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Readthrough has already been used to implement transcriptional NOR gates, wherein tandem input promoters express a repressor that represses an output promoter. (First time I recall here: 21150903, used widely here: 27034378, roadblocking modeled here: 32141239.) An advantage of this design is that the repressor need only be encoded in DNA once, which can ...


6

The recurrent patterns of connections in a network are known as network motifs. You can check this paper out. They have identified common network motifs in different types of real networks including neural networks (of C.elegans though). Apart from feed forward loops, bi-fan is also a common network motif in neural networks. Perhaps the connectome ...


6

There is no single answer, because networks (or graphs, as they are called in discrete mathematics) are flexible tools that can be used to model all sorts of relationships between genes, transcripts, proteins, or other entities in biology. (And networks are useful models in many other disciplines too, like sociology.) Depending on the type of network you're ...


3

Gene expression can be very well described by Hill functions, i.e.: $$c_Y(c_X) = \frac{c_X^n}{1+c_X^n}$$ when $X$ activates $Y$ and $$c_Y(c_X) = \frac{1}{1+c_X^n}$$ when $X$ represses $Y$ (omitting units and all sorts of constants for simplicity). For the common case that $n>1$, these functions look like this: As you can see, they are far from linear, ...


3

I think this is an interesting proposal to try to turn a problem into a benefit. What you are considering is conceptually similar to the organization of an operon, in which multiple genes are controlled by a single promoter, with multiple ribosome binding site (RBS) entry points for translation, and read-through can certainly occur in these cases too. ...


3

Epidemiology, the spreading of diseases, is probably one of the most famous applications of network theory in biology, going all the way back to John Snow. A more relevant example would maybe be something like graph models of habitat mosaics. Populations are often both spread out over landscapes and clumped together in smaller pockets of suitable habitats. ...


3

It means the software has 4753 genes it has labeled with "biological regulation" and the short list you gave has 45 of those genes. That is a little higher than you might expect by chance if you'd given a random gene list of the same size.


3

While this does look like a very nice approach, I would actually argue that there are (almost) no biological datasets, that can be directly used for your algorithm - you can only do a sort of meta analysis using results/predictions based on other datasets. The reason for this is that the nature of a signed graph requires you to look at effects that can have ...


3

Ok, let's talk about mammalian neocortex rather than about the entire central nervous system. The vast majority of synapses within the cortex are formed between neurons within the same cortical area (Binzegger et al 2004). Although most of these synapses will not be self-connections (from a single neuron back to itself), they are recurrent in the sense that ...


2

Others have nicely summarized to different types of biological networks and how they can evolve. I would like to make a fine distinction between evolution and dynamic adaptation and add some comments about relative evolvabilities of different biological networks. Given an architecture, a network can respond dynamically to different inputs. The preferred ...


2

Sun and moon-faking devices exist. Not sure about 'biosphere'. I have personally observed one for honeybees, but that one was fairly crude(bees are dumb). It was just a big room with a hive stand in the middle and a series of fluorescent lights on the ceiling that turned on one after the other. The goal was to mess with bee circadian rhythms and see which ...


2

This is a great biological question! It asks a lot about how empirical science is done in the field of modern biology. I'm glad we encourage such questions from curious people who want to learn more. Here's some directed network graph data for regulatory networks of transcription factors (TFs), via http://www.regulatorynetworks.org/: • Mouse TF: http://www....


2

I am assuming that in your model, the reactants and products (species) are metabolites and each reaction denotes conversion of one metabolite to another. From transcriptomics, you will get the relative expression levels of different genes. When you have two samples from different conditions you can calculate the differential expression. A model can be ...


1

To add to gaspanic's answer, not all terminators work equally well under all conditions. So if terminators weren't characterized in the organism or tissue you are working in the efficiency might be different. Additionally, though rarely people might be looking to design with a leaky terminator mimicking bacterial use of leaky terminators to use one DNA ...


1

Just to expand on @Wrzlprmft's great answer with a concrete example: Sticking to your simple gene circuit with $X$, $Y$, and $Z$, now consider the possibility that $Y$ also activates its own expression via a positive feedback loop: $X \to \underset{\circlearrowright}Y \to Z$ Here activation of $Y$ will be only minimally dependent on $X$. Once $Y$ is ...


1

There's quite a bit of use of graph theoretic approaches in gene regulation, so I'd suggest you start there and Stand on the Shoulders of Giants rather than reinventing gravity: https://scholar.google.com/scholar?hl=en&q=graph+theoretic+gene+regulation Although this isn't my area of expertise, I think the thing most obvious to me as a biologist and ...


1

Enrichr has a huge (164 libraries) collection of term-to-gene relations in machine-readable format. X2K can be more interesting for you, as it partially implements networks approach. It has a large collection of libraries as well. And two more - KEA3 (kinases to genes), and ChEA3 (transcription factors to genes).


1

A bit of a preface: A lot of this large scale collaboration (which I would maybe rather describe as the work of a whole [sub]field over a given time frame) often ends up in some sort of database. Really, there A LOT of those in modern biology - and often with minor but sometimes important differences. One of those would be the level of curation: are results ...


1

I am not 100% sure that I understand the question, but I am going to try to answer, based on the following assumptions: The "number of 3 and 4 node motifs" is not very clear. If I understand correctly, it should be a quantity determined in large part by the degree distribution. You could rewire your network to lose all information about the TRN other than ...


1

As I understand it, "physical" and "functional" are just two different ways of analyzing an interaction between proteins. Proteins can and do interact physically, meaning they can bind in specific ways and sites, and this binding can produce changes in those same proteins (like conformational changes), which alter their properties. This is the purely ...


1

It depends on the PPI network. Some, like DIP are exclusively experimental, depending on high-throughput robotic AP/MS, Tandem Affinity Purification, Y2H, cross-linking with formaldehyde, clever things involving half-fluorophores, etc. There are a lot of different methods, is what I'm saying. Y2H has a high false-positive rate, immunoprecipitation is ...


1

This seems to me to be two independent pieces of data. mRNA seq allows one (in case of linear amplification) to quantify message RNA transcripts of genes of interest. that is, how many copies of mRNA for given gene are in the cell at the moment. It is ultimately, how active this gene is. In neurons different genes will be more active, than in epithelial ...


1

Ants, slime molds, and brains. Ants and slime molds use simple rules to generate pretty good transportation networks in an emergent way, and brains wire and rewire themselves constantly(adding/removing edges, but not usually nodes). Evolutionary networks, metabolic networks, and ecological networks are much harder to get concrete data sets from, because ...


1

I find this a very interesting question as I personally work with networks very frequently! Based on your definition of evolving networks, it is feasible to consider protein-protein interaction networks as evolving since over time more and more interactions between different proteins are discovered and more novel proteins (nodes) are tested for their ...


1

The following does not answer the question! It only gives some ideas of where I personnaly found some work involving network analysis in biology. Most of the networks I've heard about in biology concern network of species interactions network of individual interactions within a population network of subpopulation interactions within a metapopulation ...


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Not sure if this meets your definition of a network, but there are several kinase cascades which transmit signals. For example, the basic MAPK cascade has evolved to serve different roles via the ERK, JNK, and p38 cascades The evolution of the MAP kinase pathways: coduplication of interacting proteins leads to new signaling cascades. Caffrey et al., Journal ...


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