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                        <h6>Creating tools for synthetic genetic network creation</h6>
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                         <h1>
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                        <h2 class="lead">Sequential logic vs. combinational logic</h2>
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                            <img src="https://static.igem.org/mediawiki/2016/f/ff/Logo_intelligene_medium.png" alt="INTELLIGENE" style="max-width: 350px;" />
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                        <h3>Introduction</h3>
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                                If you could <b>reprogram</b> your cells, what would you make them do?
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                            Combinational logic circuits implement Boolean functions, which always depend on input. Boolean functions are mappings of input to output. They are functions of input only. It means that if you feed in an input to a circuit, the output will always be the same for that circuit. If that value were not the same every single time, then the output must not completely depend on input. Something else must be affecting the output.
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                                A <b>multi-biosensor</b> for soil contamination?
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                                Cells that can <b>count</b>?
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                        <h3>Example-Buy coffee</h3>
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                            Let's consider a vending machine. We want to see if its output is solely dependent on the input.
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Imagine this vending machine only sells coffee, and that the price of a coffee is 75 cents. The machine can only take quarters. Once 75 cents are deposited, a coffee is dispensed without any button being pressed. (What a stupid machine!)
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One day, when you want to drink coffee, you see this machine, and you happen to be holding several quarters. You place the first quarter in the machine, and out comes...nothing! Undaunted, you put another quarter in, and out comes.... nothing! Frustrated, you decide to put in yet another quarter, and out comes a coffee! Delicious, savory and mellow Coke! You drink, and are content.
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                                A medical patch that can <b>heal</b> patients with faulty methabolic pathways?
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                                Cells that can <b>survive</b> in harsh environements by making complex calculations about their surroundings going where <b>no cells have gone before</b>?
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Then, invigorated by caffeine, you begin to think "Is this machine a function?"  
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You gave the same input, three times in a row, but it did not produce the same output.
 
  
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A mathematical function maps inputs to outputs. Thus, once you know what the input maps to, that should be it. In this case, the input (a quarter) mapped to nothing. So, clearly, this does not behave like a function.
 
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What's happening? Clearly, the machine is storing some information. In particular, it's records how much money you have entered so far. The output is determined not only by the input, but also by this stored information.
 
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                        <h3>Sequential logicS</h3>
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                <h2><span>Why </span>genetic circuits ?</h2>
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                    <p class="sub-lead">As synthetic biologists, we are constantly striving to change and understand the world around us. Whether it be the creation of biosensors for drugs or cellular environments, or the creation of new, highly specialized cells, many of the problems facing synthetic biologists today can be solved through the creation of cells with synthetic or partly-synthetic genetic networks. These cells can be taught to make calculations that they have never done before. They can produce new proteins, but also take combinations of inputs and have a predictable output, essentially “making decisions” based on cellular information. The applications are boundless, but this technology is not ready yet. Intelligene represents a concerted effort by a group of students to create new tools - and refine old ones – geared towards the creation of synthetic genetic networks. Follow our journey through a spectacular summer below.
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Key things to notice:
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 Like combinational logic circuits, a sequential logic circuit has inputs and outputs.
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 Unlike combinational logic circuits, a sequential logic circuit is related to time.
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 Also, there is a box inside the circuit called State. This box contains flip flops. Assume it has k flip flops. The flip flops basically store a k-bit number representing the current state. The state can be updated.
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 The output is computed based on the inputs and the state coming out of the state box.
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The information needed to update to the state (called the next state) comes from the current state (the current value of q) and the input, which is fed through combinational logic, and fed back into the state box, telling the state box how to update itself.
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                <h2><span>What </span>we did</h2>
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                     <p class="sub-lead">Intelligene is best thought of as a suite of tools, combining aspects of computational biology and synthetic biology to help scientists with new dCas9-based genetic circuits, from their design all the way through to their implementation.
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                    <p class="sub-lead">In order to help with the intelligent and intuitive design of these circuits, we decided to modify a recently-published program called Cello. Cello works by combining information about the circuit the user wants to create, and biological information about the system the user is building the circuit for, with a series of algorithms which produce a plasmid which contains an optimized biological form of that circuit. While we loved the concept and its powerful design, we thought that certain aspects could be built upon to increase user-friendliness and embellish its open-source nature. To this end, we created a new, simple, graphical user interface, and connected Cello with databases we created, which make the information it uses public and easily transferrable between users.
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                        <h3>Summary</h3>
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                    <p class="sub-lead ">Once a circuit is designed, biological gates are needed to make that circuit. These gates essentially take a simple combination of inputs and produce a given output, according to some rule. Many currently used gates are based on transcription factors, but the use of artificial transcription factors, built using dCas9, is becoming more commonplace as these transcription factors can be reprogrammed to be specific to a certain target and their effect can be fine-tuned by selecting appropriate transcriptional effectors to use within them. During our project we successfully used an architecture first described by Zalatan et al. (2015) to reproduce a previously described activating artificial transcription factor. We also produced two novel repressing artificial transcription factors. Using these parts, we developed two novel NOT gates, each with differing designs.
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The main difference between sequential circuits and combinational circuits is that sequential circuits compute their output based on input and state, and that the state is updated. Combinational logic circuits implement Boolean functions, so they are functions only of their inputs, and are not based on clocks.
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Revision as of 01:03, 2 November 2017

iGEM EPFL 2016

Creating tools for synthetic genetic network creation

INTELLIGENE

If you could reprogram your cells, what would you make them do?

A multi-biosensor for soil contamination?

Cells that can count?

A medical patch that can heal patients with faulty methabolic pathways?

Cells that can survive in harsh environements by making complex calculations about their surroundings going where no cells have gone before?

Why genetic circuits ?

As synthetic biologists, we are constantly striving to change and understand the world around us. Whether it be the creation of biosensors for drugs or cellular environments, or the creation of new, highly specialized cells, many of the problems facing synthetic biologists today can be solved through the creation of cells with synthetic or partly-synthetic genetic networks. These cells can be taught to make calculations that they have never done before. They can produce new proteins, but also take combinations of inputs and have a predictable output, essentially “making decisions” based on cellular information. The applications are boundless, but this technology is not ready yet. Intelligene represents a concerted effort by a group of students to create new tools - and refine old ones – geared towards the creation of synthetic genetic networks. Follow our journey through a spectacular summer below.

What we did

Intelligene is best thought of as a suite of tools, combining aspects of computational biology and synthetic biology to help scientists with new dCas9-based genetic circuits, from their design all the way through to their implementation.

In order to help with the intelligent and intuitive design of these circuits, we decided to modify a recently-published program called Cello. Cello works by combining information about the circuit the user wants to create, and biological information about the system the user is building the circuit for, with a series of algorithms which produce a plasmid which contains an optimized biological form of that circuit. While we loved the concept and its powerful design, we thought that certain aspects could be built upon to increase user-friendliness and embellish its open-source nature. To this end, we created a new, simple, graphical user interface, and connected Cello with databases we created, which make the information it uses public and easily transferrable between users.

Once a circuit is designed, biological gates are needed to make that circuit. These gates essentially take a simple combination of inputs and produce a given output, according to some rule. Many currently used gates are based on transcription factors, but the use of artificial transcription factors, built using dCas9, is becoming more commonplace as these transcription factors can be reprogrammed to be specific to a certain target and their effect can be fine-tuned by selecting appropriate transcriptional effectors to use within them. During our project we successfully used an architecture first described by Zalatan et al. (2015) to reproduce a previously described activating artificial transcription factor. We also produced two novel repressing artificial transcription factors. Using these parts, we developed two novel NOT gates, each with differing designs.