Redes Complejas en Biología de Sistemas …Vision que guia los trabajos del labo y actividades desarrolladas en el periodo Dr. Ariel Chernomoretz

Sobre el curso Nombre materias.df.uba.ar/IRCBSa2016c2 Grado: Biofísica Posgrado: Física de los Sistemas Biológicos materias.df.uba.ar/IRCBSa2016c2 Teóricas subidas a la página de la materia Para leerlas antes de la clase (!)

Régimen teórico-práctico Proyecto final (grupos) Trabajos Computacionales 3 Guias Computacionales 1 Proyecto paper Guías de problemas Aprobación: Prácticas: Tr. Computacionales Teóricas: Proyecto final Guía de problemas

Sobre el curso Textos Papers: Ver pagina de materia “Networks: an Introduction”, Mark Newman “Network Science”, Albert-Laszlo Barabasi http://barabasi.com/networksciencebook/ http://barabasilab.neu.edu/networksciencebook/downlPDF.html “Networks, crowds and markets”, Easley & Kleinberg. http://www.cs.cornell.edu/home/kleinber/networks-book Papers: Ver pagina de materia

Vivimos en un mundo complejo

Sistemas Complejos RAE: complejo, ja Del lat. complexus, part. pas. de complecti 'enlazar'. 1. adj. Que se compone de elementos diversos. 2. adj. complicado (‖ enmarañado, difícil). 3. m. Conjunto o unión de dos o más cosas que constituyen una unidad. Complejo vitamínico. 4. m. Conjunto de establecimientos industriales generalmente próximos unos a otros. 5. m. Conjunto de edificios o instalaciones agrupados para una actividad común. 6. m. Psicol. Conjunto de ideas, emociones y tendencias generalmente reprimidas y asociadas a experiencias del sujeto, que perturban su comportamiento. Enciclopedia Britannica: Complexity  A scientific theory which asserts that some systems display behavioral phenomena that are completely inexplicable by any conventional analysis of the systems’ constituent parts. These phenomena, commonly referred to as emergent behaviour, seem to occur in many complex systems involving living organisms, such as a stock market or the human brain. (Would-be Worlds (1997) John L. Casti)

Sistemas Complejos Muchos agentes + leyes de interacción simples = comportamientos emergentes no-triviales

Sistemas Complejos Comportamiento social Comportamiento bursátil El enfoque conceptual es el de sistemas complejos. Muchos agentes… Comportamiento bursátil

La célula como sistema complejo Escala intra-celular Neutrophyl chasing a bacteria Escala celular Bajo el enfoque de los sistemas complejos se puede analizar lo que ocurre a nivel celular…interacciones bioquimicas dan lugar…a otras esacala a comportamientos no’triviales (relevantes desde el punto de vista biologico) como la capacidad de establecer ritmos internos, la de establecer procesos de repdorduccion sexual al sensar la presencia de partners en el medio, o alteraciones en la red de interacciones que repercuten a nivel sistemico Escala organismo Escala molecular Biologia como propiedad emergente

Complejidad multiescala Neutrophyl chasing a bacteria La biologia ES Multiescala Neutrofilo persiguiendo, entre globulos rojos, y fagocitando una bacteria Neutrophyls are the most common type of white blood cell. They can migrate following chemicals cues associated to acute inflammation, bacterial infection, environmental exposure, and some cancers yeast Escala molecular Sistema abierto Muchisimos grados de libertad Interacciones no-lineales Diferentes escalas espacio-temporales Escala celular Funcionalidad biológica Fenotipo celular … Propiedades emergentes

