56:198:582 Biological Networks Lecture 10

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1 56:198:582 Biological Networks Lecture 10

2 Temporal Programs and the Global Structure

3 The single-input module (SIM) network motif The network motifs we have studied so far all had a defined number of nodes. We now look for larger motifs In the single-input module (SIM) network motif, a master transcription factor controls a group of target genes, Z 1, Z 2,..., Z n Each of the target genes in the SIM has only one input and no other transcription factor regulates any of the genes The regulation signs (activation/repression) are the same for all genes in the SIM The master transcription factor is usually autoregulatory

4 The single-input module (SIM) network motif Fig 5.1 The single input module (SIM) network motif. Transcription factor regulates a group of genes Z 1,.. Z n, with no additional transcription factor inputs. usually regulates it self. An example of a SIM, the argninine biosynthesis pathway (in this system, all regulations are repression).

5 The single-input module (SIM) network motif The SIMs are a family of structures with a free parameter: the number of target genes n They are a strong network motif when compared to random networks The most important task of a SIM is to control a group of genes according to the signal sensed by the master regulator The genes in a SIM always have a common biological function (e.g., SIMs often regulate genes that participate in a specific metabolic pathway) Other SIMs control groups of genes that respond to a specific stress (DNA damage, heat shock, etc.) SIMs can control groups of genes that together make up a protein machine (such as a ribosome)

6 The single-input module (SIM) network motif Fig 5.2 A single-input module (SIM) regulating a three-step metabolic pathway. The master repressor R represses a group of genes that encode for enzymes, E1,E2 E3 (each on a different operon). These enzymes catalyze the conversion of substrate S0 to S1 to S2 culminating in the product S3. The product S3 binds to R, and increases the probability that R is in its active state R*, in which it binds the promoters to repress the production of enzymes. This closes a negative feedback loop, where high levels of S3 lead to a reduction in its rate of production. R Gene E1 Gene E2 Gene E3 S0 E1 S1 E2 S2 E3 S3 product

7 SIMs can generate temporal expression programs The SIM can generate temporal programs of expression, in which genes are activated one by one in a defined order A simple mechanism for this temporal order is based on different thresholds of for each of the target genes Z i When activity changes gradually in time, it crosses the thresholds K i at different times, and the genes are turned ON or OFF in a specified order When activity increases, it first activates the gene with the lowest threshold, then it activates the gene with the next lowest threshold, etc. When activity goes down, the genes are affected in reverse order The first gene activated is the last one to be deactivated last-in first-out (LIFO) order We will discuss later how first-in first-out (FIFO) order can be achieved

8 SIMs can generate temporal expression programs Fig 5.3 The SIM can generate temporal programs of expression. As the activity of gradually rises, it crosses the different thresholds for each target promoter in a defined order. When activity declines, it crosses the thresholds in reverse order (last-in-first out or LIFO order). Source: shen-orr nature genetics 2002

9 SIMs can generate temporal expression programs The faster the changes in the activity of, the more rapidly it crosses the different thresholds, and the smaller the delay between the genes There is often an asymmetry in the naturally occurring dynamics of the regulator activity, e.g., sometimes turn-on is fast, but turn-off is gradual In such cases, temporal order will be more pronounced in the slow phase of the transcription factor activity profile Typical delays are on the order of 0.1 generations between genes (about 5 to 10 minutes) In E. coli metabolic pathways, the earlier the protein functions in the pathway, the earlier its gene is activated

10 SIMs can generate temporal expression programs Zaslaver et al Nature genetics 2004 Fig 5.4 Temporal order in arginine biosynthesis system with minutes between genes. Colored bars show expression from the promoters of the different operons in the system, measured by means of a luminescent reporter gene. The position of each gene product in the pathways that produce arginine is shown.

11 Topological generalizations of network motifs For larger motifs, one can define topological generalizations of motifs Consider the feed-forward loop (FFL) with nodes,, and Z We can consider generalizations obtained by choosing a node, say, and duplicating it along with all its edges This results in a double-input FFL. This can be repeated to obtain multi-input FFL generalizations Two other simple topological generalizations of the FFL are obtained by replicating the appropriate nodes called multi- and multi-output FFLs Only one of these generalizations is actually a motif in transcription networks: the multi-output FFL

12 Topological generalizations of network motifs Fig 5.6: Simple topological generalizations of the FFL. Each topological generalization corresponds to duplicating one of the nodes of the FFL and all of its edges. (a) The FFL, (b) generalizations based on duplicating one node ( c) multi-node generalizations. Source: Kashtan et al, PRE 2004.

