos genes y, por tanto, ser utilizado como una sonda, se corresponden sólo con su línea complementaria de ADN [cDNA para "ADN complementario"] para formar la doble hélice. RNA, que es el ADN del primo químico, también sigue una estricta base-apareamiento regla cuando se unen al DNA, por lo que la secuencia de cualquier filamento del RNA que con pares de ADN en un microarray puede inferirse también.

Combinatoria análisis basado en la luz-dirigidas síntesis de ADN en un chip que ofrece excelentes oportunidades, y había una serie de razones por las cuales Fodor quería proseguir los chips de ADN con más energía que péptido arrays. Simplemente en términos de consideraciones de carácter práctico de la construcción, un péptido gama de sólo dos unidades de aminoácidos de cada secuencia con los 20 aminoácidos como bloques de construcción produce una gama de 400 secuencias en 40 medidas del tipo descrito anteriormente. Por el contrario, en el mismo número de pasos (40), una serie de ADN de 10 unidades (ATTGC. ..) secuencias sintetizadas de cada uno de los cuatro ácidos nucleicos como bloques de construcción se puede construir una matriz que contiene de un millón de secuencias. Por otra parte, el ADN es ideal para la luz dirigido por síntesis, y bien establecidas las técnicas existen para el anclaje de ADN a una placa de cristal. Después de haber demostrado que la luz dirigida síntesis de péptidos fotolitográfica utilizando la tecnología de ocultación era posible, y sabiendo que todas las piezas para hacer un paralelo de síntesis de ADN fueron al alcance de la mano, Fodor está deseoso de cambiar totalmente su atención al desarrollo del chip de genes. En un fax de 15 de mayo de 1990 a Stryer, Fodor se indica las razones de sus convicciones que ha llegado el momento de dedicar plena concentración en el gen chip. El resultado de esto fue a las spin off gen chip proyecto como su propia empresa, Affymetrix (por afinidad Matrix).

2. Sobre la base del Silicon Valley Network

Fodor's prototipo de la luz-dirigidas paralelo péptido síntesis gama, el escáner de fluorescencia y sistema informático para realizar un seguimiento de cada mancha en el chip y cuantificar la relación entre la etiqueta de partidos como ADN color ratios in situ en una computadora gráfica de salida, el photomasks y adecuado photochemicals para la construcción de las matrices fueron diseñados por la embriagadora "think tank" de Affymax medio ambiente. Los debates en el Affymax científica reuniones de la junta permitió Fodor y sus colegas de aprovechar el conocimiento y la visión de algunos de los principales académicos bioquímicos y químicos del día de varias universidades, entre ellas Stanford, Berkeley, Cal Tech, y Lawrence Livermore Labs. Este apoyo º y flujo de información entre los investigadores académicos y los esfuerzos para poner en marcha la empresa había mucho el estilo y el espíritu de una startup de Silicon Valley.

El carácter de red distribuida de la innovación en el Silicon Valley es un ejemplo de Fodor's original sistema prototipo para la utilización de la fotolitografía para diseñar un péptido de microarrays. Como una manera de construir diversidad química, Stryer sugerido por la que se establecen una serie de franjas paralelas - cada banda con un compuesto diferente - un compuesto a la vez, y luego repetir el procedimiento con franjas establecidas perpendicularmente a la red, uno a la vez. Con el fin de ver la forma de trabajo a cabo utilizando la fotolitografía, Fodor en contacto con Fabián Pease, profesor de ingeniería eléctrica en Stanford, especializada en haz de electrones de fabricación de máscara litográfica. Pease, un doctorado de la Universidad de Cambridge, había sido un ayudante de profesor en UC Berkeley brevemente antes de pasar, en 1967, a Bell Labs, donde trabajó primero sobre la televisión digital y, posteriormente, dirigió un grupo que se desarrollaron los procesos de haz de electrones máscara litográfica fabricación y demostró un pionero LSI circuito construido con haz de electrones-litografía. Pease ha estado en Stanford desde 1978. Fodor y Stryer Pease persuadió a unirse a Affymax como consultor en su proyecto, y tanto él como Fodor dedicado mucho tiempo a discutir los aspectos técnicos de la litografía necesarios para construir los microarrays. Pease tomó alrededor de Fodor a varios almacenes en Silicon Valley para adquirir la antigua litografía instrumentos necesarios para la construcción del prototipo péptido matriz. En mayo de 1990 con aportaciones periódicas de Pease, Fodor había un grupo de trabajo semi-automatizado litografía instrumento que haría binario péptido síntesis combinatoria. Pease mantuvo su conexión con Fodor después de la puesta en marcha de Affymetrix en 1992. En 1993-94, por ejemplo, él tomó un año sabático de Stanford para trabajar en los microarrays de ADN. Pease ha sido co-inventor junto con Fodor y Stryer en varias patentes clave Affymetrix, y ha seguido manteniendo una relación de consultoría con Affymetrix [Nota F].

Una historia similar de Silicon Valley en red condujo al diseño de la primera microarray scanner y lector. A través de la red de contactos de la junta científica Affymax, Fodor se puso en contacto con Peter Fiekowsky para que le ayuden en el desarrollo de un sistema para la detección y la imagen fluorescente marcada marcadores de secuencias de polímero en el péptido matriz. Fiekowsky había recibido su Licenciatura en Física del MIT en 1977. Tras graduarse, se trasladó a Silicon Valley para trabajar en la NASA y se trasladó a Fairchild de la inteligencia artificial de laboratorio en 1983, donde trabajó en el análisis de imágenes en la industria de semiconductores. Un año más tarde, en 1984, fundó Fiekowsky Automatizado de Inspección Visual. El trabajo que hizo y Fodor sobre el proyecto conjunto llevado a dos de las 23 patentes Fiekowsky celebra en procesamiento de imágenes que van desde técnicas de semiconductores y de panel plano a la inspección médica de rayos X y los chips de genes [13, 14].

James L. Winkler 's con la participación de Fodor y las tecnologías básicas en la puesta en marcha de Affymetrix ofrece otro ejemplo típico de la amplia gama de talentos y la verdadera reserva genética de los innovadores que circulan a través de empresas de Silicon Valley. Fodor Como recordó en una entrevista, "Winkler es uno de esos chicos que se acaba de brillante, no tenía ninguna educación formal, sino que puede construir nada. Él podría tomar una placa de circuito en blanco y al final del día tienen algo que pudiera conectar en la parte trasera del ordenador para ejecutar una pieza externa del equipo. "[Entrevista con Stephen Fodor, de agosto de 2004] Winkler Una de las primeras contribuciones fue el diseño y la aplicación del método y los dispositivos de reactivos que fluye a través de los canales de bloque en el substrato de vidrio de microarrays para formar las rayas de diferentes péptidos en combinación con la luz dirigido por el método de acoplamiento y desacoplamiento. Después de cada banda fue establecido, el sustrato fue trasladado por una rotación de fase, y repite el proceso para formar matrices de polímeros en el sustrato [14]. Este fue sólo el primero de lo que se convertiría en 31 patentes en diferentes aspectos de la producción de chips de genes y fotolitográfica máscara de diseño, incluyendo un conjunto de herramientas informáticas para seleccionar el diseño de sondas y el diseño de una serie de ADN u otros polímeros y la utilización de archivos de diseño de chips para diseño y / o generar máscaras litográficas [16].

