Some signaling mechanisms are used by many different signaling pathways, whereas other pathways respond to a specific set of stimuli [ 2 ]. We have mentioned above that the cell is characterized by a multitude of control mechanisms. Signaling pathways does not operate in isolation, and a key element of cellular control mechanisms is the extensive cross-talk between signaling pathways. The role of control mechanisms is to maintain the cell inside the normal operating parameters.
In the following we will present some of them:. How it was mentioned above, abnormal remodeling of cellular signalsomes creates signaling defects that have great significance for the onset of many diseases. The Endoplasmic Reticulum—mediated Quality Control ERQC control mechanism is a protein quality control mechanism, integrated with an adaptive stress response. The role of the ERQC control mechanism is to assure that the cell synthetized proteins have the correct three-dimensional structure, essential for normal function of the cell [ 4 ].
In essence, cellular response and cellular activity lies in synthesis of a specific protein required to fulfill various physiological functions of the cell. Biochemical structure and spatial geometry of the protein components are those that cause active or inactive state of the protein, and the impact that will have on other protein-specific mechanisms of cellular metabolism. Sometimes, during to the synthesis process, some parts of functional proteins may remain unfolded or may be misfolded.
Scientific studies reveals that failures to fold into native structure generally produces inactive proteins, or, in some instances, may generate modified or toxic functionality. The ERQC pathway is characterzed by a number of factors located into the endoplasmaticum reticulum lumen membrane and cytosol. The role of ERQC factors located in ER lumen consist in the detection and retention of proteins that have been produced by cell with folding aberrations. The ER membrane located factors are implied in retrotranslocation of misfolded polypeptides, and the cytosol located enzymes degrade retrotranslocated proteins.
Elimination of misfolded proteins from the ER by the ERQC program counteracts the production of aberrant proteins from various folding mishaps. Misfolded proteins are exported from the ER and subsequently destroyed by the ubiquitin-proteasome system in the cytosol by a process called retrotranslocation or ER-associated protein degradation ERAD [ 5 ].
In Fig 3. Protein synthesis processes are subject of interference from both intracellular and extracellular environment. The effects of intracellular and extracellular disturbances are materialized in aberrations of structure and folding of proteins. There are two mechanisms used to eliminate nonfunctional proteins or proteins with aberrations of the structure, as well as other subcellular components: the proteosome degradation and the autophagy. The 26S proteasome is the major degradation machinery in the cell for dysfunctional or damaged proteins [ 5 ].
Proteasomal degradation also regulates a variety of cellular process such as cell cycle progression. The proteasome is a large multi-subunit enzyme that is comprised of two sub-complexes, the 19S regulatory complex and the 20S proteolytic complex [ 5 ]. Autophagy is a lysosomal degradation pathway that degrades damaged or superfluous cell components into basic biomolecules, which are then recycled back into the cytosol.
In this respect, autophagy drives a flow of biomolecules in a continuous degradation-regeneration cycle. Autophagy is a non-selective, bulk degradation pathway and it is generally considered a pro-survival mechanism protecting cells under stress or poor nutrient conditions. Current research clearly shows that autophagy fulfills numerous functions in vital biological processes. It is implicated in development, differentiation, innate and adaptive immunity, ageing and cell death [ 5 ]. Research in the field have identified Three types of autophagy mechanisms have been in mammalian cells:.
The vast majority of these systems are oriented towards pattern recognition processes of large volumes of data in in the field of and proteoemic genomic the identification transcription factors, RNA sequence analysis, modeling biochemical factors involved in the generation and transmission of information in case of signaling pathways, such as and other similar activities that can be divided into static modeling bioprocesses.
Mathematica Wolfram Mathematica [ 7 ] is a dedicated software system; a multi-paradigm programming language; with a huge built-in library of both general-purpose and highly specialized functions. Programming in Mathematica consist in finding the right combination of primitives. In contrast to Matlab, Mathematica does not offer similar features to the other module in Matlab Simulink module. Python is a programming language interpreter based [ 9 ], with a good numerical support, provided by Numerical Python numpy package, which also provides the possibility to define specific bioinformatics functions for tasks as data management, file parsing, string processing, and interaction with databases.
Models designed in Python find their applicability in pattern matching or pattern identification in genomic and proteomic data. Python makes a distinction between matching and searching , which other languages do not. Matching looks only at the start of the target string, whereas searching looks for the pattern anywhere in the target [ 10 ]. Its open architecture enables sharing all source code among the user community and several different areas are solved and the solution appears usually as a new toolbox.
