Notebook of Small Ideas

The wet-lab machine

2012-02-06T11:26:00.000-08:00

The Incredible Machine is a nice game that is fun and intellectually challenging. For how complex the scenarios are, the interface and visualization make the game relatively simple to learn.

I wonder if an interface like the incredible machine can be used to explain wet-lab procedures. Of course, the minute details would not be illustrated, but one of the issues with wet-lab at present is that there are only protocols. There are no higher-level 'big picture' explanations, which are necessary for a novice or someone who is trying to learn how to do wet-lab.

Perfect class project for teaching science

2011-12-16T09:21:00.000-08:00

Imagine a class project where the students have to find a few failed experiments (or experiments with confusing results) and develop hypothesis on possible explanations. Of course, some of them might be human error, but that is the part of the challenge of the assignment -- to develop reasons why that is the likely explanation.

I don't think this assignment is possible at present. Why? Below are my reasons, although I am sure there are many more.

1) There are a few journals of negative results, but I think they probably capture only a very small fraction of the number of failed experiments [in biological sciences as a whole].

2) EVEN IF all the failed results were published, how would a student navigate through them. Usually, understanding the nature of a failure is far far more difficult that understanding a successful experiment, because the pieces of a successful experiment make a coherent story. For a failed experiment, an expert in the field is often required to hypothesize potential explanations.

3) Failures MAY very often relate to each other. I, personally, think that most failed experiments can be attributed to a combination of human error and actual biological issues. If this is true, then looking at any single failed experiment is meaningless (sort of obvious). Finding "patterns" amongst thousands of failed experiments is practically impossible via the traditional journal-reading process.

Possible solutions (ambitious):

I believe that the human eye is extremely efficient in finding patterns, and therefore, it is necessary to leverage the visual + intuition capabilities of human researchers in order to make sense out of failed results.

A class project means that this task needs to be accomplished by non-experts. I think a framework that combines education + research + visual representation of results can by-pass this problem. In other words, provide ways to educate a student about a method when presenting the results from that method. If such a framework can be created, the boundary between education and research may become vague, which might be quite remarkable.

Implications:

The traditional test-based system for selecting students is bound to miss several great minds who have the potential to solve many unresolved mysteries in science. If an infrastructure can be constructed where anyone can educate themselves and connect research results efficiently, there is a possibility that various geniuses around the world will be able to utilize their potential and service the community. The opportunity to circumvent the traditional test and money-based avenue to higher-education should provide an incentive for students around the world to try to do science on the side.

Leave "pheromone trails" on research articles

2011-12-08T19:43:00.001-08:00

What if there was a way to leave a "trail" (like ants) whenever researchers jump from one research article to another and find the connection very interesting. As more researchers walk the same path, the "pheromone" along that trail becomes stronger. This would provide a nice way of linking interesting research articles together, highlighting interesting patterns in the network of related research papers.

Yet another perspective on evolution

2011-09-13T19:53:00.000-07:00

Consider this possibly true scenario:
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Virus X lives inside 10% of the human population. It has integrated itself into the human genome, as many viruses do. It gets activated during the lifetime of a human and it exists the human body through the mouth and nose It is only able to invade other humans with specific proteins, which happens to be the same 10% of the population. In addition, this virus is also able to invade 10% of dogs and cats.
---
In the above situation, the virus X serves as a channel for DNA exchange. In other words, it is possible to build a "family tree" based on this virus. It is another a family tree based on a different means of reproduction. In normal reproduction, we do not reproduce our entire selves. Rather, some part of us is reproduced. Virus X is also reproducing a small part of us, and therefore, it is just like normal reproduction.

We think of the "tree of life" as a tree with lots of horizontal transfers. What if all these horizontal transfers actually form another tree of life, a tree that uses a different means of reproduction. Why limit to viruses? Microbes in our body are part of us. If microbes are mutating and being carried between species, then they, too, can be a means of reproducing a part of us. In summary, there might be several means by which we reproduce specific aspects of "us". Each of these means of reproduction can be used as a basis for building a different "tree of life". All these trees intersect.

