An Introduction to Systems Biology

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  Evolution can be fast. Mutations can occur at a comparatively high rate. A single bacterium in 10ml culture grows and divides to reach saturation of 10^10 cells in less than a day. Since mutation rate is 10^(-9) per base-pair per generation, up to saturation, the bacterial population reach 10 mutation per base-pair. If we treat mutation as randomized force, which abolishes edges in gene regula... (更多)

  Evolution can be fast. Mutations can occur at a comparatively high rate. A single bacterium in 10ml culture grows and divides to reach saturation of 10^10 cells in less than a day. Since mutation rate is 10^(-9) per base-pair per generation, up to saturation, the bacterial population reach 10 mutation per base-pair.

  If we treat mutation as randomized force, which abolishes edges in gene regulatory graph, evolutionarily conserved edges preserve fitness to the organism.

  Autoregulation, including positive and negative autoregulations, are over-represented in E.coli transcription network. Parameters of such autoregulation are under natural selection.

  Negative repression efficiently and robustly lock the gene into steady-state expression. Presence of such repression reduces cell-cell variation.

  Positive autoregulation is slower than unregulated gene. Such slow dynamics can be useful in relatively long developmental processes. (收起)

  2011-02-26 03:21:04   回应

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  While activation/repression graph reveals a qualitative dynamics of simple circuits, further consideration has to be done on separation of time scale of different cellular processes. For stable proteins, especially protein assuming static structural roles, which do not belong to response module to external signals, have a response time similar to that of a cell generation time. (更多)

  While activation/repression graph reveals a qualitative dynamics of simple circuits, further consideration has to be done on separation of time scale of different cellular processes. For stable proteins, especially protein assuming static structural roles, which do not belong to response module to external signals, have a response time similar to that of a cell generation time. (收起)

  2011-02-22 18:29:39   回应

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  Appendix C - Graph Properties of Transcription Networks sparse connectivity: Transcription networks are sparse, since ratio of existing edges and theoretical upper limit of edges << 1. Typically, less than 0.1% of possible edges are found in the network. degree distribution: Typically mean degree is 2 to 10 edges/node. Out-degree distribution can be approximately describ... (更多)

  Appendix C - Graph Properties of Transcription Networks

  sparse connectivity:

  Transcription networks are sparse, since ratio of existing edges and theoretical upper limit of edges << 1. Typically, less than 0.1% of possible edges are found in the network.

  degree distribution:

  Typically mean degree is 2 to 10 edges/node.

  Out-degree distribution can be approximately described as a power law, i.e. P(k) ~ k^(-r), with r ~ 1 to 2. Transcription network often have many transcription factors regulate a few genes, fewer nodes that regulate tens of genes, and even fewer global regulators that regulate hundreds of genes. Global regulators usually respond to key environmental signal to control large ensembles of genes, e.g. bacteria CRP to glucose starvation, RpoS respond to general stress.

  In-degree distribution instead resembles compact distribution such as Poisson distribution. This may correspond in part to physical limitation that promoters are short in simple organisms. In more complex organisms, DNA looping action can increase the number of input transcription factors to a given gene. Higher organisms often display larger in-degrees than microorganisms, accommodating the complex computational needed during development.

  motifs:

  Clustering coefficient C is the average number of triangles that a node participates in.

  Feed-forward loop (a type of triangle) is generally overrepresented in motifs of sensory transcription networks. The major contribution to the clustering coefficient therefore stems from feed-forward loops. This pattern appears to be selected due to its function, such as filtering and response acceleration.

  Generally, it appears that global statistical properties of biological network such as degree sequences and cluster sequences are the result of selection working on the detailed circuit patterns in each individual system. Different networks have different selection constraints, which must be understood in order to understand their graphical properties.

  modularity:

  Network modularity is the degree to which it can be separated into nearly independent sub-networks.