Más es diferente… Neutrophyl chasing a bacteria Reduccionismo ≠ Constructivismo “…The ability to reduce everything to simple fundamental laws does not imply the ability to start from those laws and reconstruct the universe. ..” “… The constructionist hypothesis breaks down when confronted with the twin difficulties of scale and complexity. The behavior of large and complex aggregates of elementary particles, it turns out, is not to be understood in terms of a simple extrapolation of the properties of a few particles. Instead, at each level of complexity entirely new properties appear and the understanding of the new behaviors requires research which I think is as fundamental in its nature as any other. Philip Warren Anderson (Indianápolis, 13 de diciembre de 1923) es un doctor en Física por la Universidad Harvard en 1949, es uno de los físicos más prolíficos y de amplio abanico de los últimos tiempos. En 1977, obtuvo el Premio Nobel de Física por sus investigaciones en la estructura electrónica de sistemas magnéticos desordenados. Después de completar sus estudios trabajó en los laboratorios Bell con muchas de las eminencias electrónicas del momento, Bill Shockley, John Bardeen, Charles Kittel, Conyers Herring, Gregory Wannier, Larry Walker, John Richardson, etc. Pasó un año (1953) como becario del Programa Fulbright en la Universidad de Kyoto, también por el laboratorio Cavendish de la Universidad de Cambridge y más tarde por la Princeton, hasta 1984. Su trabajo abarca un innumerable grupo de materias: ferromagnetismo, resonancia magnética, superconductividad, estudio de semiconductores, líquidos cuánticos, efecto Kondo, vidrio de espín, estrellas de neutrones, superfluidos, materiales amorfos. Fue uno de las fundadores de la física del estado sólido contemporánea. Es autor del libro "More and different:notes from a thoughtful curmudgeon", editado en 2011,1 y que varía ligeramente el título de su célebre artículo "More is different", publicado en 1972 en la revista Science.2 El kit de la complejidad no se pone de manifiesto via reduccionismo …todo es descomponible en elementos y leyes fundamentales….sino ante la imposibiliad de implementar una nocion pragmatica constructivista que a partir de elementos basicos permita entender el funcionamente de las cosas.

Más es diferente… - Neutrophyl chasing a bacteria Ciencia X Ciencia Y Venia discutiendo sobre una idea de Weisskopf, sobre que habia ciencias intensivas (fundamentales: astrofisica, particulas,) y extensivas (aplicaciones de ciencias intensivas)… Broken symmetry: el estado estacionario de cualquier sistema tiene que respetar la simetria de las leyes que lo gobiernan…pero existe broken symmetry Que entidades elementales de la ciencia-X obedezcan leyes de la ciencia-Y, no implica que ciencia-X sea solamente ciencia-Y aplicada.

Sistemas Complejos Neutrophyl chasing a bacteria Sistemas abiertos Agentes + interacciones Ausencia de control centralizado Existencia de lazos de retroalimentacion Estructura modular/jerarquica Reglas simples dan lugar a comportamientos complejos emergentes.

Sistemas Complejos Neutrophyl chasing a bacteria Diversos fenómenos pueden ser abordados como sistemas complejos: sociales, tecnológicos, biológicos, etc Detrás de cada sistema complejo es posible identificar una red de interacciones entre sus componentes Teoría de redes: caracterización de la estructura de interconexionado sobre la que se monta la complejidad del sistema

La metáfora de redes Representacion de interacciones En este enfoque el analisis de las interacciones es muy importante y la teoria de redes provee una herramienta muy importante. En una red: nodos (entidades bioquioquimicas) enlaces (relaciones entre ellas). Hay de diferente tipo PIN, metabolicas de coexpresion de regulatcion genica, pero en todos los casos…. Podemos preguntarnos…El conexionado es random? Si no es asi… patrones de conectividad y organización locales y globales en redes funcionalidad biológica estado global comportamiento a escalas mayores

Ejemplo: redes de interacción de proteinas Biologia Escencialidad Funcion biologica Estudio de enfermedades