So what is a species? It is basically a way of categorizing individuals. Just like there are different ways to classify books (e.g. language, subject, readers), there are different ways of classifying individuals. Each individual belongs in multiple species -- one for each type of reproduction process that affects the individual. We are all part of more than one species. So why do we give the regular form of reproduction so much importance? It's because of the brain, of course. The brain is not the master of our body. It is one component of the body, and just like any other component, it has roles. The brain is designed to pay attention to certain types of reproduction. For humans, these types of reproduction include sexual reproduction and cultural (meme) reproduction and maybe some other ones. We should not let the role of the brain affect the reality, which is that all of these forms of reproduction contribute significantly to natural evolution.

Therefore, natural evolution is a process where multiple trees of life are expanding and interacting. Individuals sit at the intersection points of these trees.

Crowd science

2011-08-31T16:02:00.000-07:00

First of all, the two problems being addressed...

1) Great ideas are created when several small ideas come together. The
inability to keep up with a wide variety of research will lower the
chances of small ideas coming together. We're having trouble keeping
up with publications in just our own field, so imagine what all useful
discoveries we might be missing from other fields, especially those
obscure journals which might be hiding fragments of a great discovery.

2) Currently, if a curious high school student wants to learn more
about ongoing research, they would have to start reading review papers
or something like that -- this is not very inviting. No wonder the gap
between researchers and public is growing. The current mode of doing research requires a person to go through the PhD 4-6 year ritual. A self-motivated individual does not have sufficient material to become a self-motivated scientist. Even worse, a scientist who wants to cross from one field to another also needs a cross several large barriers.

And now, the solution...

An online game. Imagine a role-playing game where you walk around on
the earth (google earth-like interface with a person walking on it).
As your character walks through regions of the globe, you will see
little signs pop up indicating where the research labs are. There will
also be signs for "schools" (described later). When you click on a
research lab, you see comic strips of up-to-date research -- that's
right, comic strip of what each post-doc, graduate student, and
faculty did that day or any other day. You will also see the data
(graphs, tables, etc.) below each block of the comic strip.

Each comic strip is one line. It starts with a 'mission statement'
(objective or hypothesis) and ends with a concluding statement.
Conclusions can include emoticons, of course. Everything between those
two sentences describes what was was done using pictures and short
descriptions. The comic strips would describe procedures like miniprep
and PCR or even computational steps like parameter fitting. The player
can hover over the comic strip and find "schools" that teach those
concepts. E.g. I would hover over "PCR" step and see several schools
located across the globe that teach what PCR is, with ratings for each
school. Clicking on the schools takes me to online lessons (videos,
etc.) that teach those concepts. Schools with high ratings might even
make money from ads (incentive).

Ok, so now you ask how will these comic strips be generated... with
the Comic Maker of course! Each research lab participating in this
game can download a software called Comic Maker. Comic Maker comes
with hundreds of comic blocks representing some basic procedures. Each comic block can be generates by combining those basic blocks. It will have an easy drag-n-drop interface for creating a pipeline and attaching data to each step of the pipeline. The researcher must start a pipeline using a 'mission statement' and end it using some conclusion, even something as simple as :-( or just a few key words.

More fun stuff: researchers can announce "quests", which are open
problems that they are unable to resolve. Gamers can get involved in
quests. These players have to gather facts from other labs across the
world and generate some solution. They can request the researcher to
perform new experiments for them if they need more data. Similarly,
gamers can create novel hypotheses by collecting results from several
pipelines and present them to researchers.

One key question is: how does this reward the scientists who are in universities? Mainly, the reward is visibility, which is in many cases a big reward. Making research visible is a key for doing good research, and most researchers understand this obvious fact. Of course, placing results in this online game might interfere with the current "publications" approach. The workaround for that problem is simple: just place this in the game after they are published.

That's it. Hopefully that was a fun read. I think it can can be done.
Imagine spending your weekend looking at comics of what everyone is
going at Berkeley instead of going through the procedure section of a
paper. Of course, there will be nice search features, like "find me
everyone who is doing XYZ", where "XYZ" is some sequence of
procedures.

Information vs information carrier

2011-08-19T10:08:00.000-07:00

Suppose I write the word "Mango" using pen and paper. Then, suppose I wrote the same word using a chalk and blackboard. It is obvious that I am conveying the same information. The instrument used to convey that information is hardly relevant.

