Systems Biology
MOTIF: Functional Unit of An Interaction Network
In a network, integration of elements and interacting components enables identification of conserved modules and
motifs. The topological analysis, however, reveals much about the nature and functions of a network and provides sufficient statistics for any further study. Supported by several data types viz interaction data, expression data, Boolean data, and raw sequence data, modules and motifs provide an easy way to understand the specific function of a gene and protein. Basically, network motifs are characteristic network patterns comprising of both transcription regulation and protein-protein interaction that recur more often than in a random network.
The idea of network motif (sub-graph) was presented by Uri Alon and his group in 2002 [1] as they were discovered in a gene regulation network of E. coli and then in a large set of a neural network. According to their occurrence and behavior in a network, “motifs are subgraph recurring repeatedly, defined by a particular pattern of interaction between vertices that reflect a framework in which particular functions are achieved”. They are of vital importance largely because they may display functional properties and may also provide deep insight into network’s functional abilities. Significant studies have been done from perspective of the biological application as well as computational theory. Biological analysis mainly endeavors to interpret the functions of network motifs associated with genetic regulation as the first motif was found in the transcription unit of E. coli as well as Yeast and other higher organisms. Apart from those of genetic regulation, some distinct motifs were also discovered from the neural network and protein interaction network (fig-1).
Fig: 1. Different types of motifs in the biological network. (courtesy: Google image)
Statically a motif is identified as a pattern that occurs at least five times and is more significant than in a random network. With only two or at least three nodes, we may randomize to get a maximum pattern in a network. It is up to analyses that one has to perform. Patterns with two, three, four and five nodes are significant as their occurrence is more frequent in a network than any other pattern. Based on directivity, connectivity, pattern, regulation, and the number of nodes, they are classified into various categories as below:
1. Negative auto-regulation (NAR)
One of the simplest and most abundant network motifs is negative autoregulation in which a transcription factor represses its own transcription (fig. 2-a). Its generalized function is in response regulation and SOS DNA repair system response. NAR was observed to speed-up the response to signals in a synthetic transcription network. It also increases the stability of the auto-regulated gene product concentration against stochastic noises, reducing variations in protein levels between different cells.
Fig: 2. Different types of loops and motif in biological networks; a. auto-regulation, b. feedforward motif, c. coherent and incoherent loops, d. different types of patterns (motifs), commonly occurring in biological networks. They all occur in almost every biological system and represent a specific regulatory functional unit. (Courtesy- Google Images)
2. Positive auto-regulation (PAR)
It is characterized by the enhancement of transcription by its own gene product (fig 2-a). Comparatively, it shows a slower response than NAR. In a case where rapid regulation is required, PAR leads to a bimodal distribution of protein levels in cell populations.
3. Feed-forward loops (FFL)
This motif commonly occurs in many genetic regulatory networks and consists of three genes and three regulatory interactions (fig 2-b). In the diagram, target gene C is regulated by 2 TFs (transcription factor) A and B and in addition TF B is also regulated by TF A. Since each of the regulatory interaction may either be positive or negative, there are eight possible types of FFL motifs. Computationally, in most of the cases, FFL represent an AND & OR gate but other circuitry inputs are also possible.
4. Coherent type 1 FFL (C1-FFL)
This is one of the sub-types of FFL, characterized by giving a pulse filtration in which a short pulse of a signal will not generate a response but a persistent response will generate a short delay. Importantly, the signal response is fastened after one shut off. Such a vital mode of signal transduction in genetic or cellular regulatory system is observed in metabolic pathways and protein-gene interaction network.
5. Incoherent type 1 FFL (I1-FFL)
It is known to be a pulse generator and response accelerator. Two signal pathways function in two opposite ways, one signal activates and the other represses. After repression, a pulse dynamics is generated. Importantly, it speeds up the activation of any gene, not necessarily a gene of a transcription factor. Feedforward regulation shows better regulation than negative feedback.
6. Multi-output FFLs
The same regulator controls (regulates) multiple genes of the same system.
7. Single-input modules (SIM)
This motif occurs when a single gene regulates a single set of a gene with no additional regulation. This is significant when genes are carrying out a specific function and therefore need to be activated in the synchronized manner.