La orientación que Affymetrix recibido en su incipiente años a partir de mecanismos de consulta con académicos locales y otros expertos de Silicon Valley ha sido crucial para el avance de genes chips y sistemas relacionados. La investigación en varios dominios que había estado ocurriendo durante años en la universidad y el gobierno los proyectos de investigación que siempre fértiles fuentes de ideas y técnicas para el desarrollo de la compleja tecnología de los microarrays de ADN. De hecho, los científicos universitarios aparecieron varias decenas de veces en las patentes concedidas Affymetrix, aunque algunos de estos se explica por profesores universitarios que habían sido contratados en la empresa [Nota G]. En el cuadro 1 se presentan los resultados de nuestra exploración de los datos de patentes para la colaboración académica con Affymetrix [Nota H].

Creemos que estos universitarios colaboradores siempre conocimientos que permitan a Affymetrix, sin estas consultas en curso el desarrollo de los microarrays se han tomado mucho más tiempo. La financiación federal ha sido particularmente importante en el desarrollo de microarrays. Por un lado, como hemos visto, la financiación federal para la extra-universitaria basada en la investigación industrial y desarrollo siempre que el capital para poner en marcha el grupo de tecnologías innovadoras relacionadas directamente con el GeneChip ® de Affymetrix, y como vamos a mostrar a nuestros estudios de caso más adelante, la financiación federal es fundamental para el despegue de algún competidor tecnologías en el campo de microarrays. Pero el trabajo a Affymetrix y otras empresas en el ámbito de microarrays se depende en gran medida de los conocimientos y la experiencia que había acumulado en varias disciplinas académicas, incluyendo bioquímica, genética, ingeniería eléctrica y ciencias de la computación como resultado de, al menos, dos décadas de fondos federales de la NSF, NIH, DOE, y programas como la Iniciativa Genoma Humano, especialmente en el Área de la Bahía universidades, Stanford, UC Berkeley, y UCSF. En términos de la infraestructura de la innovación se ha mencionado anteriormente en nuestra introducción, "derrames" conocimiento "de estos el gobierno federal apoya programas de investigación académica proporcionado importantes recursos para el campo emergente de microarrays. La financiación federal de extra-universitarios de investigación y desarrollo de la industria siempre que el estímulo para la preparación de esos recursos en un conjunto de la acumulación de innovaciones que dieron lugar a una nueva e importante tecnología y varias nuevas líneas de investigación. El apoyo del Gobierno, especialmente cerca a las universidades, bajar los costos de desarrollo mediante el cultivo de expertos que desempeñan un papel fundamental en la creación de la GeneChip ®.

3. La financiación federal de la investigación y el desarrollo

El desarrollo de la química combinatoria, microarrays, y el GeneChip ® a Affymax y Affymetrix y otros Área de la Bahía de las empresas llama la atención sobre una importante pero a menudo pasan por alto la función de desarrollo de alta tecnología regiones, a saber: el papel de los fondos federales para la investigación y el desarrollo en las empresas que transforma el entorno de la investigación académica, mientras que el lanzamiento de nuevos sectores industriales. La mayoría de los debates de la financiación federal para la investigación se concentran en el papel de la financiación federal de conducción en la investigación académica. Pero a medida que nuestro análisis del aumento de Affymetrix demuestra, fondos federales también ha sido crucial para estimular el otro lado de la ecuación en la simbiosis de Silicon Valley y la investigación como las universidades de Stanford, a saber, en la formación de la puesta en marcha empresas y colaboraciones con las grandes empresas establecidas en el desarrollo de nuevas tecnologías innovadoras. Con frecuencia el punto masivamente a la función central del gobierno federal en la financiación de la investigación académica, pero también es cierto que en Silicon Valley, el gobierno ha desempeñado y sigue desempeñando una importante y absolutamente vital papel en la financiación de nuevos desarrollo industrial. Este punto se ha hecho con frecuencia sobre el papel de defensa de contratación en apoyo de principios de la evolución de la electrónica y las industrias de semiconductores durante los años 1950-'70. Pero la financiación federal también ha sido un factor importante en el desarrollo de la biotecnología, la ciencia de los materiales, y varias de las industrias relacionadas con el decenio de 1990 hasta la actualidad.

Cuadro 2 y Gráfico 2 ilustran la importante contribución de fondos federales de ambas universidades y la industria de I + D en California para el período comprendido de 1990 a 2002.

Durante el decenio de 1990 a través de comienzos del decenio de 2000 California clasificado número uno entre los estados que reciben fondos federales para la investigación. Durante este período el promedio anual federal de California obligación de I + D en la industria fue de aproximadamente $ 6,96 millones, mientras que el apoyo de la universidad actividades de I + D un promedio de aproximadamente $ 3,3 mil millones. Aunque la tendencia apunta a una disminución de cantidad de gasto federal para la industria de I + D en los últimos años del período con un aumento alentador para las universidades, el hecho es que el apoyo federal de California en I + D casi se triplicó el apoyo a investigadores en universidades. Por supuesto, una parte considerable, por lo general superior al 50% del total federal de I + D en California se dirige hacia las industrias de defensa. Pero incluso teniendo en el gasto de defensa y no teniendo en cuenta que algunos de la investigación biotecnológica es financiado por el DOD, la cantidad de los no relacionados con la defensa de fondos federales a la industria en California supera apoyo federal para la investigación académica por un margen considerable - normalmente por un factor de dos de especial importancia para las empresas como Affymetrix, Symyx y otras empresas de microarrays en el campo vamos a discutir a continuación es el número de Pequeñas y Medianas Empresas de Innovación Programa de Investigación (SBIR) premios y Pequeñas y Medianas Empresas Programa de Transferencia de Tecnología (STTR), premios de ir a California [Nota I ]. A lo largo de este período de California tiene un promedio de alrededor de 1000 SBIR y STTR premios, ocupe el primer lugar en ambas categorías de premios, con la clasificación general, Massachusetts segundo. Por otra parte, durante este período de California ha recibido 184 premios del Programa de Tecnologías de Avanzada, un programa que patrocina el arranque empresas con una universidad basada en la colaboración académica o PI. Desde la perspectiva de Silicon Valley, el decenio de 1990 fueron los mejores momentos. If we compare all SBIR and STTR awards received by firms in the Bay Area zip codes that constitute Silicon Valley versus all California awards, the Bay Area has averaged 33 percent (an average of $62 million per year) of the awards with a high of 39 percent in 1993 and a low of 25 percent in 2002. Several Bay Area companies, such as Affymetrix, have been the recipients of multiple SBIR/STTR/ATP awards. Figure 3 provides an overview of SBIR and STTR awards specifically to the Bay Area.

When we consider that 58 percent of the federal funding for R&D to universities in California has gone toward funding innovation in the life sciences (see Table 2 ), the importance of the NIH and the Human Genome Project for the explosion of biotech firms in Silicon Valley becomes evident. Federal funding has also been significant in sustaining an entrepreneurial academic environment at Stanford and other Bay Area universities that have participated in numerous waves of technological innovation within the Silicon Valley through the students they train and the faculty engaged in research and consulting as well as in working with their university technology licensing offices to disclose, patent, and license inventions. As we have shown in another study, Stanford's openness to (in former Stanford Dean of Engineering, Jim Gibbons' phrase) "reverse engineering," the enhancement of new research directions through absorption of technological directions emerging in the Silicon Valley as key to its entrepreneurial culture, is one of the pillars of its success. Stanford receives approximately $500 million in federally funded research grants annually. Berkeley and UCSF are also in the top 20 research universities receiving federal support. As we now see, federal funding is also deeply involved in stimulating and sustaining the reverse engineering essential to this co-evolution of Bay Area research universities and the Silicon Valley.