If this analogy can be applied to biological systems, then it is misleading to study physical aspects of signaling separately. Whether the signaling is via transcription factors, enzymes, RNA molecules, small metabolites, or DNA structure, there may not be any relevance with the content of the information that is being delivered by those molecular interactions. Similarly, between-cell communication may not be related to the type of signaling (paracrine, endocrine, quorum sensing, etc.). It might even be possible that the information encoded by molecular interactions is no different that information encoded by cellular interactions.

In summary, it is possible that information in biological systems might come to light if we study the patterns and ignore the physical components that create those patterns. At the same time, the physical aspects are not completely irrelevant. One would not write a book using chalk and blackboard, so the physical aspects of the instruments do restrict the type of information that can be conveyed.

Cell density based effects

2011-08-10T10:24:00.000-07:00

In videos such as the one below, the cells in the middle have smaller size and therefore, there are more cells per unit area. This means that the concentration of molecules inside those cells might be different from the concentration of molecules in the cells at the outer boundaries. Suppose the cells have multiple stable states; in such cases, concentration differences can trigger state changes, causing different behavior of the cells based on their location in the colony.

Colonies where density is not equally distributed might use this fact (density dependent state change) to create different roles within the colony. Maybe the state of the cells at the center governs certain aspects of the colony and the state of the cells at the edges governs some other aspect of the colony. Even if evolution has not used this observation as a design strategy, it does not limit us from using the density difference as a design strategy for creating diversity within a population.

Unnoticed evolution of humans

2011-06-26T12:11:00.000-07:00

1) Suppose that the microbial community living inside us have a sufficiently long relationship with our own cells such that the microbes have an important role in a large majority of physiological functions, not just digestion.

2) As human societies have evolved, the variety of microbes that live in our environment have undoubtedly changed. Once humans wandered grasslands on bare-foot and ate raw food without even washing them. Now we buy processed food from supermarkets and even walk inside our houses with socks. The result is that the community of microbes that interact with and enter/exit our bodies has changed.

Combining (1) and (2) above leads to the possibility that the human physiology has indeed changed of the past several centuries, even though the change the human phenotype "appears" unchanged. The extent of evolution depends on the extent to which the microbial communities influence human physiology. If there are long-distance signaling molecules that are released by microbes which enter the blood and if the microbes are capable of receiving our signals, then...

Analogy for Protein Bursts

2011-04-22T16:21:00.001-07:00

Consider this scenario:

Imagine a store that is open for a certain time interval during the day. In that time interval, several customers rush in to the store. Each customer buys several items. If someone would record the number of items sold as a function of time, he/she would probably observe bursts (one burst = items bought by one person). The time duration for which the store is open will correspond to the number of bursts. In other words, time duration maps to frequency of bursts.

Now, consider this transcription model:

Consider this mechanistic/intuitive model explaining how proteins are produced in bursts and how the frequency of bursts are controlled by the transcription factor:

1. transcription factor binds to promoter regions and opens the region for access by the polymerase

2. the region remains "open" for some time

3. during this time interval, the polymerase may initiate transcription multiple times

4. for each mRNA that the polymerase transcribes, multiple proteins are produced

So, in summary, the transcription factor "opens" the promoter region for the polymerase. Lets assume that upregulating the transcription factor affects the time duration of the "open" promoter. The longer the time interval, the more mRNA will be produced. Each mRNA creates a burst of proteins. Therefore, upregulating transcription factor affects frequency of bursts.

Stochasticity can lead to Stability

2011-04-22T16:12:00.000-07:00

While we think of "noise" as a destabilizing force, we actually use noise many times to lead a system to the most stable position. Consider the scenario where you want to get all the items out of a hand bag very quickly; you would hold the bag up-side-down and shake, i.e. add noise. If you just tilt the bag up-side-down, there is a risk that some of the items would get stuck in the bag. Shaking ensures that such temporary "traps" (in mathematical terms, local minima) are avoided. In a sense, this is like stochastic optimization. When all the items are on the floor, they will remain there even if you shake the floor, because that is that is the most stable position.

Perhaps stochasticity in natural systems is a means of "shaking", i.e. a means by which natural systems reach the most stable states and avoid local traps. The most stable state remains relatively stable even at the presence of noise. Consider the situation when all the items from the bag are on the floor -- even shaking the floor would not change the situation significantly. Perhaps this is the nature of the "most stable state" -- it has a wide basin and thus can easily tolerate noise.

