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The term "smart molecules" generally refers to molecules that have been designed or engineered to exhibit certain properties or behaviors, such as the ability to respond to specific stimuli or to self-assemble into functional structures.

Smart molecules can be used in a variety of applications, including drug delivery, sensing, and nanotechnology. For example, some smart molecules are designed to target specific cells or tissues in the body and release their cargo (such as drugs) only when triggered by a certain signal, such as a change in pH or the presence of a particular molecule.

Other smart molecules may be designed to self-assemble into specific shapes or structures, which can be useful for creating nanoscale devices or materials. Smart molecules can also be used in sensors that can detect specific molecules or changes in the environment.

The concept of "smart molecules" can refer to molecules or molecular systems that exhibit complex and intelligent behavior, often through intricate interactions and dynamic processes. These molecules can carry out sophisticated tasks, such as information processing, self-organization, and adaptive behavior.

Histone proteins, as well as other molecules involved in cellular processes, can be considered as "smart molecules" due to their ability to regulate gene expression, orchestrate DNA packaging, and participate in various signaling pathways. These molecules possess intricate structures and exhibit dynamic properties that allow them to interact with other molecules and respond to environmental cues.

Understanding the computational potential of such smart molecules, including histones, can shed light on the complex mechanisms underlying biological processes. By investigating their structural properties, interactions, and regulatory functions, scientists can gain insights into how these molecules contribute to cellular function and potentially harness their capabilities for various applications in fields such as synthetic biology, drug development, and nanotechnology.

Overall, the study of smart molecules, including histones, represents an exciting area of research with the potential to uncover novel principles of molecular computation and provide inspiration for developing advanced biomaterials and molecular systems.

  This stereographic image (which can be viewed in crosseyed 3d) was created by simulating a chain of carbon atoms (similar to a protein backbone) fixed at one end and with a paintbrush at the other end. Is this system with it's output what physicists would call a "string"?  This structure has program code 0.004,0,0.004,1,2,0,1,2,0,4,2,0,4,2,0.004,4,4  B17=180.0  (The numbers represent the relative rotational velocities of succesive covalent bonds, B17=180 indicates the last bond in the chain started with a 180 degree rotation). In other words, the output from a molecular program. 

  A single carbon atom, while possessing intriguing geometrical properties and being the basis of life as we know it, does not immediately appear very intelligent.  Two carbon atoms, however, bonded together covalently, have some interesting properties.  As the chain of the organic molecule grows, the more intelligent it seems to become.  At the head of the molecular class, we have the networked nucleic acids and proteins of the human brain.  In another sense, perhaps it should be the stem cell molecules. Evidently, given some population of molecules M at time zero, given time to evolve… at some point in time life is achieved.  The transition from a dumb collection of molecules to intelligent being is a fuzzy one.  When does life begin?  Is it RNA molecules…or viruses? Today, bioengineers aim development of novel or improved functional molecules ("smart molecules") in areas such as the following:   "Smart media" (e.g. micro emulsions, ionic liquids, mesophases)   "Smart catalysts and reagents"   "Smart materials and devices" (e.g. switchable and/or electro-optical materials, sensors, light-emitting diodes, solar cells)

Proteins are a class of molecules with many different biological functions and classified according to their biological roles.

.       Enzymes
·      Transport Proteins
·       Structural Proteins
·       Storage Proteins
·       Hormonal Proteins
·       Receptor Proteins
·       Contractile Proteins
·       Defensive Proteins

 ·      Intrinsically Unstructured Proteins  

.       Signaling Proteins

Now we can add Computational or information processing proteins to the list.  Due to their ability to transition through multiple states and accept discrete inputs, the Histone proteins are at the kernel of biological information processing. 

Some examples of computational or information processing proteins include:

1. Kinases: These are enzymes that can add phosphate groups to other proteins or molecules. This process, known as phosphorylation, can activate or deactivate proteins, and can serve as a way to transmit signals within a cell.
2. G-protein coupled receptors (GPCRs): These are transmembrane proteins that can detect external signals and transmit them across the cell membrane to the inside of the cell. They play important roles in many physiological processes such as vision, taste, and smell.
3. Transcription factors: These are proteins that can bind to specific DNA sequences and regulate the expression of genes. They can activate or repress gene expression, and play important roles in cell differentiation and development.
4. Ion channels: These are transmembrane proteins that can allow ions to pass through the cell membrane. They play important roles in many physiological processes such as nerve conduction and muscle contraction.
5. Ribosomes: These are molecular machines that can synthesize proteins by reading the information encoded in mRNA molecules. They can translate the genetic code into a specific sequence of amino acids to form a protein.