Affymetrix was well positioned to take advantage of the flows of information from both the academic and biotech communities within Silicon Valley to acquire funding and intellectual resources necessary for assembling the pool of ideas, inventions, and know-how behind the microarray. Local university help and federal funding were essential to Affymetrix's push to begin developing the GeneChip ® in 1990. Encouraged by Stryer and increasingly confident about the success of the GeneChip ® , Fodor sought to capitalize on the wave of funding for technologies associated with the NIH's goal to uncover and exploit genetic information. The Human Genome Project had been launched a few months earlier and the NIH was soliciting proposals for the development of technology in support of genomics. Ron Davis from the Stanford Biochemistry Department, together with David Botstein from Genetics, were developing technology for the human genome sequencing effort at that time. Stryer invited Davis over to Affymax to discuss the use of the gene chip technology to perform genetic sequencing by hybridization. Paul Berg, who was on the Affymax scientific board, was also interested in the technology, and he attended the meeting. Both Davis and Berg immediately saw the potential of the technology, and Davis was excited enough about what he saw to propose a collaboration with Fodor to apply for NIH funding to support the development of the gene chip. The NIH panel for sequencing technology for the Human Genome Project directed by Leroy Hood was meeting across the bay in Walnut Creek, CA in the spring of 1991, and Paul Berg arranged for Fodor to be invited to present on the peptide and DNA chip project. James Watson and a blue ribbon panel of genome scientists were in attendance, and when the meeting concluded, Fodor and Davis were encouraged to apply for funding. In September 1992 the first of several grants to Affymetrix was awarded with Stephen Fodor as PI. Co-PIs on the project were Ron Davis from Stanford and Ronald Lipschutz from Daniel H. Wagner Associates, a mathematics firm that contributed expertise on improving algorithms for sequence analysis [Note J]. The initial NIH grant, funded from 1992–95, was for $2.5 million, and together with a Phase I Small Business Innovation Research (SBIR) grant from the Department of Energy (one of several SBIR grants the company has received) for $500000 awarded in 1992, Fodor was able to demonstrate proof of the concept of using large arrays of DNA probes in genetic analysis. A Phase II grant was awarded to assist Affymetrix in moving the technology towards commercialization. Scientists at Affymetrix also received several grants from the National Institutes of Health. For example, Fodor was principal investigator on a second round of NIH funding in 1995 for a three-year $5.5 million NIH, grant from 1995–97. One component of this grant addressed the development of chip-based sequencing, re-sequencing, sequence checking and physical, genetic, and functional mapping. A technology development component addressed the production of chips and the development of instrumentation and software specific to the chip applications.

Affymetrix's largest government award in the startup phase of the company came from the Advanced Technology Program (ATP) of the National Institute of Standards and Technology (NIST) in the Tools for DNA Diagnostics Focused Program competition in 1994. In its reports documenting the successes of its programs, the ATP lists Affymetrix as one of its banner projects [ 17 ]. The ATP program was started in 1990 to stimulate new science-based research ventures and to encourage joint ventures among universities, industry, research organizations, and consortia of companies. A consortium established by Affymetrix was awarded a $31.5 million, five-year grant in 1994 to develop miniaturized DNA diagnostic systems. Under this grant, Affymetrix directly received $21.5 million, some of which was used to fund activities at a number of collaborating institutions as subcontractors to the project. As part of this grant, Affymetrix and its partner Molecular Dynamics collaborated with researchers at the California Institute of Technology, Lawrence Livermore National Laboratory, Stanford University, the University of California at Berkeley, and the University of Washington to develop the next generation of diagnostic devices to capitalize on the advances of the Human Genome Project. After developing its core chemical synthesis technology while still funded under the ATP and SBIR grants, Affymetrix entered into agreements with OncorMed to collaborate in development of clinical validation of genetic testing services utilizing the GeneChip ® for analysis of genes associated with cancer; and under a separate distribution and instrumentation alliance between Affymetrix and Hewlett-Packard, Hewlett-Packard began developing and supplying a next-generation scanner to read the GeneChip ® in 1996. The Advanced Technology Program was particularly enthusiastic about the ways in which Affymetrix accelerated the diffusion of its technology through alliances and collaborations with the Genetics Institute, Roche Molecular Systems, Incyte Pharmaceuticals, and Glaxo Wellcome in order to continue raising capital for expanding its own internal R&D [ 18 ]. Table 3 tracks federal funding that Affymetrix received over a ten year period [Note K].

Two themes emerge from the way the government funded Affymetrix: the wide range of government organizations that provided the funding, and the variety of federally funded research projects at Affymetrix. The diversity of agencies that saw benefits to the GeneChip ® is quite apparent: the Department of Energy, NASA, and several organizations within the National Institutes of Health funded Affymetrix over the eleven-year period studied. Later affirmed by the breadth of research applications the DNA chips found, this broad set of government health organizations, such as the National Cancer Institute, the National Institute of Allergy and Infectious Diseases, and the National Institutes of Neurological Disorders and Stroke, provided early testimony to the GeneChip ® 's widespread applicability.

These kinds of collaborative research efforts were a prerequisite to acquiring federal funding to launch the company, and they have continued ever since to be a deliberate core strategy of Affymetrix, carried over from Affymax, to maintain simultaneously within the firm an entrepreneurial as well as an academic environment. The firm's goal was to attract preeminent researchers and convince them that the company was creating cutting-edge technology. Steve Fodor was persuaded to leave his postdoctoral research position at UC Berkeley – despite his initial lack of interest in leaving academia – by the possibility of continuing to work with some the field's brightest academics as well as having in-house funding to do research. The freedom to seek outside grants to pursue research peripheral to the company's core strategies was also considered an important tool in attracting high-quality people to the project. Affymetrix has been able to attract staff who continue to keep their academic contacts through participation in grant proposals, and who have the freedom to pursue ideas to which they have dedicated their careers, while gradually migrating to a commercial environment where more tangible products can be generated . The exercise of building a consortium of other companies to work together under the ATP project, for example, fed a very collegial environment where researchers worked hard with the best people in their field around the world, pushing these technologies to a stage at which they could be commercialized successfully.

4. The microarray revolution: diffusion of the GeneChip ® and microarrays

The 1991 paper in Science on parallel chemical synthesis using microarrays inaugurated the field of combinatorial chemistry, and it may indeed be one of the key events in the genomics revolution. By 1999 articles in Science among many other scientific journals were celebrating the widespread use of microarrays and the way they had transformed genomics [ 19 ]. People who never thought they would do large-scale gene studies suddenly were eager to try their hand at monitoring thousands of genes at once. The National Institutes of Health (NIH) heavily supported this trend, funding its own microarray studies and providing grants to institutions to buy the technology. This generous support of studies using microarrays generated a flood of data that traditional journals found hard to accommodate and digital databases didn't yet know how to handle. The NIH funded workshops to spread the technology. A Cold Spring Harbor Laboratory workshop on microarrays led by Pat Brown from Stanford in 1999, for instance, was the most over subscribed laboratory course on record in the history of Cold Spring Harbor programs. The new course was not even advertised, yet eight times as many people signed up as could be accepted. Sixteen people paid $1955 each to learn how to build and use a machine for genetics research. For another $30000, four actually took the machine home.

Research and development of microarrays was the hot new field in the 1990s. Although their approach was distinctive in focusing on in situ synthesis of DNA libraries on a chip, Affymetrix was not alone in the microarray field. About the same time the Affymetrix group was developing the GeneChip ® , several academic teams were developing alternative microarray systems [Note L] [ 20 ]. Of particular importance were spotted microarrays developed at Stanford by Pat Brown, Dari Shalon, Stephen J. Smith, Mark Schena and Ron Davis. The Stanford system was a contact array that used two-color fluorescence hybridization. On the heels of this system was a non-contact array developed by Leroy Hood at Cal Tech that adapted the technology for ink-jet printers to micro spot solutions of nucleotide reagents printed on a glass substrate [ 21 - 23 ].