Centralized synthetic biology for the community

2011-01-21T18:51:00.000-08:00

Vision:

Build a "fun" framework that would attract lots of young minds to ask interesting questions, engineer biological cells to answer those questions, and easily share their results and explanations with each other.

Problem:

1. providing the education to the masses... but I have some hope that young people are quite good are educating themselves, given the motivation

2. Resource

3. Safety

Possible solution:

One or two well maintained centralized robots with a fun interface for entering experiments and storing/visualizing the results. This is very much possible, except that it is expensive. But if the benefit is large enough, it is possible that government and/or companies might solve that issue.

But if such a programmable robot existed as a resource, I can imagine numerous researchers wanting to try new experiments and test new hypotheses. Since the data is centralized, there is a built-in benefit of data-integration, i.e. finding relationships between multiple experiments and drawing conclusions based on large number of experiments. It would be like a Facebook for biological experiments: everyone wants to dump their favorite hypothesis on it.

Exercise as a general solution to living systems

2011-01-20T10:27:00.000-08:00

I heard the statement a couple of times that places in this world with more cleanliness tend to have more auto-immune illnesses such as allergies. One can imagine that the immune system also needs to "exercise" from time to time, just like the muscles in our body or the brain.

What if "exercise" is a more general theme? Perhaps every pathway in a living system needs some form of exercise (but not too much, of course). What if the error-checking mechanisms and all the other anti-cancer mechanisms in our body also need exercise... perhaps we can cure cancer by exercising anti-cancer pathways. Exercise against cancer would be very tricky. It would require producing small controllable (or self-limiting) tumors in our body from time to time. By doing so, the hypothesis is that the body's anti-cancer pathways would remain alert (well exercised). This might make the body better prepared against the real cancer or tumor.

The same theme of exercise can perhaps be applied to achieve other goals, e.g. directed evolution of microbes.

Possible functions to evolve using reaction networks

2010-11-17T19:07:00.001-08:00

How would simulated evolution resolve the following challenges:

1) Measure the variance (noise) of a signal
2) Measure the frequency of a signal -- can be related to (1)
3) Reverse of (1), i.e. increase noise based on deterministic signal (without affecting mean)
4) Reverse of (2), i.e. increase frequency based on amplitude of an input signal
5) Control the width of a bimodal distribution based on a deterministic signal
6) Adapt to an external pattern of events (i.e. learn cause-delay-effect relationships) purely through the reaction network -- this would probably be a combination of signal processing + memory

Design by homologous recombination

2010-06-17T13:10:00.000-07:00

Engineers have frequently used random mutations as a means of optimizing a protein or genetically engineered network. However, I don't think this is an effective optimization or design process. Planned mutations, such as the one used by the immune system, rely on homologous recombination rather than random mutations. Using recombination, we can plan the mutation events, therefore perform a much more predictable optimization. The optimization process can even be simulated computationally.

Cost-based hypothesis for evolution of modules

2010-06-03T15:20:00.000-07:00

Suppose Amazon and eBay want some algorithm that finds common patterns is the customer purchasing history. Suppose a third company is developing exactly such an algorithm. It is cost-effective for Amazon and eBay to outsource this pattern finding problem to this third company, because the third company only charges half of what is required to get the job done. The third company is able to charge half the normal price because it gets payment from both companies. This can be considered evolution of modularity in economics, or niche finding.

The same reasoning might apply to modular structures in biology. It is perhaps easiest to think of an ecosystem first, where each species is like a company. If one species provides a function that is needed by the other species, then the ecosystem depends on that first species. In other words, the ecosystem will probably evolve so that there is a reserved seat for the first species. Now, moving the analogy to population of cells or population of genes within cells is a bit different, but I think some of the analogies still apply. The underlying idea is that the system favors those species that are necessary for the whole system to survive. And the reduction of cost is the incentive for new species to evolve and take a specific role in the system.

Directed evolution using engineered cells

2010-05-17T17:22:00.000-07:00

Directed evolution of cells is generally done by setting up a screening process. An example is a binding assay for evolving cell surface receptors.

