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A chemical reaction is a process by which the bonds in the atoms of substances are rearranged to generate new substances. Matter cannot be created or destroyed in a chemical reaction—the same type and number of atoms that make up the reactants are still present in the products. Merely, the rearrangement of chemical bonds produces new compounds.
Chemical Reactions Rearrange Atoms into New Substances
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A balanced chemical equation provides the information of chemical formulas of the reactants and products involved in the chemical change. A reaction’s stoichiometry helps predict how much of the reactant is needed to produce the desired amount of product, or in some cases, how much product will be formed from a specific amount of the reactant.
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Chemical reactions require sufficient energy to cause the matter to collide with enough precision and force that old chemical bonds can be broken and new ones formed. In general, kinetic energy is the form of energy powering any type of matter in motion. Imagine a person building a brick wall. The energy it takes to lift and place one brick on top of another is the kinetic energy—the energy matter possesses because of its motion. Once the wall is in place, it stores potential energy.
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Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks
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Programming discrete distributions with chemical reaction networks.

Luca Cardelli1,2, Marta Kwiatkowska1, Luca Laurenti1

  • 11Microsoft Research, Cambridge, UK.

Natural Computing
|March 27, 2018
PubMed
Summary

This study demonstrates programming Chemical Reaction Networks (CRNs) to achieve desired probability distributions. Methods allow precise control over CRN steady states for various distributions, including approximations for infinite ones.

Keywords:
Discrete distributionsQuantitative reasoningStochastic chemical reaction networks

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Systems Biology

Background:

  • Chemical Reaction Networks (CRNs) are powerful models for biochemical systems.
  • Engineering specific probabilistic behaviors in CRNs is a key challenge in synthetic biology.

Purpose of the Study:

  • To explore and define the range of probabilistic behaviors engineerable with CRNs.
  • To develop methods for programming CRNs to realize desired probability distributions at steady state.

Main Methods:

  • Development of programming techniques for CRNs to target specific probability distributions.
  • Utilizing mathematical frameworks to approximate distributions with countable infinite support.
  • Formulating a computational calculus for distribution manipulation.

Main Results:

  • Methods provided to program CRNs for target distributions with finite support.
  • Arbitrarily small error approximations achieved for distributions with countable infinite support.
  • Optimized schemes developed for specific distributions, such as the uniform distribution.

Conclusions:

  • CRNs can be engineered to exhibit a wide range of probabilistic behaviors.
  • A complete calculus for finite support distributions is established, compilable to CRNs.
  • This work advances the design and control of complex biochemical systems using CRNs.