The spotted microarrays were extensions of methods that had been in use in genome analysis and molecular biology for two decades, going back to Edwin Southern's introduction of the Southern Blot [ 24 ]. Another forerunner for all the microarray work, including the work of Fodor et al., were the methods for locating the position of specific sequences in chromosomes through fluorescence in situ hybridization (FISH), which allowed cell nuclei and chromosomes to be fixed to glass microscope slides as solid support. Ron Davis had contributed to those early methods for identifying genes, and the same technique was used to fix DNA to slides as solid support for his later microarray work [ 25 ]. The technique of using ordered arrays of DNA at the core of microarray techniques also grew out of earlier work. Of special importance was the dot-blot method introduced in 1979 by Fotis Kafatos, et al., in which hybridizations were carried out in parallel and fluorescent signals representing hybridization were measured with an imaging method [ 26 ]. The procedures for constructing these arrays were manual and the spots, as in the Southern Blot method, were deposited on various types of porous filters. While effective, these early spotting methods on porous materials were not suitable for the large-scale genome analyses that took off in the 1990s: it was not possible, for instance, to reduce the size of the spots beyond certain limits, or to control their size and shape on a porous membrane. The large scale automation of these dot-blot procedures was undertaken by Hans Lehrach and his co-workers at the Berlin Max-Planck-Institute for Molecular Biology in 1994. Lehrach's group developed laboratory robotic systems for picking and spotting clones onto filters [ 27 ]. This move toward large-scale automation with robots coupled with the replacement of the porous materials used in dot-blots with impermeable supports, such as glass or silicon, were key steps in the development of the spotted microarray systems. Non-porous surfaces permitted the use of very small sample volumes and high sample concentrations of spots. Over the next few years during the early 1990s technical advances made it possible to generate arrays with very high densities of DNA spots, allowing for tens of thousands of genes to be represented in areas smaller than standard glass microscope slides. These changes to the macroscopic format of filter based arrays resulted in the miniaturized "biochip" format of the microarray that has brought about a fundamental revolution in biological analysis. By effectively making it possible to represent the entire genome of an organism on a single biochip, researchers are able to study the expression of all the genes of a particular organism at once.

The spotted microarray developed in Pat Brown's lab consisted of two principle pieces of hardware; the arrayer and scanner [ 28 ]. The arrayer was a variation of the standard "pick-and-place" XYZ-axis gantry robot common to many large university molecular biology laboratories. Glass slides coated with a poly-lysine surface were placed on a platter. The robot picks up pre-synthesized single strand or double stranded DNA samples from a 384 well microtitre plate by placing a specially designed cluster of spring-loaded printing tips into adjacent wells of the source plate, each tip filling with approximately 1 micro liter of DNA solution. The DNA samples are, in most cases, labeled by incorporating fluorescently tagged nucleotides. The cone-shaped printing tips in Brown's original system were stainless steel with manually sharpened points and a slit up the center for holding the DNA solution. They operated on the same principle as a quill pen; liquid was drawn up by capillary action and deposited when the tip made contact with the slide surface. The printing tips are tapped leaving a small (less than 0.5 nano liters) drop at identical positions on each slide. With the spacing between tips deployed in the microarrayer the entire human genome could be spotted onto a standard 1-inch by 3-inch laboratory slide.

After hybridization a fluorescent image of the array is acquired by a laser scanning confocal microscope. The scanner has a laser (or lasers) producing light at the appropriate wavelength for the excitation spectra of the two dyes (red and green) being used. The light passes through the microscope objective and illuminates a single point on the slide. The emitted light gathered by the objective is filtered to remove the excitation beam, passed through a pinhole (removing noise), and finally quantified in a photomultiplier tube. The relative amount of fluorescence is measured for each spot on the array using software Brown's team developed for segmenting the images into boxes and determining the average fluorescence for each box. The advantage of using fluorescent signals is that they do not disperse, and accordingly allow for very dense array spacing. Also a significant advantage of using two or more differently labeled probes targeted to the same spot in this system is that each can be detected separately. In this way, two-color hybridization detection allows for a direct quantitative comparison of the abundance of specific sequences between two probe mixtures that are hybridized competitively to a single array.

Brown, Shalon, and Smith [ 29 ], and Davis and Schena [ 30 ] have argued that spotted microarrays have several advantages over the in situ chips designed by Affymetrix and Edwin Southerland. As we have seen in the case of GeneChip ® design, in situ synthesis methods work with oligonucleotides, libraries of nucleic acid sequences of between 2–25 base pairs. On a GeneChip ® a given gene might be represented by 15–20 different 25-mer oligonucleotides that serve as unique sequence-specific detectors. To be effective, the Affymetrix arrays require gene sequence information for specifying the de novo synthesis of the oligomers on the array. Spotted microarrays by contrast represent genes by single DNA fragments greater than several hundred base pairs in length, and virtually any length or origin. Moreover, spotted arrays do not require prior sequence knowledge but can be produced from both known and unknown cDNA and PCR fragments. Spotted microarrays, it is argued, are more flexible and more easily adaptable to a variety of research problems in genomics. Also to the point, spotted microarrays are inexpensive by comparison to Affymetrix chips [ 31 ]. Indeed, microarrayers based on the Brown-Shalon design could basically be constructed in-house by most major university research labs at a complete cost (in 1999) of around $60000 [ 32 ]. Brown in fact has been so committed to the low cost production of microarrayers and an open source approach as a means to expedite the production of knowledge in genomics that he posted on his Stanford website all the details of manufacture for his microarray system, including all the software updates for operation of the scanning system, details on manufacturing and servicing the printing tips, and other fine points of the system.

To study the adoption of both in situ and spotted microarray technologies, we considered the first academic articles either reporting studies based on using DNA chips or simply discussing DNA microarrays. We focused on pre-1999 studies because the DNA chip-based research began to take off in 1999. These articles broke down into four main types: results of microarray studies, overviews of how to use gene chips, technology forecasts, and descriptions of new or otherwise improved DNA chips. As an indication of what types of studies were represented in the early publications about gene microarrays, we present Table 4 :

Of the early articles we studied, by far the majority reported on the results of experiments using DNA chips. Most of these studies aimed to uncover significant genetic information in areas of existing interest, such as cancer and cardiovascular disease; to understand the role of genes already identified as being important to particular diseases; or to attempt wide-scale gene expression monitoring of organisms whose genomes were already heavily studied, such as the Arabidopsis plants and Saccharomyces yeasts. By addressing several different research communities, these initial studies served to broadcast the potential of the new microarray technology. While these studies were excellent advertisements for the technology, they also opened up promising avenues of inquiry, helping the technology establish itself in a variety of research areas.

Getting involved with the technology early on was not simply a matter of desire; generally, early authors had some affiliation with Affymetrix. In part because of their longstanding relationships and ongoing collaborations with Affymetrix and due to their internal microarray development efforts (largely arising from their collaboration on the Human Genome Project), Stanford and the NIH also possessed a great deal of in-house expertise in using microarrays , which allowed them to assist researchers from other organizations in using the technology. In fact, when we tabulated the affiliations of scientists appearing on the pre-99 microarray studies, we found that Stanford, NIH, and Affymetrix appeared most often. Table 5 lists the most frequently occurring author affiliations within the set of 130 early articles on microarrays [Note M]:

Perhaps unsurprisingly, the first organizations to publish studies based on research using DNA chips were often those that had the strongest links to gene chip manufacturers. In fact, the second most common organization to appear as an author affiliation among the 130 studies we surveyed published prior to 1999 was Affymetrix. The top organization was Stanford, which had collaborated extensively with Affymetrix in the development of the GeneChip ® , was developing its on pin-based arrayers, and possessed a great deal of in-house expertise in using the gene chips. The NIH's massive network of intramural research and its strong links (including research collaborations) to Stanford and Affymetrix made it third.