It is very difficult to build screening procedures that select for functions. However, it might be possible to engineer "killer" cells that attack cells with particular types of behaviors. Thus the killer cells provide the screening process. And why limit to a single type of killer cell.. of course, the killer cells should not evolve (might be an issue)

Imagine this scenario: a population of cells evolving in an environment with populations of 3 or 4 different types of killer cells. Each killer cell targets a particular type of behavior. Further, another population of "helper" cells excrete specific nutrients in response to particular behaviors. The target population of cells should evolve to avoid specific functions that are targeted by the killer cells and acquire specific functions targeted by the helper cells. Due to the existence of multiple criteria, the evolution might be more gradual as well.

Signaling - specifity and decoding

2010-05-17T17:15:00.000-07:00

Lets consider wireless signals. The signals themselves travel in all directions, so there is no specificity. The frequency provides the sender-receiver specificity. The signal pattern contains information that the receiver can decode, i.e. the receiver must expect a specific type of pattern.

Comparing the general idea to biological signaling... the specificity usually comes from binding affinity, so that aspect of signaling is clear. Now for decoding the information. A pathway probably has multiple molecules serving as signal carriers. The pattern of concentrations of those input molecules *might* serve as the encoded information that the receiver, i.e. the pathway, is able to decode. The pathway then sends a new set of molecules as output signals . Note that this results in a conversion of signal carrier, which is analogous to the wireless signaling analogy where the wireless signal is decoded into some other form such as digital signals.

Modules should span multiple layers

2010-04-18T20:48:00.000-07:00

In biology, something like a protein domain is an ideal candidate for a "module" because it is a tool that can be reused at different places to serve different purposes. Nature does not want to reinvent tools; it would be more efficient to reuse existing ones -- protein domains are hard to invent, so it is a perfect item to reuse.

Moving on to network structure. Patters, such as feedback or feed-forward motifs, are not too difficult to reinvent (depending on how hard re-wiring is). For example, re-wiring genetic networks is easy. So it does not make sense to call any genetic network a "module". However, a combination of protein interactions combined with gene regulation might be a module.

For example, lets consider a network composed of a protein that responds to a small molecule and activates a protein that then upregulates a gene. This network is difficult to reinvent because it has multiple interactions that are very specific. It would be a module that is worth reusing. For example, the final gene product can be replaced with some other gene -- a simple way to reuse the module.

As an additional observation, I think it makes sense to say that modules span multiple "layers". For example, in electronics, logic gates convert analog circuits to digital. The example in the previous paragraph is a module that converts small molecule concentrations to gene regulation.

Microbial ecosystem programming

2010-03-18T19:21:00.000-07:00

There is a lot of hype about programming single cells by editing their genetic code, which in turn alters the dynamics of their regulatory or metabolic networks. However, using these engineered microbes in the real world is a very skeptical step, simply because we cannot predict exactly what can happen.

An alternative is to not edit the microbes themselves at all. Instead of building networks using enzymes inside the microbe, why not see a cell itself as a complex catalyst. A living cell converts some chemicals into others (environmental conditions apply).

Using microfluidics, it might be possible to completely characterize the "catalytic" profile of hundreds of microbial species, including bacteria, fungi, amoeba, algae, archaea. Then, build a "network" of different species such that the whole system is stable and performs some metabolic process that is of use to us, such as bio-remediation. This "engineered" network should be safer in the real world.

Reducing stochasticity in biology

2010-03-13T08:27:00.000-08:00

Suppose we are diagnosing a set of symptoms of a disease. If there is only one symptom, we will give our conclusion very little weight because that one symptom could be due to random chance. However, if we see multiple symptoms, then it is probably not due to random chance but due to some cause.

This simple rule is sufficient to eliminate stochastic effects in the final decision: make decisions based on multiple observations rather than a single observation.

In a cell, a "decision" can be something like upregulating a gene. If this decision is made by a single transcription factor that detects some sort of environment, then the decision (transcription) will be noisy. In contract, if multiple transcription factors that all respond to the same environment are used, then the transcription process will be a function of the sum of multiple stochastic processes. The sum will always have a lower variance. Unfortunately, multiple regulators means the there are multiple association/dissociation events, which would add more noise. The solution would have to be a bit more clever than this, but the general idea holds.