13 The multi-output FFL can generate FIFO temporal order We examine the multi-output FFL motif in the gene system that controls the production of flagella, E. coli s outboard motors When E. coli is in a comfortable environment with abundant nutrients, it divides and does not try to move When conditions become worse, it makes a decision to growth several nanometer-size motors attached to helical flagella (propellers), which allow it to swim It has a chemotaxis system to decide where to go (to be discussed in a later lecture)

14 The multi-output FFL can generate FIFO temporal order The flagella motor is a 50-nm device built of about 30 types of protein The motor is electrical, converting the energy of protons moving through the motor to drive rotation at about 100 Hz The motor rotates the flagellum, a long helical filament, about 10 times longer than the cell Flagella rotation pushes the cell forward at speeds that can exceed 30 microns/sec The motor is assembled in stages The motor and flagellum have a hollow central tube through which the proteins move to assemble each stage

15 The multi-output FFL can generate FIFO temporal order Fig 5.7: The flagella motor of E. coli and its assembly steps info.bio.cmu.edu/.../ FlagellaMotor.html Annual Review of Microbiology ςολ. 57: (2003) (δοι: /αννυρεϖ.µιχρο ) ΗΟΩ ΒΑΧΤΕΡΙΑ ΑΣΣΕΜΒΛΕ ΦΛΑΓΕΛΛΑ Ροβερτ Μ. Μαχναβ

16 The multi-output FFL can generate FIFO temporal order The proteins that build up the flagella motor are encoded by genes arranged in six operons The flagella motor operons are regulated by two transcription factors, both activators, and The master regulator activates, and both jointly activate each of the six operons, Z 1, Z 2,..., Z 6 This is a multi-output FFL Each operon can be activated by in the absence of and by in the absence of. Thus the input functions are similar to OR gates

17 The multi-output FFL can generate FIFO temporal order Fig 5.8: Schematic plan of the multi-output FFL that regulates the flagella motor genes. Shown are the logic gates at each promoter, and the activation thresholds. =flhdc, =flia, Z 1 =flil, Z 2 =flie etc. K xy K 1 K 2 K n K 1 K 2 K n OR OR OR Z 1 Z 2 Z n

18 The multi-output FFL can generate FIFO temporal order Experiments show that the six flagella operons have a defined temporal order of expression The Z genes get turned ON one by one, with about 0.1 cell generations between them The order in which the operons are turned on is about the same as the order in which the proteins participate in the assembly of the motor In contrast to the SIM, the flagella turn-off order is the same as the turn-on order

19 The multi-output FFL can generate FIFO temporal order 5.9: Temporal order in the flagella system of E. coli. Colored bars are the normalized expression of each promoter, where blue is low and red is high expression. Expression was measured by means of green fluorescent reporter gene. The temporal order matches the assembly order of the flagella, in which proteins are added going from the intra-cellular to the extra-cellular sides. Source: Kalir etal Science 2001

20 The multi-output FFL can generate FIFO temporal order How can FIFO order be achieved by the multi-output FFL? alone is sufficient to turn the genes on. Therefore, turn-on order is determined by the times when crosses the activation thresholds for the promoters Z 1, Z 2,..., Z n The thresholds are K 1, K 2,..., K 6. The promoter with the lowest K is turned on first, and the one with the highest K is turned on last When production of stops, is still around. Its production only stops when decays below the threshold of activation of the promoter, K Thereafter, gradually decays Since is still present after levels have decayed, the turn-off order is governed by, which has its own thresholds for each of the genes, K 1, K 2,..., K n

21 The multi-output FFL can generate FIFO temporal order K2 K 1 K 1 K 2 K 1 K 1 K 2 K 2 Z 1 Z 2 K 1 <K 2 K 1 >K 2 time Fig 5.10: First-in First out order (FIFO) in the multi-z FFL with OR-logic input functions. The output genes Z 1 and Z 2 are turned on when crosses Activation thresholds K 1 and K 2 (dashed lines). The genes Are turned off when decays below activation thresholds K 1 and K 2. When the order of K 1 and K 2 is opposite to that of K 1 and K 2, FIFO order is obtained.