It was not simply a matter of being involved with the development of various microarray systems that led to publication, later research collaborations with and between these expert organizations also coincided with earlier and more frequent publication. For example, 28% of articles with a Stanford author also had an Affymetrix scientist and researchers from Stanford spin-out Synteni (whose technology was largely based on the Dari Shalon and Pat Brown system) appeared on 20% of the Stanford publications.

The top three organizations listed above – Stanford, Affymetrix, and the NIH – were the major "hubs" (or highly connected points) in co-authorship networks for the 130 studies we surveyed. To study the network of collaborations during this early phase of research using gene chips, we used an analysis tool that graphically places organizations according to their co-authorships with other organizations [Note N]. For example, in Figure 4 , Affymetrix (large green node) co-authored with Princeton, but Princeton did not co-author with NIH (large blue node), so Princeton is near Affymetrix but distant from NIH. Furthermore, although Affymetrix and NIH co-authored papers together, they also co-authored papers with several organizations that did not co-author with both organizations, thus Affymetrix and NIH are pulled some distance apart (as opposed to NIH and Stanford, the large red node, which share more institutional co-authors) [Note O].

There were many organizations represented in the first 130 articles dealing with DNA microarrays, but Stanford, Affymetrix, and the NIH emerge as major nodes in this network. Often, other organizations would partner with one or more of these major players and then go on to collaborate with organizations previously outside the network. The heavy overlap of collaborations indicates that this was a fairly tight-knit research community. Several organizations in the center and upper right of the map collaborated with at least two of the three major players. Interestingly, many of the initial participants in microarray based research were also involved in the Human Genome Project.

Institutions that were the first to publish microarray studies and that collaborated with DNA microarray makers were also the best able to attract federal funding for microarray based research [Note P]. Organizations that appeared in Table 5 as having been the first to publish studies based on gene chip research tended to be those that received the most federal grants for DNA microarray research over the period 1993–2004 (shown in Table 6 ) [Note Q]. This particular phenomenon in the academic setting of being first to collaborate with Affymetrix, subsequently being first to publish DNA microarray based studies, and in turn receiving more federal funding is roughly analogous to the type of positive feedback loop that economists have used to describe how initially successful high technology firms become increasingly entrenched within their industries.

DNA microarray makers such as Affymetrix have been hubs for an expanding network of companies and technologies across the spectrum of technologies fueling contemporary biotech, gene-based medical therapies, and areas of materials science. These companies have drawn heavily upon academic researchers as consultants and scientific advisory board members, and they have collaborated with academic researchers in sponsoring postdoctoral work and a variety of research projects funded by the NIH, NSF, DOE, and other federal agencies. The academic researchers involved have only in rare cases relinquished their university positions to move into industry. While some of these individuals, such as Schultz, Berg, Stryer, Ron Davis, Mark Davis, and others have been involved in numerous startups, they have returned to their universities (Stanford and UC Berkeley) where they have continued to develop graduate programs that incorporate these new innovations. Other Stanford faculty, such as Fabian Pease and Calvin Quate, have continued as advisors and collaborators in shaping new generations of microarray and sequencing technologies at Affymetrix. Through these technologies and the academic researchers who have participated in developing them, research programs at Stanford and other universities in a variety of different disciplines have taken new shape and direction.

In order to trace the widespread impact of microarrays on the academic research environment, Table 7 presents a chronological overview of the interest in microarrays and gene chips by several disciplines as indicated by citations to the first 130 articles published based on microarray research. (For the totals from nearly all fields citing microarray research, see Appendix A) [Note R]. The data show that interest in DNA chips and microarrays more generally was manifest in a variety of disciplines. As a new, promising, but unstable and unproven technology, microarrays were attractive as a platform that could be improved upon by many different fields. In an era when researchers were motivated to find new ways to interpret the massive amounts of data being generated by the Human Genome Initiative, researchers in just about every field of biomedicine were looking for novel high-throughput techniques to refine genetic analysis and develop tools for rapidly interpreting gene expression data. In many of the new areas, the microarray and gene chip were tools for advancing a program of "molecularizing" established disciplines. But this could not be accomplished by simply plugging in a microarray and reading off the results. New tools and even modifications of the gene chip itself had to be developed in order to assimilate the microarray to the research objectives of these several fields. Multidisciplinary teams of researchers and collaboration between academic researchers and their industry partners proved essential to advancing the technology. The demand for alternatives greatly expanded the market for these research tools and, as we show below, created opportunities for other firms to enter the market.

The top three categories citing these studies (Biochemistry, Biotechnology, and Genetics) were not surprising; they represented the areas DNA microarrays were squarely targeted to address. However, the amount of interest generated around microarray research methods was quite striking. When first released there was much concern regarding the reliability of the chips, quality control issues in manufacturing them, and how to interpret results of microarray experiments. In some cases it was difficult to reproduce the results of experiments based on DNA chips. In addition, researchers discovered that each manufacturer's DNA microarray had its relative strengths and weaknesses; finding the right chip for the job was and still is of significant concern. Many studies were done both to address a particular research question and to learn something about how to better use gene chips.

The next major adopters of microarrays were those investigating cancer and cell biology. Often, these studies involved comparing the expression of thousands of genes in tumor cells to their expression in non-tumor cells. The same type of cancer (eg breast cancer) may involve a different (and very large) set of genes depending on the patient, so it is not enough to simply determine that gene A is related to cancer B. It is often necessary to capture the broad set of involved genes (including those regulating expression) and their interplay to begin to profile particular cancers. An understanding of the processes in cancerous cells aids in designing future drugs to disrupt the chain of events. More immediately, gene expression profiling of a particular patient's tumor through diagnostics, the genes for which are often selected by expression analysis with high density microarrays, enables prediction of the efficacy of existing treatments. Thus, microarrays enabled comparative study of gene expression in cells that led to insights about the complex processes behind cancer progression, but they also allowed for research on selecting patient-specific treatments based on gene expression profiles in tumor cells.

Some of the biological and medical fields affected by microarray research raise equally interesting issues. Microarrays enabled a broad range of researchers to better address questions such as how certain genes and their expression are related to the processes involved in particular diseases, to development and aging, and to the workings of the brain. Microarrays could also be used to address questions on evolution. In other words, microarrays not only provided a valuable tool to these researchers; in certain cases, they made genetics more relevant to their respective fields than it had been previously, and in particular, to their methods of inquiry.

There were also technical fields that took up research on DNA microarrays not for purposes of applying them within the field but in order to improve them and to provide better methods for interpreting gene expression data. Physicists, chemists and various kinds of engineers created custom microarrays, labeling systems for genetic material and systems for reading gene chips, or they explored new methods of manufacturing arrays. Interestingly, the sheer volume of data generated by gene expression studies forced geneticists, biologists, and others using microarrays to pull statisticians, mathematicians, and computer scientists into their research teams. Methods of reading, visualizing, and interpreting gene expression information and linking it to existing scientific knowledge became codified in a plethora of computer programs from in-house statistics and visualization tools at universities to major software suites developed by corporations that can be connected to online repositories of biological information.

In order to convey a sense of the interests in microarray technologies motivating researchers, we present in Table 8 a list of articles from many of the new fields that received a substantial number of citations. Many of these articles served as a basic bridge into a new discipline, making microarrays relevant to the science and/or vice versa. In a network view, they would represent a major forward-linking hub that collapses a question addressed within the authors' traditional field of study into a problem solvable with microarrays and motivates a flurry of subsequent research in that new domain. For example, statistical analysis of gene expression data has become a major topic of research at many universities; as the table shows, one study that used statistical methods to evaluate microarray data, despite being published in 2000, was cited over 300 times.