Automatic analysis and construction of feed-forward regulatory networks

2009-07-22T09:54:00.000-07:00

Given the assumption that transcriptional regulation is sigmoid shaped, i.e. the steady state diagram of the inducer vs. the target protein has a sigmoid shape (or inverted in the case of a repressor), it might be possible to automatically determine the steady state diagram of a regulatory network with no feedback controls. It might also be possible to generate a feed forward network for a given steady state diagram.

Lets take a simple case, where a transcription factor "a" indirectly upregulates and downregulates the target gene "X", as shown below. Then there are two regulation curves associated with "a". If we assume that a repressor will dominate over an activator, then we can determine what the the final steady state diagram of "X" will be as a function of "a" by looking at the dissociation constants (the center of the sigmoid curves).

The same method can be used to construct networks that have more complex steady state behaviors. For example, suppose "a" and "b" regulate "X" such that the activity of "X" is described by the yellow regions (1,2,3) in the diagram below. Then, it is possible to identify the network that will satisfy each piece and then put the pieces together. The shape of the sigmoid is determined by the dissociation constants.

Inference from topology

2009-05-05T21:15:00.000-07:00

Changing parameters of a system can drastically change its behavior in some situations. But even so, it might be possible to deduce certain dynamical properties from topology...

Lets take a simple case:

The gene product of gene A positively regulates gene B.

Even if no parameters are known, one can hypothesize the steady state behavior of B will probably be a sigmoid function of A. The exact shape of the curve cannot be infered without additional information.

Lets take a less specific case:

Gene A regulates gene B, but the type of regulation is unknown.

Now, there are two relationships that are possible: a sigmoid function (positive regulation) or an inverted sigmoid function (negative regulation). Again the exact shape of each cannot be known.

Lets extend the situation: A regulates B and C, and B regulates C.

Now, there are 2x2x2 possibilities. However, the number of different shapes that the 8 different combinations can make is probably not 8. It can be more in the case that this topology is highly versatile in the types of functions it can realize. It can be less than 8 if many versions of this toplogy produce similar behaviors. It is also possible that a few versions of this general toplogy are very interesting in the variety of functions they can realize, but the other versions are similar to one another. If this last situation is the real situation, then it is possible to make some inferences about the dynamical behavior with just the topological information. Here is how:

For a given general topology, i.e. where the regulation types are not known:

Generate all the different "versions" of this topology
Analyze each version by varying the parameters. Look for steady state behaviors as well as other interesting qualitative behaviors.
Classify each version of the topology by its qualitative behaviors.
Hypothesize possible uses for each qualitative behaviour, especially in the context of where the original toplogy came from.

The above approach is not specific to genetic networks. If it works for one type of network, it should work for the others.

Cell-wide Control

2009-05-04T19:38:00.000-07:00

Identifying multiple stable states of a cell and the key regulatory motifs controlling those states might be an efficient way to have control of the whole cell's dynamics. This is essentially like identifying all the switches in a circuit. Of course, some switches affect one another; such cases would need to be resolved.

If the switching points in a cellular network are identified, the cell can be rewired so that some states follow after another or that some states affect another -- small connections can create grand effects.

Implications of Network Motifs on Evolution

2009-05-04T19:28:00.000-07:00

There are claims that "modularity" may be advantageous to evolution. An analogy for supporting this hypothesis are logic gates in digital electronics. Rate of evolution of digital electronics has increased due to the fact that logic gates can be reused in different ways to form complex circuits.

Taking the analogy to biology...

Task at hand: need to identify the equivalent of logic gates in biology.
Once the above task is complete, redraw biological networks using the "modules".

I anticipate the following features would characterize biological modules:
(1) They are common in biological networks but hard to find because they will be intertwined with each other, i.e. may or may not be as obvious as looking for high frequency sub-graphs
(2) Individual modules will have a defined behavior...but this "defined behavior" may be difficult to identify.
(3) They will be highly flexible in the types of functions they can produce when connected with each other.
(4) Different physical networks, i.e. genetic networks, signaling networks, metabolic networks, RNA networks, will probably have different modules.

Negative and Positive Autoregulation at the Interface

2009-05-04T19:20:00.000-07:00

The scenario:

stimulus ----> A ----> B and A regulates itself

The steady state of B as a function of A is a signmoid curve.