Ejemplo: Redes sociales Zachary´s Karate club: Club de karate observado por el antropólogo Wayne Zachary por 3 años Red de 34 miembros y 78 interacciones documentadas Dinámica social fuera de las actividades del club El club se dividió en 2, con 16 miembros cercanos al Sr. Hi (intructor, nodo 1), 18 miembros cercanos a Mr John A (administrador, nodo 34) Antes del colapso hubo intentos de reclutamiento desde ambos lados A social network of a karate club was studied by Wayne W. for a period of three years from 1970 to 1972.[1] The network captures 34 members of a karate club, documenting 78 pairwise links between members who interacted outside the club.[2] During the study a conflict arose between the administrator "John A" and instructor "Mr. Hi" (pseudonyms), which led to the split of the club into two. Half of the members formed a new club around Mr. Hi, members from the other part found a new instructor or gave up karate. Basing on collected data Zachary assigned correctly all but one member of the club to the groups they actually joined after the split. Before the split each side tried to recruit adherents of another party. Thus, communication flow had a special importance and the initial group would likely split at the "borders" of the network. Zachary used the maximum flow – minimum cut Ford–Fulkerson algorithm from “source” Mr. Hi to “sink” John A: the cut closest to Mr. Hi that cuts saturated edges divides the network into the two factions. Zachary correcly predicted each member's decision except member #9, who went with Mr. Hi instead of John A. ZKCC Zachary Karate Club Club is a honorific group[4] that awards membership in the group, along with a traveling trophy, to a scientist who is the first to use Zachary's Karate Club as an example at a conference on networks. The first scientist to be awarded was Cristopher Moore[5] in 2013. An Information Flow Model for Conflict and Fission in Small Groups Wayne W. Zachary Journal of Anthropological Research Vol. 33, No. 4 (Winter, 1977), pp. 452-473

Ejemplo: Redes sociales Zachary´s Karate club: Zachary´s Karate club club: If your method doesn´t work on this network then go home.

Ejemplo: Redes sociales El secreto de la felicidad: Objetivo: evaluar si la felicidad se contagia Diseño Exp: Estudio longitudinal de la red social de Framingham. 4739 paricipantes desde 1983-2003 Objectives To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks. Design Longitudinal social network analysis. Setting Framingham Heart Study social network. Participants 4739 individuals followed from 1983 to 2003. Main outcome measures Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties. Results Clusters of happy and unhappy people are visible in the network, and the relationship between people’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends). People whoare surrounded by many happy people and thosewho are central in the network are more likely to become happy in the future. Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km)andwhobecomeshappy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation. Conclusions People’s happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.

Ejemplo: Redes sociales El secreto de la felicidad: Objectives To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks. Design Longitudinal social network analysis. Setting Framingham Heart Study social network. Participants 4739 individuals followed from 1983 to 2003. Main outcome measures Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties. Results Clusters of happy and unhappy people are visible in the network, and the relationship between people’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends). People whoare surrounded by many happy people and thosewho are central in the network are more likely to become happy in the future. Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km)andwhobecomeshappy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation. Conclusions People’s happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.

Ejemplo: Redes sociales El secreto de la felicidad: Objectives To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks. Design Longitudinal social network analysis. Setting Framingham Heart Study social network. Participants 4739 individuals followed from 1983 to 2003. Main outcome measures Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties. Results Clusters of happy and unhappy people are visible in the network, and the relationship between people’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends). People whoare surrounded by many happy people and thosewho are central in the network are more likely to become happy in the future. Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km)andwhobecomeshappy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation. Conclusions People’s happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.

Management Problema de flujo de información en empresa Hungara Barabasi - Network Science

Management Mapeo de resultados a lenguaje de grafos: ¿a quién consultas para tomar decisiones? Identificación de individuos de alta influencia Barabasi - Network Science

Management Puesta en contexto: ranking dentro de la jerarquía organizacional Ninguno de los hubs ocupan puestos de management Hubs provienen de rangos bajos: managers, group leaders, associated Barabasi - Network Science

Management Mapeo de resultados a lenguaje de grafos: ¿a quién consultas para tomar decisiones? Identificación de individuos de alta influencia Barabasi - Network Science

Management Red de influencia del empleado más conectado (1eros y 2dos vecinos) Empleado de Seguridad y problematicas ambientales Visita regularmente diferentes instalaciones Conectado con mucha gente, pero no con management (!) Gossip center Barabasi - Network Science

Referencias More is Different, Anderson, Science (1972) http://www.britannica.com/science/complexity-scientific-theory http://www.santafe.edu/media/workingpapers/06-10-036.pdf An Information Flow Model for Conflict and Fission in Small Groups, Wayne W. Zachary, Journal of Anthropological Research Vol. 33, No. 4 (Winter, 1977), pp. 452-473