The dynamic model for a biological system may be created dragging and dropping the blocks from the block libraries to the new window and connect them and run the model. Next Generation Sequencing analysis and browser Sequence analysis and visualization, including pairwise and multiple sequence alignment and peak detection. Mass spectrometry analysis, including preprocessing, classification, and marker identification.
One may be converted DNA or RNA sequences to amino acid sequences using the genetic code, Perform statistical analysis on the sequences and search for specific patterns within a sequence. The toolbox provides also protein sequence analysis techniques, including routines for calculating properties of a peptide sequence such as atomic composition, isoelectric point, and molecular weight.
It can be determined the amino acid composition of protein sequences, cleave a protein with an enzyme, and create backbone plots and Ramachandran plots of PDB data. Considering the above issues, it is imperative in the model design for the biological system, to make the block diagrams resemble the logical flow of information through the biological system. The overall goal in designing a high level model for biosystems is to use the principle of feedback loops to adjust the controlled variable to follow a desired command variable accurately regardless of the command variables path and to minimize the effect of any external disturbances or changes in the dynamics of the system.
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The standard structure of is a relatively complex task if one must meet the basic requirements listed below:. The disturbances should be rejected or they must have a small influence on the controlled variable. The controlled variable should track the command input as precisely and as fast as possible. The closed loop should be as insensitive as possible with respect to changes in the plant parameters. In the model there are detailed the membrane receptors and it highlights how specific biochemical binding of a compound to the membrane receptor generates dynamic response of intracellular effectors and regulatory mechanisms.
Delay elements are corresponding to bioprocesses occurring in the membrane when the biochemical signaling structure biochemical factor is binding on the specific receptor specific factor receptor existing in the target cell membrane. Feedback control loops correspond to recognition processes of aberrant protein structures and folding aberrations and their destruction processes in the mitochondria. The results of different parameters simulations are presented in figures 5 a,b,c,d.
In simulations where used Runge Kutta 5th order method fig 5 a,c and Runge Kutta 3th order method fig 5 b,d. Adjusting the transfer functions patameters, entire dynamic behavior of the model may be adjusted. From the point of view of the dynamic behavior of the model, the simulation results revealed that after overcoming the transitional regime, the model shows a stable behavior, similary to the living systems behavior observed from experimental data pSOS, pJAK2.
In fig. There were detalied the Nucleus regulating processes and Mithocondria control mechanisms.
Matlab Codes For Control Systems
The outputs of simulations are presented in figures 7 a,b. The differences between Figures 5 a, b,c,d and 7 a, b representing the shape of the output signal of the model shows that the model detailed at subcellular level and proper adjustment of transfer functions that describe each subcellular component ensure high accuracy of the model. Model responses — System dynamic behavior.
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Attenuation or amplification factors may be adjusted from control loops according to the real behavior of the modeled signaling pathway. For example, in the case of signaling pathway that that activates Janus2 protein tyrosine kinase JAK2 or phosphorylated EPOR attenuation factors are recommended, while the signaling pathways corresponding to double phosphorylated ERK1 and 2 or double phosphorylated MEK1 and 2 are recommended amplification factors.
Living systems are complex systems, characterized by a multitude of control mechanisms. Signaling pathways do not operate in isolation, and a key element of living systems control mechanisms is the extensive cross-talk between signaling pathways that control all biological regulating mechanisms. Experimental studies revealed that each cell type has a unique repertoire of cell signaling components, signalsome, determined by the cell phenotype, expressed during the final stages of cell development, in order to control cell particular physiology. The signalsomes are characterized by a high degree of plasticity being constantly remodeled to in order to adjust cell responses to environmetal changes.
High level modeling in biosystems is a laborious process due a number of facts: the uniqueness of living systems behavior, insufficient knowledge of bioprocesses, high costs of experiments and the impossibility to repeat them in absolutely identical conditions, the large number of unknowns involved in the models, the extensive cross-talk between signaling pathways.
Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3. Help us write another book on this subject and reach those readers. Login to your personal dashboard for more detailed statistics on your publications. Edited by Kelly Bennett. Monzani, A. Prado, L. Lessa and L. Edited by Bishnu Pal.
We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Downloaded: Introduction The advances in biology, bioinformatics, programming technologies and computer systems made possible to store and analyze large amount of biological data with the possibility of global exploration and visualization of this data with sequence browsers the possibility to select features for genomic and proteomic data, the possibility to identifying the transcription factors; to analyze RNA-Sequences data in order to identify specific expressed genes and common bioinformatics workflows, identify copy number variants and SNPs in microarray data.