Consider two cases:

1) A negatively regulates itself and A has a basal level of production. In this scenario, the steady state value of A will remain not-too-low and not-too-high (due to negative regulation). Therefore, in the sigmoid curve of B vs. A, A remains in the somewhat-linear region of the curve.

2) A positively regulates itself and has no basal level of production. In this scenario, the steady state of A is either low of high, so B is low or high (possible amplified).

So:
case (1) is one where the stimulus can linearly control B, and case (2) is one where the stimulus has a threshold above which B is active.

Modular vs non-modular evolution

2009-03-13T18:25:00.000-07:00

A simple experiment:

simulated evolution using "modules" vs simulated evolution without "modules"

Questions to answer:
1) What kind of fitness landscape might favor modules, if any?
2) Does stochasticity play a role?
3) What is the affect on rate of evolution?

Measuring with limited instruments

2009-03-08T16:44:00.000-07:00

At the present, instruments for measuring events inside a cell are limited. Although there are techniques using fluorophores to detect molecular events, they are not as quick and simple as GFP.

So the question: is it possible to place GFPs at specific locations such that those different measurements will provide sufficient information for calculating some un-measurable point?

Put in a different way: given a system, find a way to measure parameter X using a minimum number of other parameters (from a limited set of parameters).

Borrowing sub-systems from another orgnanism

2009-02-17T22:59:00.000-08:00

Engineering a cell is mostly done at the genetic level because that is where the tools are available. However, it is possible to borrow an entire system from another organism, such that the controls of the system are at the genetic level.

Consider a system with various proteins, including a few transcription factors. Now, lets take the system as a whole and consider the fact that we can control the concentrations of each of the members of the system, and we can take as "output" the concentrations of all the transcription factors in the system. As long as the entire system does not interact with the host machinery, this can be a generic strategy for borrowing systems, rather than genes, from other organisms.

Borrowing phosphorylation cycle from another organism

2009-02-15T19:53:00.000-08:00

Perhaps it is not neccessary to engineer proteins themselved in order to engineer a cell at the protein level.

Suppose organism Y has a system composed of a kinase, phosphatase, and transcription factor(s) that is controlled via phosphorylation. This entire system can be transplanted into organism X (e.g. E. coli), with each component under the control of different promoters.

This will give some control at the protein level, because the equilibrium concentrations of the phosphorylated and unphosphorylated proteins can be controlled by regulating the levels of the kinase and phosphotase. Of course, the control is still at the transcription level, but the phosphorylation will serve as a fast-acting system -- i.e. producing a few kinases will activate many transcription factors.

Feed forwards

2009-02-14T18:06:00.000-08:00

Apparently, the dynamical system involving only feed forward neural networks (not artificial) converge to a single point. Feedback is required in order to create complex effects, such as convergence to a complex attractors, etc. The structure of genetic networks is similar, and E. coli's regulatory network is largely composed of feed forward networks and very little feedback (except self-loops). Is it possible that for a single input pattern, the cell is adapted to respond in the same way every time, somewhat like a neural network learning an input pattern?

Is it possible that the genetic network "sets up" the community of proteins in the cell. The community of proteins determine the dynamics of the cell. In this is the case, then the genetic network is a system that takes input from the environment and produces a dynamical system, or machine, as the output. The dynamical system is designed to survive in the particular input environment. Here is an somewhat odd example (completely hypothetical) :

Lets consider a Venus fly trap or some similar plant...

input: bug lands inside the plant's mouth.

processing: some transcription factor triggered

output: production of several insect digestion enzymes

The output proteins may not just be for digestion itself. They might together form a small machine, such as an oscillator that is responsible for closing the mouth, or some other mechanics that is responsible for carrying out the whole digestion process. By identifying the machine that is the "output" it may be possible to learn behavior pattern is needed to cope to particular environmental signals.

Using broken transcription factors as AND logic

2009-02-10T19:00:00.000-08:00

Yeast-2-Hybrid is a technique where gene X is placed under the control of a transcription factor TF, but TF will only become active when proteins A and B interact with each other. This same concept can use used to create a transcriptional AND gate:

gene A --> protein A

gene B --> protein B

TF is only active when proteins A and B are present. Hence, gene X = gene A && gene B