  A measure of modularity is proposed by Newman and Girvan, 2004 and 2005. The rationale of this modularity measure is that: a good partition of a network into modules must comprise many within-modular edges and as few as possible between-module edges. This is achieved by maximizing a modularity measure Q. (收起)

  2011-02-13 06:54:47   4回应

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  Nirvana.Viaje (游标卡尺不估读)

  2.4, p15|p21 dY/dt = \beta - \alpha Y (2.10) 最简单的一阶常微分方程，通解为指数函数乘以某系数，分离变量两端积分便得： => dY = (\beta - \alpha Y) => \int dY = \int (\beta - \alpha Y)dt 用Y_st为初值条件确定系数： => Y(t) = Y_st e^(-st) (2.12) 上式利用t接近零时e^0=1 Taylor近似即得： Y~\beta t (2.15) Ex2.3, p18 习题2.3像是常数变易法解了个形如y'＋P(x)·y ＝ Q(x)的一阶常... (更多)

  2.4, p15|p21

  dY/dt = \beta - \alpha Y (2.10)

  最简单的一阶常微分方程，通解为指数函数乘以某系数，分离变量两端积分便得：

  => dY = (\beta - \alpha Y)

  => \int dY = \int (\beta - \alpha Y)dt

  用Y_st为初值条件确定系数：

  => Y(t) = Y_st e^(-st) (2.12)

  上式利用t接近零时e^0=1 Taylor近似即得：

  Y~\beta t (2.15)

  Ex2.3, p18

  习题2.3像是常数变易法解了个形如y'＋P(x)·y ＝ Q(x)的一阶常微

  ref:

  --

  常数变易法 - http://www.cnblogs.com/lookof/archive/2009/01/06/1370065.html

  --

  欢迎参加系统生物学导论 - Uri Alon组本页讨论： http://www.douban.com/group/topic/17439561/ (收起)

  2011-02-07 12:11:15   回应

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  While activation/repression graph reveals a qualitative dynamics of simple circuits, further consideration has to be done on separation of time scale of different cellular processes. For stable proteins, especially protein assuming static structural roles, which do not belong to response module to external signals, have a response time similar to that of a cell generation time. (更多)

  While activation/repression graph reveals a qualitative dynamics of simple circuits, further consideration has to be done on separation of time scale of different cellular processes. For stable proteins, especially protein assuming static structural roles, which do not belong to response module to external signals, have a response time similar to that of a cell generation time. (收起)

  2011-02-22 18:29:39   回应

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  Evolution can be fast. Mutations can occur at a comparatively high rate. A single bacterium in 10ml culture grows and divides to reach saturation of 10^10 cells in less than a day. Since mutation rate is 10^(-9) per base-pair per generation, up to saturation, the bacterial population reach 10 mutation per base-pair. If we treat mutation as randomized force, which abolishes edges in gene regula... (更多)

  Evolution can be fast. Mutations can occur at a comparatively high rate. A single bacterium in 10ml culture grows and divides to reach saturation of 10^10 cells in less than a day. Since mutation rate is 10^(-9) per base-pair per generation, up to saturation, the bacterial population reach 10 mutation per base-pair.

  If we treat mutation as randomized force, which abolishes edges in gene regulatory graph, evolutionarily conserved edges preserve fitness to the organism.

  Autoregulation, including positive and negative autoregulations, are over-represented in E.coli transcription network. Parameters of such autoregulation are under natural selection.

  Negative repression efficiently and robustly lock the gene into steady-state expression. Presence of such repression reduces cell-cell variation.

  Positive autoregulation is slower than unregulated gene. Such slow dynamics can be useful in relatively long developmental processes. (收起)

  2011-02-26 03:21:04   回应

  收藏

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• 

  第40页

  [已注销]

  Evolution can be fast. Mutations can occur at a comparatively high rate. A single bacterium in 10ml culture grows and divides to reach saturation of 10^10 cells in less than a day. Since mutation rate is 10^(-9) per base-pair per generation, up to saturation, the bacterial population reach 10 mutation per base-pair. If we treat mutation as randomized force, which abolishes edges in gene regula... (更多)

  Evolution can be fast. Mutations can occur at a comparatively high rate. A single bacterium in 10ml culture grows and divides to reach saturation of 10^10 cells in less than a day. Since mutation rate is 10^(-9) per base-pair per generation, up to saturation, the bacterial population reach 10 mutation per base-pair.