High — Level modeling in biosystems High level modeling in biology is more complex as modeling in other fields of science and technology. Control systems theory and high level modeling in biosystems There are a multitude of worldwide databases that store information about signaling pathways and factors involved in biochemical processes, but the results of research in this field highlights that mechanisms underlying the bioprocesses are not still fully elucidated.
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- Introduction to the Simulation of Dynamics Using Simulink?
Deep knowledge of the properties of various equations that constitute the model. The problem must be formulated correctly in Hadamarad sense, ie to verify the following restrictions: The solution should exist The solution should be unique The solution should be continuous with respect to restrictions boundary conditions or in relation to initial conditions Developing systemic models in the case of bioprocesses shows some particularities compared to other areas of science and technology. Assuming that the train only travels in one dimension along the track , we want to apply control to the train so that it starts and comes to rest smoothly, and so that it can track a constant speed command with minimal error in steady state.
The mass of the engine and the car will be represented by and , respectively. Furthermore, the engine and car are connected via a coupling with stiffness. In other words, the coupling is modeled as a spring with a spring constant. The force represents the force generated between the wheels of the engine and the track, while represents the coefficient of rolling friction. The first step in deriving the mathematical equations that govern a physical system is to draw the free-body diagram s representing the system. This is done below for our train system. From Newton's second law, we know that the sum of the forces acting on a body is equal to the product of the mass of the body and its acceleration.
The forces acting on the train car in the horizontal direction are the spring force and the rolling resistance. In the vertical direction, the weight forces are balanced by the normal forces applied by the ground. Therefore, there will be no acceleration in the vertical direction. We will model the spring as generating a force that is linearly proportional to the deformation of the spring, , where and are the displacements of the engine and car, respectively.
Here it is assumed that the spring is undeformed when and equal zero. The rolling resistance forces are modeled as being linearly proportional to the product of the corresponding velocities and normal forces which are equal to the weight forces. Applying Newton's second law in the horizontal direction based on the above free-body diagrams leads to the following governing equations for the train system. This set of system equations can now be represented graphically without further manipulation.
Specifically, we will construct two copies one for each mass of the general expression or. First, open Simulink and open a new model window. Then drag two Sum blocks from the Math Operations library into your model window and place them approximately as shown in the figure below. The outputs of each of these Sum blocks represents the sum of the forces acting on each mass. Multiplying each output signal by will give us the corresponding acceleration of each mass. Now drag two Gain blocks from the Math Operations Library into your model and attach each one with a line from the output of one of the Sum blocks.
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This is accomplished by double-clicking in the space above each of the two signal lines and entering the desired label. These Gain blocks should contain for each of the masses. You will notice that the gains did not appear in the image of the Gain blocks, rather the blocks display a value of -K-.
This is because the blocks are too small on the screen to show the full variable name inside the triangle. The blocks can be resized so that the actual gain value can be seen. To resize a block, select it by clicking on it once. Small squares will appear at the corners. Drag one of these squares to stretch the block.
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Your model should appear as below. The outputs of these gain blocks are the accelerations of each of the masses the train engine and car. The governing equations we derived above depend on the velocities and displacements of the masses. Since velocity can be determined by integrating acceleration, and position can be determined by integrating velocity, we can generate these signals employing integrator blocks. Drag a total of four Integrator blocks from the Continuous library into your model, two for each of our two accelerations.
Connect these blocks and label the signals as shown below. The second integrator then takes this velocity and outputs the displacement of the first mass "x1". The same pattern holds for the integrators for the second mass. Now, drag two Scopes from the Sinks library into your model and connect them to the outputs of these integrators. Label them "x1" and "x2". Now we are ready to add the forces acting on each mass. First, we need to adjust the inputs on each Sum block to represent the proper number of forces we will worry about the signs later.
The symbol " " serves as a spacer. There are only 2 forces acting on mass 2, therefore, we can leave that Sum block alone for now. The first force acting on mass 1 is just the input force,. Drag a Signal Generator block from the Sources library and connect it to the uppermost input of the corresponding Sum block.
Label this signal as "F". The next force acting on mass 1 is the rolling resistance force. Recall that this force is modeled as follows. To generate this force, we can tap off the velocity signal and multiply by an appropriate gain. Drag a Gain block into your model window. Connect the output of the Gain block to the second input of the Sum block.
The rolling resistance force, however, acts in the negative direction. Next, resize the Gain block to display the full gain and label the output of the Gain block "Frr1".
Your model should now appear as follows. The last force acting on mass 1 is the spring force. Recall that this force is equal to the following. Therefore, we need to generate a signal which we can then be multiplied by a gain to create the force.
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