22 The multi-output FFL can generate FIFO temporal order In addition to generating a FIFO temporal order, the multi-output FFL also conveys all the functions of the feed-forward loop Each of the output nodes benefits from the sign-sensitive filter property of the FFL In the flagella system, the FFL functions as a device that delays the deactivation of the Z genes following the loss of activity (OR-gate C1-FFL) This FFL filters away brief OFF pulses of deactivation only when activity is gone for a persistent length of time Flagella production only stops when the appropriate conditions have been sensed for a persistent period of time

23 Bi-fans and dense overlapping regulons Our fourth and final network motif stems from the analysis of four-node patterns There are 199 possible four-node patterns with directed edges Only two are significant motifs in known sensory transcription networks 1. Two-output FFL, which belongs to the family of multi-output FFLs 2. The bi-fan, an overlapping regulation pattern

24 Bi-fans and dense overlapping regulons Fig 5.11: The 4-node network motifs in sensory transcription networks. 1 2 Z 1 Z 2 Z 2 Z 1 Bifan Two-output Feed-forward loop

25 Bi-fans and dense overlapping regulons The bi-fan gives rise to a family of motif generalizations called dense overlapping regulons (DORs) (a regulon is the set of genes regulated by a given transcription factor) The DOR is a row of input transcription factors that regulate a set of output genes in a densely overlapping way DORs are usually not fully wired not every input regulates every output The wiring is, however, much denser than the patterns found in randomized networks The function of the DOR depends on the multi-input that integrates the inputs at the promoter of each gene

26 Bi-fans and dense overlapping regulons Fig 5.12 The main five-node network motifs in the transcription network of E. coli. The bi-fan generalizes bifan to larger patterns with a row of inputs and a row of outputs. Two-output FFL

27 Bi-fans and dense overlapping regulons Fig 5.13 The Dense-overlapping regulons (DOR) network motif, and an example in the E. coli stress response and stationary phase system. Source: Shen-Orr, R Milo, S Mangan & U Alon, Nature Genetics, 31:64-68 (2002).

28 Global structure of sensory transcription networks Network motifs can help produce a slightly less complicated image of complex sensory transcription networks Sensory transcription networks such as those of E. coli and yeast are made of a layer of DORs The DORs form a single layer they do not form cascades. There is no DOR at the output of another DOR

29 Global structure of sensory transcription networks Fig 5.14 The global structure of part of the E. coli transcription network. Ellipses represent transcription factors that read the signals from the environment. Circles are output genes and operons. Rectangles are DORs. Triangles are outputs of single- or multi-output FFLs. Squares are outputs of SIMs. Blue and red lines correspond to activation and repression interactions.

30 Global structure of sensory transcription networks Sensory transcription networks contain very few long cascades of transcription interactions, Z.... Most genes are regulated just one step away from their transcription factor inputs Long cascades would typically be far too slow for sensory transcription networks that need to respond quickly to environmental stresses and nutrients Cascades do occur in other biological networks whose interactions are rapid with respect to the timescale on which the network needs to function (e.g., development transcription networks that control slow developmental processes and signal transduction networks whose components are fast)

31 Summary of sensory transcription networks Fig 5.15 Network Motifs in sensory transcription networks Negative Auto-regulation speeds response time, steady-state robust to fluctuations in production Chapter 3 Positive Auto-regulation Coherent Feed-forward loop C1- FFL Z Slows response time Possible bi-stability Chapter Sign-sensitive delay Filters out brief ON (OFF) input pulses when the Z-input function Is AND (OR) logic. Chapter Incoherent Feed-forward loop Pulse generation Signs-sensitive Response acceleration Chapter 4.7 I1-FFL Z

32 Summary of sensory transcription networks Fig 5.15 cont: Network Motifs in Sensory transcription networks Single- Input Module (SIM) Multi-output Feed-forward loop (multi-output FFL) Z n Z 2 Z n Coordinated control Temporal (LIFO) order of Promoter activity Acts as FFL for each input (sign-sensitive delay, etc) FIFO temporal order of promoter activity Chapter Chapter 5.5 Bifan Dense overlapping Regulons) DOR( n 1 2 m Combinatorial logic Chapter based on multiple inputs, depends on 5.6 Input-function of each gene