If the Histone protein is viewed as a boolean network then Karnaugh Maps and The Quine-Mccluskey algortihm can be used to minimize the boolean function describing the network.  Each gene serves as an output port.  In other words each gene reflects in an on/off way to the input into the boolean network.  Current estimates of the number of input sites on the Histone tails and body combined are currently at about fifty...and rising. The combinatorial numbers involved are large yet finite.  These procedures can be used to optimize pharmaceutical therapy and suggest new therapies. At any rate the potential uses for this knowledge would clearly be many. 

In the followiing A,B,C,D represent binding sites on the histone tail or body. The table indicates the effect of each of the 16 possible binary possibilities (f). Every gene in the genome has it's own such table.

 f(A,B,C,D) = ∑m(4,8,10,11,12,15) + ∑d(9,14) 

           A B C D    f

m0     0 0 0 0       0

m1      0 0 0  1     0

m2     0 0  1 0      0

m3     0 0 1  1      0

m4     0 1 0  0      1

m5     0 1  0  1     0

m6     0  1  1 0     0

m7      0 1  1  1    0

m8      1 0 0 0      1

m9      1 0 0  1      X

m10    1  0  1 0     1

m11     1 0  1  1    1

m12    1  1  0 0     1

m13    1  1  0 1      0

m14    1  1  1 0     X

m15    1  1  1  1    1

 The Quine-McCluskey algorithm minimizes the following corresponding equation f(A,B,C,D)  = A'BC'D' + AB'C'D' + AB'C'D + AB'CD' + AB'CD + ABC'D' + ABCD' + ABC

to either of the two following equivalent equations-  

f(A,B,C,D) = BC'D' + AB' + AC 

f(A,B,C,D) = BC'D' + AD' + AC  

          

A carbon-based vibrating string, modeled as a chain of covalent bonds, possesses computational possibilities. Each bond in the chain can be considered as a discrete unit that can assume multiple states or rotational positions, effectively acting as a counter or storage unit. Here are some computational possibilities and concepts related to a vibrating carbon-based string:

1. State Counting: Each bond can be in one of several rotational states (e.g., 0, 1, 2, 3, etc.), allowing the string to count or represent discrete numerical values. By coordinating the rotational states of multiple bonds, you can perform arithmetic operations, such as addition or subtraction.
2. Storage and Memory: The different rotational states of individual bonds can be used to store and retrieve information. Patterns of rotational states can encode data, and the string can act as a memory device.
3. Information Processing: Vibrational patterns of the string can encode and process information. By manipulating the rotational states of bonds, you can implement logical operations, comparisons, and decision-making processes.
4. Sequential Processing: The string can be designed to process information sequentially. As vibrations propagate along the string, they can interact with different bonds, leading to sequential computations or transformations.
5. Parallel Processing: If you have multiple vibrating strings operating in parallel, each string can handle a specific task or computation. This parallelism can be harnessed for complex calculations or simulations.
6. Feedback Loops: By creating feedback loops within the string, you can implement control systems or feedback mechanisms for regulating processes or responses to external stimuli.
7. Signal Processing: The vibrational patterns of the string can serve as signals that carry information or perform signal processing tasks, such as filtering, modulation, or demodulation.
8. Finite State Machines: The string can be configured as a finite state machine (FSM), where different rotational states represent different states of the machine. FSMs are widely used in computer science for tasks like pattern recognition and control.
9. Analog Computing: The continuous vibrations of the string can represent analog quantities, making it suitable for analog computing tasks such as solving differential equations or simulating physical systems.
10. Energy Transfer and Wave Phenomena: The vibrations in the string involve the transfer of energy and exhibit wave-like behavior. This can be used to study wave phenomena, interference, and resonance, among other physics-related computations.

Exploring these ideas can lead to insights into novel computational paradigms and inspire new approaches to information processing. Additionally, in the realm of nanotechnology and molecular computing, researchers are investigating the potential of molecular-scale structures, including carbon-based molecules, for performing computational tasks.