While some of these articles' citations simply reflect acknowledgement of the new technology being applied in some fashion, many aim at expanding the capabilities of microarray technologies by addressing fundamental questions in an existing research domain. Nonetheless, both types of citations indicate the growing relevance of gene expression and other microarray based studies on various scientific fields.

Below we chart the rising use of microarray technologies and research underpinned by microarrays through counts of microarray related studies [Note S] by subject (according to the Scopus database) and by departmental affiliation of at least one of the authors. We are particularly interested in highlighting the growth of use in particular disciplines in addition to biology, biochemistry, and genetics, and in illustrating how microarrays became relevant to a host of fields that could benefit from a better understanding of gene processes. In addition, we try to demonstrate that non-biological fields such as computer science became involved in order to enhance the microarray research itself. While neither subject classifications nor departmental affiliations provide a definitive account of the story of microarrays in these fields, we believe that the two approaches to tracking diffusion reinforce one another and at a minimum point to the growing relevance of large scale gene expression monitoring technologies in various academic disciplines. Figure 5 presents the number of microarray articles by subject over a seven year period in order to demonstrate the growing relevance of microarrays to diverse disciplines. Following, Figure 6 attempts to capture a similar picture of the spread of microarrays into different corners of academia through the departmental affiliations of authors rather than the subject classification of the article as in Figure 5 .

As we have discussed above, microarrays were not a simple tool biologists and geneticists could readily apply to understanding the role of particular genes. They often had to enlist the support of colleagues in other departments to analyze, view, and interpret the data provided by DNA microarrays. In addition, research to improve numerous aspects of gene chip experiments took hold in departments outside of the biological sciences. In addition, biological fields that still had to profit significantly from the results of mapping the human genome and myriad studies on individual genes were now able to better link existing research questions to genomics questions.

5. Commercial interest in DNA microarray technology

Although Affymetrix has dominated the commercial market for DNA microarrays since its inception, distantly followed by Agilent, it is important to note that nearly half of scientists using microarray systems had built them locally according to plans similar to those made available by Pat Brown and his colleagues at Stanford. While these generally offered less reliability, consistency, and had fewer applications, they were far cheaper than the commercially available systems.

Alongside the research universities and other non-profit institutions that had begun in incorporating gene chips into their research programs, many companies were looking at how to enter the gene chip business and how to build complementary systems. Interestingly, many of these efforts, particularly at the smaller companies were offshoots of the university research that had begun earlier on some aspect of gene chip applications or technologies, such as bioinformatics software. Larger companies often stepped in by applying existing expertise and familiar manufacturing techniques to building their own versions of DNA microarrays.

In Table 9 we have identified organizations with a commercial interest in gene chips through their patents' backward citations to Affymetrix patents [Note U]. After finding the organizations that cited Affymetrix patents most frequently (reflecting their interest in microarray technologies), we selected those that we believed represented one of the common or important directions that microarray technologies took following the introduction of the GeneChip ® [Note T]. In the rightmost column we indicate whether the organization received any types of government grants for its research in this area. Twenty-five of the forty organizations listed received government grants (note the presence of universities, which depend heavily on government funding for all their research, and large companies, which rarely receive funding for this type of research). In the case of smaller, recently formed companies, 20 out of 28 received government funding. While the data is limited, it appears that new companies building technologies around microarrays were heavily supported by the federal government, helping to broaden the applications and power of the technology.

6. Case studies

Our case studies were chosen to explore different perspectives on the issues we have defined as salient features of the networked, symbiotic structure supporting innovation in technology regions such as the Silicon Valley; namely, the role of federal support, the ability of companies to draw upon universities to provide expertise in addressing challenging scientific questions or help them couple their existing systems to new technologies, and the ability of commercially viable technologies generated by high-tech companies to attract government funding and shape entirely new academic research directions. It might be argued that Affymetrix is a special case since, with its star-cast of consulting scientists, engineers, and successful entrepreneurs it was so remarkably positioned to take optimal advantage of the networks supporting innovation. To address such concerns we chose four case studies that represent different trajectories microarray technology could take. Affymetrix was a startup. But what about a large, well established firm with large internal resources to devote to developing its own technology for entry in the microarray market? Would it act independently of the network? Or would it draw upon the same regional networks as Affymetrix in developing its own microarray platform? What sorts of factors would motivate it to enter the market, and what sorts of resources would it draw upon? The case of Agilent, daughter firm of Silicon Valley giant Hewlett-Packard provides a striking opportunity to explore these issues.

More importantly though, these case studies will help to illustrate in detail how a viable infrastructure of scientific research and complementary technologies emerged in the case of DNA microarrays, motivating universities, industry, and government, each in different ways, to pursue competitive scientific and commercial opportunities in the emerging microarray landscape. But gene chips or DNA microarrays turn out to be only one possible application of microarrays. In the case of Symyx we explore how researchers – indeed researchers intimately connected with the original microarray project at Affymax – seized the opportunity to launch a new company that developed the basic idea of the original microarray to vigorously pursue combinatorial chemistry in the direction of non- organic materials science. Quantum Dot provides an example of a "classic" university startup coming out of an entirely different technical domain, nanocrystals, and seizing an opportunity to incorporate its technology as a component in the DNA microarray system. Our final case, Perlegen, is a spinoff of Affymetrix itself, focused on lines of research aimed at extending basic Affymetrix technology in ways directly relevant to concerns of the pharmaceutical industry. Together these case studies show that once microarrays got off the ground, players such as these made the technology an expansive and self-sustaining force.

Conclusión

In this study we have explored the dynamics of innovation in a networked technology region, Silicon Valley. Our study confirms the picture put forward by a several researchers that the open character of this economy is what makes it truly innovative. In an open system innovations emerge from the network. The emergence and diffusion of microarray technologies we have traced here provides an excellent example of an open system of innovation in action. Whether they originated in a startup company environment that operated like a think-tank, such as Affymax, the research labs of a large firm, such as Agilent, or within a research university, the inventors we have followed drew heavily on knowledge resources from all parts of the network in bringing microarray platforms to light.

Another key point in our study is the role of federal funding in stimulating the innovation networks associated with the emergence and diffusion of microarray technology. Federal funding for high-tech startups and new industrial development was important at several phases in the early history of microarrays. As we have seen, federal funds were the enabling factor for several startups, and as the technology evolved, support for collaborative research projects using gene chips and microarrays was crucial to evolving the various microarray platforms and their supporting technologies. Companies developing microarray technologies such as Affymetrix and Perlegen have functioned very much like research programs at universities, and in many ways the collaborative research going on in those firms with academics is more productive and has a greater impact than research in most university settings. Federal funding of academic researchers using microarrays was fundamental to transforming the research agendas of several fields within academe. The typical story told about the role of federal funding emphasizes the spillovers from federally funded academic research to industry. Our study has shown that the knowledge spillovers worked both ways, with federal funding of non-university research providing the impetus for reshaping the research agendas of several academic fields.

In the early days of the Human Genome Project (HGP) Walter Gilbert pointed to a paradigm shift about to transform biology as a result of the efforts to sequence the genomes of all organisms and store that information in large electronic databases [ 53 ]. At the heart of this revolution were automated computer-based systems for the massive throughput of biological information and technology that would allow biologists to perform thousands of experiments in parallel. Gene sequencing technology, including PCR machines, laboratory robotic systems, such as the arrayers we have discussed, bioinformatics software such as, FASTA, BLAST, PSI-BLAST, hidden Markoff models, and other sensitive tools for imaging and interpreting sequence information: these and other tools for automated acquisition and mapping of genetic sequence information were products of the Human Genome Project, all enabling elements of the revolution in molecular biology anticipated by Gilbert. Microarrays are among the signature technologies of this ongoing Genomics Revolution.