A possible confirmation of motif importance is motif conservation. In evolution conservation implies importance. The conservation of a protein in the network may be taken as an indication of the biological importance of that motif. The conservation of a motif shows the evolutionary pressure that can be followed to find ortholog in other organism. Wuchty et al, 2003 [2] tested this hypothesis for the correlation between the protein evolutionary rate and the structure of the motif it is embedded in. The conservation of motif component was found to be tens to thousands of times higher than expected at random, suggesting conservation of motif constituents. Motifs representing small functional unit or sub-graph in a network are found using different software like M-finder (http://www.weizmann.ac.il/mcb/Uri Alon/groupNetworkMotifSW.htm), MODIS, FANMOD (http://theinf1.informatik.uni-jena.dewernicke/motifs/index.htm), MAVisto (http://mavisto.ipk-gatersleben.de) iGRAPH: (https://cran.r-project.org/package=igraph), HOMER & Motif-X etc. Online tools like Amadeus, Web Motif, and MEME suite are also used for the same purpose.
References:
1. U Alon, Network Motif: theory and experimental approaches, Nature Reviews Genetics 8, 450-461,(June 2007) doi :10.1038
2. Wuchty S, et al. (2003) Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat Genet 35(2):176-92.
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In the previous section, mathematical modeling was exemplified by metabolic process and its biochemical regulation. It could also be done by signalling pathways and genetic regulatory process. At all cellular phase, one observe changing mode of a cell with effect from environmental factors. It is quite difficult to maintain cellular functions and reach to steady state. Thus, one needs to fix a range of parameters for all molecular reactions while going for mathematical modeling.
Derivation of Mathematical Equations for Understanding Systems Behaviour:
Depending upon the nature of biological process, it is essential to understand different modeling approach as numbers of methods have been used for different biological systems. Functionally, most of the cellular processes are dynamic that change with environmental change such that the signaling or regulation for specific genes when cell is exposed to an extraordinary medium. In order to describe such time-dependent phenomena it is necessary to choose mathematical equations that can capture these dynamic effects. In other biological systems where cellular products/molecules don’t change over time i.e., concentration remains same, it is not necessary to describe details of underlying dynamics. For modeling the systems behavior, suitable methods have been developed. Among them are two methods, commonly used in modeling of metabolic process, modeling of signaling and regulatory pathways.
The month of November has just arrived with its generic glimpse of winter. We welcome this month with an evergreen and hot topic of cancer research. This time we intend to introduce you to an old research topic with a new vision…..
Cancer being an ailment with no remedy of full confidence has been pursued as a career by a lot of researchers. A cell biologist says it is an uncontrolled proliferation (increase in number by division and growth) of cells, molecular biologists call it a mutant variety of some biomolecules forcing a cell to commit such an uncontrolled cell division cycle. But, how does a Systems Biologist see such kind of a problem? Let us try to pursue it in a different way.
Proteins if are not assigned some name based on their function or structure, scientists mark them according to their molecular weight, e.g. p53, p200, p19 etc. Scientists have proven an abnormally high expression of p53 protein in Cancerous cells/tissues. p53 protein is actually the reason behind those other proteins which regulate the cell cycle and makes it to divide in to two as a normal scenario, p53 also helps in the manufacture of its inhibitor named Mdm2 protein. In any case of mutation in p53, that leads the failure of abnormality recognition by p53, doesn’t lead to increase in p53 and consequently Mdm2, p21 and other p53 regulated proteins. And thus, the division of abnormal cells continues indefinitely and causes Cancer.
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So, in a normal cell, when p53 senses the danger and signals the Cell by increasing p21 to combine with PCNA (Proliferating Cell Nuclear Antigen – An enzyme that helps in cell division) it stops the cell division. This type of cell cycle division has been shown in one of the diagrams mentioned below, while for the mutated case of p53 where it can not sense the cellular damage and thus divides normally is also shown in one of the images above.
We have also mentioned a combined picture, which shows a referral of how different stages of Mathematical Biology looks like. These figures are in special contrast to Cancer cells and normal cells.
Reference: Alam MJ, Kumar S, Singh V, Singh RKB (2015) Bifurcation in Cell Cycle Dynamics Regulated by p53. PLoS ONE 10(6): e0129620. doi:10.1371/journal.pone.0129620
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129620
Gathering of Dynamic/Kinetic information
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