  If we treat mutation as randomized force, which abolishes edges in gene regulatory graph, evolutionarily conserved edges preserve fitness to the organism.

  Autoregulation, including positive and negative autoregulations, are over-represented in E.coli transcription network. Parameters of such autoregulation are under natural selection.

  Negative repression efficiently and robustly lock the gene into steady-state expression. Presence of such repression reduces cell-cell variation.

  Positive autoregulation is slower than unregulated gene. Such slow dynamics can be useful in relatively long developmental processes. (收起)

  2011-02-26 03:21:04   回应

  收藏

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• 

  第22页

  [已注销]

  While activation/repression graph reveals a qualitative dynamics of simple circuits, further consideration has to be done on separation of time scale of different cellular processes. For stable proteins, especially protein assuming static structural roles, which do not belong to response module to external signals, have a response time similar to that of a cell generation time. (更多)

  While activation/repression graph reveals a qualitative dynamics of simple circuits, further consideration has to be done on separation of time scale of different cellular processes. For stable proteins, especially protein assuming static structural roles, which do not belong to response module to external signals, have a response time similar to that of a cell generation time. (收起)

  2011-02-22 18:29:39   回应

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• 

  第260页

  [已注销]

  Appendix C - Graph Properties of Transcription Networks sparse connectivity: Transcription networks are sparse, since ratio of existing edges and theoretical upper limit of edges << 1. Typically, less than 0.1% of possible edges are found in the network. degree distribution: Typically mean degree is 2 to 10 edges/node. Out-degree distribution can be approximately describ... (更多)

  Appendix C - Graph Properties of Transcription Networks

  sparse connectivity:

  Transcription networks are sparse, since ratio of existing edges and theoretical upper limit of edges << 1. Typically, less than 0.1% of possible edges are found in the network.

  degree distribution:

  Typically mean degree is 2 to 10 edges/node.

  Out-degree distribution can be approximately described as a power law, i.e. P(k) ~ k^(-r), with r ~ 1 to 2. Transcription network often have many transcription factors regulate a few genes, fewer nodes that regulate tens of genes, and even fewer global regulators that regulate hundreds of genes. Global regulators usually respond to key environmental signal to control large ensembles of genes, e.g. bacteria CRP to glucose starvation, RpoS respond to general stress.

  In-degree distribution instead resembles compact distribution such as Poisson distribution. This may correspond in part to physical limitation that promoters are short in simple organisms. In more complex organisms, DNA looping action can increase the number of input transcription factors to a given gene. Higher organisms often display larger in-degrees than microorganisms, accommodating the complex computational needed during development.

  motifs:

  Clustering coefficient C is the average number of triangles that a node participates in.

  Feed-forward loop (a type of triangle) is generally overrepresented in motifs of sensory transcription networks. The major contribution to the clustering coefficient therefore stems from feed-forward loops. This pattern appears to be selected due to its function, such as filtering and response acceleration.

  Generally, it appears that global statistical properties of biological network such as degree sequences and cluster sequences are the result of selection working on the detailed circuit patterns in each individual system. Different networks have different selection constraints, which must be understood in order to understand their graphical properties.

  modularity:

  Network modularity is the degree to which it can be separated into nearly independent sub-networks.

  A measure of modularity is proposed by Newman and Girvan, 2004 and 2005. The rationale of this modularity measure is that: a good partition of a network into modules must comprise many within-modular edges and as few as possible between-module edges. This is achieved by maximizing a modularity measure Q. (收起)

  2011-02-13 06:54:47   4回应

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