There are important parallels between the revolution in molecular biology ushered in by the Human Genome Project and the revolution in computing and information technology presided over by the NSF and DARPA's Information Processing Technology Office in the 1960s–1970s [ 54 , 55 ]. Both were large scale federally funded efforts that provided support to academic research and industry startups – literally creating the field of computer science and a nascent computer industry on the one hand and the various academic, medical and commercial fields related to genomics on the other. In our view, the infrastructure of federal funding policy was crucial for enabling the Genomics Revolution. Over the span of 15 years – from 1988 through its completion in 2003 – the Human Genome Project expended more than $3.8 billion dollars in federal grants and contracts to universities, genome research centers, and industry. From the start, HGP planners anticipated and promoted private sector participation in developing and commercializing genomic resources and applications. When the HGP was initiated, vital automation tools and high-throughput sequencing technologies had to be developed or improved. The HGP leadership recognized these goals could not be achieved within the timeframe set for the Project without complementary efforts of both university and private sector researchers. The strategy succeeded beyond anyone's expectations: the cost of sequencing a single DNA base was about $10 at the outset of the HGP; by 2001, sequencing costs had fallen about 100-fold to $.10 to $.20 per base. DOE-funded enhancements to sequencing protocols, chemical reagents, and enzymes contributed substantially to increasing efficiencies and reducing costs. The commercial marketing of these technologies greatly benefited basic R&D, genome-scale sequencing, and lower-cost commercial diagnostic services. As we have shown in the case of microarrays, substantial public sector R&D investment by the DOE and the NIH launched key startup ventures such as Affymetrix, Synteni, Incyte and other firms; and as we have also shown, federal funding of research collaborations between academic and industry researchers at these companies improved the technology and reshaped the academic landscape. In addition to the HGP itself, the key policy instrument enabling these developments was the Small Business Innovation Development Act of 1982, which created the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs [ 56 ] [Note Z] . Although not created with the Human Genome Project in mind, the Small Business Development Act and the direct federal funding of industry R&D by the NSF, NIH, and DOE provided the basis for the two-way flow of innovation between academic researchers and industry that has fueled the Genomics Revolution.

Notes

[A] Schultz was already celebrated for his contributions to the understanding of the mechanisms of molecular recognition and catalysis in biological systems. He was working on the design of highly efficient "catalytic antibodies" able to cut, splice, and modify biological molecules at specific points. Schultz was also beginning his pioneering work on a new technique for studying proteins in which unnatural amino acids are inserted site-specifically into proteins so that their catalytic and binding properties and stability could be studied.

[B] The board included Paul Berg, Carl Djerassi, Mark Davis, Avram Goldstein and Michael Pirrung from the Stanford biochemistry, chemistry, and pharmacology departments; Murray Goodman, from biochemistry at UC San Diego; and Joshua Lederberg from Stanford and the Rockefeller University . They met weekly with Zaffaroni, Schultz, Read, and Lubert Stryer, who was the director of the board. Stryer had taken a leave of absence from Stanford to head the research team at Affymax.

[C] For an excellent discussion of Geysen's peptides on pins method and its limitations see Pirrung MC: Molecular Diversity and Combinatorial Chemistry: Principles and Applications . Amsterdam: Elsevier; 2004, pp. 17–20. Geysen's approach involved a strategy of iterative steps, called iterative deconvolution, in identifying the optimum set of peptide pairings that would identify a protein-binding region; the Affymax scientific board felt it involved assumptions that would allow potentially valuable drug leads to be missed.

[D] Another strategy was Richard Houghten's "tea bag" approach and the "split-and-mix" synthesis pioneered by Árpád Furka from Hungary (later at Advanced ChemTech in Louisville, KY).[ 1 ] Kit Lam from the University of Arizona (now at UC Davis) took the split-and-mix method to the next level in 1991 by growing the peptides as attachments to polystyrene beads as the supports for the synthesis. Lam's highly efficient split-and-mix synthesis method generated "one-bead one-compound" (OBOC) combinatorial peptide libraries with millions of peptides, in which each 80-μm bead displayed only one peptide entity.

[E] The ability to attach and remove molecules using light to activate or deactivate linkages at different stages of the synthesis was crucial to Pirrung and Read's ideas for in situ synthesis. The parallel synthesis in situ on the solid surface using photolithographic techniques depends on decoupling protective groups followed by coupling of oligonucleotide. In order for synthesis to progress, a protecting group on the 5'-hydroxyl terminus of the growing DNA molecule must be removed. As demonstrated by the work of Patchornik and others, this deprotection reaction is readily adapted to light control through a large class of protecting groups that are photochemically removable. With his background in photochemistry, Pirrung was deeply familiar with the research on photolabile protecting groups in mononucleotide syntheses by Patchornik and his students dating back to the early 1970s, in addition to the contributions to the field of a number of other researchers in the intervening years . For a list of more than thirty scientific publications relevant to the gene chip see Fodor SPA, Stryer L, Winkler JL, Holmes CP, Solas DW: USPTO 5489678 . Photolabile nucleoside and peptide protecting groups, February 6, 1996.

[F] Pease's wife, Anna Caviani Pease was first author on the May 1994 paper in the Proceedings of the National Academy of Sciences which introduced sequencing by hybridization on the Affymetrix gene chip. Anna Pease had her Ph.D. in Chemistry from UC Berkeley in 1990 and joined Affymax shortly after. She also took a law degree from the Stanford Law School in 1995 and joined the firm of Dorsey & Whitney in the Stanford Research Park as Co-Head of the firm's Life Sciences and Healthcare, focusing on strategic aspects of patent law in the biotechnology, pharmaceutical and chemical fields. Pease continued to work at Affymetrix, where she became the chief inventor on several Affymetrix patents on DNA arrays.

[G] This includes multiple appearances by the same researcher. To identify these scientists, we extracted all of the inventors from Affymetrix patents, along with their state. We matched inventors and states to all US patents to find all their previous patents and hand classified those patents to ensure that the inventor was the same individual. While there was some overmatching and some undermatching because of common names and individuals moving across states, we believe this was a fairly complete process. We then hand-classified unique inventors based on the subject matter of the patent, which assignees they were associated with, and temporal/geographic information (eg you cannot file patents from two different places at the same time). In addition to identifying previous and contemporary university affiliations, we included of patents that were co-assigned to a university.

[H] Pirrung moved from Stanford to Affymetrix and then quickly moved to Duke.

By 2000, when he filed a patent with Affymetrix, Francis Collins was Director of the National Human Genome Research Initiative.

The University of California faculty who worked with Affymetrix were from the Berkeley campus.

Andrei Mirzabekov was also a professor in Russia at the Moscow Physics-Technical Institute at the time, but we chose to include his affiliation with Argonne here because that was how he became involved with Affymetrix.

The reason we say "some" of the faculty who appeared on the patents is due to our matching process. Because there were hundreds of Affymetrix inventors we decided to link them to a larger database of all patents from 1974–2003 using their name and state. We screened these resulting patents against a table of all assignees to identify potential university professors (assuming they had filed a patent at their respective institution). Following our initial matching process, we searched the web for each suspected faculty person to verify his or her affiliation. Graduate students or post docs who appeared on the patents were not included despite the fact that a few of them are now professors at other institutions.