33 Network Motifs in Developmental Transcription Networks

34 Developmental Transcription Networks The transcription networks we have studied so far are built to sense and respond to environmental changes. They are called sensory transcription networks In all multi-cellular organisms and in many microorganisms, cells undergo differentiation processes: they can change into other cell types Multicelled organisms begin life as a single celled egg, which divides to form the diverse cell types of the body Differentiation processes are governed by transcription networks known as developmental transcription networks Developmental transcription networks display most of the network motifs we have described in sensory networks. They also display a few additional network motifs not commonly found in sensory transcription networks

35 Two-node positive feedback loops Developmental networks display a network motif in which two transcription factors regulate each other The regulation signs of the two interactions usually lead to positive feedback loops There are two types of positive feedback loops with two transcription factors: 1. Two positive interactions. Two stable steady states: either and are both ON or and are both OFF A signal that causes or to be produced irreversibly locks the system into a state where both and are ON Most useful when genes regulated by and genes regulated by encode proteins that belong to the same tissue 2. Two negative interactions. Two stable steady states: either is ON and is OFF or is OFF and is ON. Most useful when genes regulated by and genes regulated by encode proteins that belong to different cell fates

36 Two-node positive feedback loops Double-positive feedback loop Double-negative feedback loop Z Z Z Z Steady-State 1 ON ON ON Steady-State 1 ON OFF OFF Steady-State 2 OFF OFF OFF Steady-State 2 OFF ON ON Fig 6.1 Positive transcriptional feedback loops with two nodes. The double positive loop has two activation interactions, and the double negative is made of two repression interactions. An output gene Z is regulated as shown. Each of the feedback loops has two steady states: both and genes ON or OFF in the double-positive loop, and either ON in the double-negative loop.

37 Regulating feedback Two-node feedback loops can appear within larger motifs in developmental networks There are two main three-node motifs that contain feedback loops The first is a triangle pattern in which the mutually regulating nodes and both regulate a gene Z

38 Regulating feedback Fig 6.2: a) Two-node feedback loops with auto-regulation are a common network motif In developmental transcription networks. b) The ten distinct types of regulating feedback motifs, each corresponding to a different combination of regulation signs. Z Z Z Z Z Z Z Z Z Z a) b)

39 Regulated feedback The other main three-node motif containing feedback loops is one in which a two-node feedback loop is regulated by an upstream transcription factor The regulated feedback motif is a memory element: the regulator Z can switch the feedback loop from one state to another, such that the state persists even after Z is deactivated

40 Regulated feedback a) Z b) Z Z* Z* memory memory time time 6.3 The regulated-feedback network motif in developmental transcription networks. (a) Double positive feedback loop. When Z is activated, and begin to be produced They can remain locked ON even when Z is deactivated (at times after the dashed line). (b) Double negative feedback loop. Here Z acts to switch the steady states. Initially is high and represses. After Z is activated, is produced and is repressed. This state can persist even after Z is deactivated. Thus, in both a and b, the feedback Implements a memory.

41 Long transcription cascades a) b) Z Z Kxy Kyz Z Z Cell generations Cell generations Fig 6.4 Transcription cascades can generate delays on the order of the cell-generation time (in the case of stable proteins). Each step in the cascade activates or represses the next step when it crosses its threshold (dashed lines). Shown are a cascade of activators and a cascade of repressors.

42 Interlocked feed-forward loops in the B. subtilis sporulation networks In developmental networks, the FFL often form parts of larger and more complex circuits than in sensory transcription networks We discuss a well-mapped developmental network mode of interlocking FFLs that governs differentiation in the bacterium Bacillus subtilis When starved, B. subtilis cells stop dividing and differentiate into durable spores. When placed in the right condition the spore converts itself again into a normal bacterium When B. subtilis makes a spore, it uses hundreds of genes that are turned ON and OFF in a series of temporal waves, each carrying out specific stages in the formation of the spore The network that regulates sporulation is made of several transcription factors arranged in linked coherent and incoherent type-1 FFLs

43 Interlocked feed-forward loops in the B. subtilis sporulation networks 1 Z Z 1 Z 2 Z 3 1 AND AND Z time AND Z 2 AND Z 3 Fig 6.5: The transcription network guiding development of the B. subtilis spore. Z 1, Z 2 and Z 3 represent groups of tens to hundreds of genes. This network is made of two type-1 incoherent FFLs, that generate pulses of Z 1 and Z 2, and two type-1 coherent FFLs, one of which generates a delayed step of Z 3. Based on R. Losick, PLOS 2004

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