[I] SBIR and STTR awards are for small companies with fewer than 500 employees. The PI on the award does not necessarily have to be a member of the company. For more information, see: http://www.sba.gov/

[J] NHGRI Grant Number: 5R01HG000813-03

Project Title: Sequence Determination by Hybridization

Principal Investigator: Stephen A. Fodor

Abstract: The long term goals of this proposal are to construct spatially defined arrays of oligonucleotide probes and to study the feasibility of using these arrays in applications of sequencing DNA by hybridization. A multidisciplinary research program is proposed which will integrate the necessary expertise in photolithography, photochemistry, synthetic chemistry, detection technology, informatics and applications to large scale DNA sequencing. We will apply newly developed techniques in light-directed polymer synthesis to oligonucleotide chemistry, explore kinetic and solvent related parameters of target hybridization to oligonucleotide arrays, read the positions of hybridization by epifluorescence microscopy, and apply new combinatorial methods to determine sequence from the hybridization data . The method will be applied to actual sequencing applications at the yeast genome center. Successful completion of this work will lead to sequencing instrumentation that will provide order of magnitude improvements in DNA sequencing productivity and will be directly applicable to the Human Genome Project.

Institution: Affymetrix, Inc.

3380 Central Expressway

Santa Clara, Ca 95051-0704

Project Start: 25-SEP-1992

Project End: 31-OCT-1995

[K] Radius: https://radius.rand.org . The asterisk* for the last item in the total column refers to the fact that Radius has not updated the data for funding received in 2004 and 2005.

[L] Edwin Southern and Uwe Maskos developed an array on impervious supports comprised of short oligonucleotides of up to 19-mer length by in situ synthesis in 1991. The method used a process of physical masking in contrast to the light directed synthetic method developed by Fodor, et al. at Affymetrix. Southern filed for a US patent on this process in 1994. See Southern EM: USPTO 5,700,637. Apparatus and method for analyzing polynucleotide sequences and method of generating oligonucleotide arrays, December 23 1997.

[M] Based on our analysis of the first 130 articles published regarding DNA chips. We used author affiliations to count institutions, thus one article could add more than one institution to these totals. The exact search query in Google Scholar (which allows for full text article searching) and Web of Science (in the TS field, with a slightly different query format): microarray OR "gene chip" OR genechip OR "DNA array" OR "oligonucleotide array" OR "DNA chip" OR "cDNA array" OR "cDNA chip" OR "oligonucleotide chip". We then found all the pre-1999 articles in Web of Science (which contains better bibliographic information for download). We were not overly concerned with finding every early microarray paper, we simply needed a sample of those papers. Moreover, it is likely that early users of microarrays were more likely to make the use of the technology a more prominent feature of the paper than later users both to highlight its novelty and to justify their approach, once the practice had become more customary. Thus, we expect that a large proportion of these early studies using microarrays explicitly referred to their methods and equipment. Approximately 60% of our search results were false positives (mostly due to "microarray") and these were eliminated from the dataset by reading the methods section of the paper (or some part of the discussion if it was a forecasting article), this left us with 130 papers. The entire search was independently checked for completeness on PubMed using the MeSH controlled terms (which added only one or two articles to our dataset) and by using Affymetrix's list of publications using DNA microarrays.

[N] We used VxInsight from Sandia Labs and its module VxOrd to do the initial placement of organizations according to their coauthorship patterns. We used the resulting coordinates in KiNG (available free from the Duke Biochemistry Department website: http://kinemage.biochem.duke.edu/software/king.php ) to do the final visualization.

[O] There were also several organizations that had collaborated separately but did not connect to this larger network of early research. These largely included research efforts in Korea, Taiwan, Finland, and Germany, but also included a few articles published by US based authors that did not link up with the rest of the early research.

[P] Note that this distribution is heavily skewed toward 2000–2004, and continued to rise through 2004 (although we did not have complete coverage of 2004). A major uptick occurred beginning in 1999/2000, when gene chips officially hit the market. The grants in the early years were very long term grants that had microarray projects added in during later years.

[Q] Based on our searches within Radius. We believe that proposed studies making use of DNA chips have become less likely to mention the technology as it becomes more commonplace, which would mean that we capture a smaller fraction of newer studies making use of the technology as compared with older government proposals, despite the overall rising trend.

The query we used, after experimenting with the usefulness of various search terms, was: "gene chip" OR genechip OR "gene array" OR "gene microarray" OR "dna microarray" OR "dna array" OR &quot ;dna chip" OR "cdna microarray" OR "cdna array" OR "cdna chip" OR "oligonucleotide array" OR "oligonucleotide chip" OR "oligonucleotide microarray"

Washington University is based in St. Louis and is separate from the University of Washington system.

[R] Cite* (total citations from each category) reflects the number of articles in each category that cited the first 130 microarray-based studies. The articles were weighted by the number of times they cited the first 130, because we believed that this is an indication that the article is more relevant to microarrays. In addition, in most cases articles had multiple category classifications. We decided not to divide each article by the number of classifications it had because those articles with multiple classifications were more likely to be those of interest to us and, it can be argued that we should not risk downplaying the importance of an article due to the arbitrariness of a classification system. We decided to exclude categories that were not useful, such as Multidisciplinary Sciences and Multidisciplinary Chemistry.

[S] According to our keyword searches within the Scopus database. The keywords we used were similar to those above: "gene chip" OR genechip OR "gene array" OR "gene microarray" OR "dna microarray" OR "dna array" OR "dna chip" OR " cdna microarray" OR "cdna array" OR "cdna chip" OR "oligonucleotide array" OR "oligonucleotide chip" OR "oligonucleotide microarray"

[T] We considered organizations that cited Affymetrix patents fourteen or more times and used normalized assignee names to avoid undercounting because of typographical errors and acquisitions.

[U] In this case, we present the organizations that cited Affymetrix fourteen or more times, fourteen was only chosen in the interest of space. The reason we describe these organizations as "selected" is that we excluded a few organizations because the technology they developed was not very innovative or if there were already enough examples of assignees on the list that were very similar. We also excluded companies with technology that was tenuously related to DNA microarray technology. Incyte changed its name and the total number of citing patents was fifteen.

[V] We used a three step query based on keywords, classifications, and inventor names for this search. We tested this method on Affymetrix's patent and application portfolio and found 630 out of 633 of its patents. While this query method could be improved, it was not necessary for our purposes to find every single Agilent/HP patent on microarray-related systems, nor was it crucial that we exclude every single invention that was not related to microarrays.

[W] We did this by matching inventor names and state/country locations to prior patents. We made common sense assumptions such as inventors not being able to file from multiple locations on different types of technologies at the same time. We also assumed that inventors who appeared to have switched companies and then quickly moved back, or moved back and forth repeatedly, were actually different individuals (although we kept an eye out for university consultants who might have exhibited this pattern).

[X] Based on a search of the Scopus database: http://www.scopus.com . The query used was (TITLE-ABS-KEY(microarray OR "gene chip" OR genechip OR "DNA array" OR "oligonucleotide array" OR "DNA chip" OR "cDNA array" OR "cDNA chip" OR "oligonucleotide chip") AND AFFIL(agilent))

[Y] Interestingly, Moungi Bawendi and Paul Alivisatos, the heads of their respective labs at MIT and Berkeley had both worked at Bell Labs in the 80s when major discoveries on the properties of quantum dots were made there.

[Z] The Small Business Innovation Development Act of 1982 legislated that 2.5 percent of the budget of any federal research program with a budget over $100 million would be devoted to assist small business concerns to obtain government contracts for their own research and development, and ( in the case of the STTR Program) to assist collaboration between small business concerns and federally funded projects at universities or federally supported research centers for the purpose of transferring the technology to the commercial sector.

Agradecimientos

The authors are grateful to three anonymous reviewers for extremely helpful comments. We are also grateful to our colleagues Wesley Cohen and Robert Cook-Deegan for comments and discussion of several drafts of the paper. The authors gratefully acknowledge the support of the National Human Genome Research Institute and the Department of Energy (CEER Grant P50 HG003391, Duke University, Centers of Excellence for ELSI Research).