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In Cellular biophysics

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Metabolic control analysis. MCA was originally developed to describe the control in metabolic pathways but was subsequently extended to describe signaling and genetic networks.

Metabolic control analysis

MCA has sometimes also been referred to as Metabolic Control Theory but this terminology was rather strongly opposed by Henrik Kacser, one of the founders[citation needed]. More recent work[4] has shown that MCA can be mapped directly on to classical control theory and are as such equivalent. Control Coefficients[edit] A control coefficient[7][8][9] measures the relative steady state change in a system variable, e.g. pathway flux (J) or metabolite concentration (S), in response to a relative change in a parameter, e.g. enzyme activity or the steady-state rate ( ) of step i. Biochemical systems theory. Biochemical systems theory is a mathematical modelling framework for biochemical systems, based on ordinary differential equations (ODE), in which biochemical processes are represented using power-law expansions in the variables of the system.

Biochemical systems theory

This framework, which became known as Biochemical Systems Theory, has been developed since the 1960s by Michael Savageau and others for the systems analysis of biochemical processes.[1] According to Cornish-Bowden (2007) they "regarded this as a general theory of metabolic control, which includes both metabolic control analysis and flux-oriented theory as special cases".[2] Representation[edit] The dynamics of a species is represented by a differential equation with the structure: where Xi represents one of the nd variables of the model (metabolite concentrations, protein concentrations or levels of gene expression). j represents the nf biochemical processes affecting the dynamics of the species.

On the other hand, ij (stoichiometric coefficient), Dynamical system. The Lorenz attractor arises in the study of the Lorenz Oscillator, a dynamical system.

Dynamical system

Overview[edit] Before the advent of computers, finding an orbit required sophisticated mathematical techniques and could be accomplished only for a small class of dynamical systems. Numerical methods implemented on electronic computing machines have simplified the task of determining the orbits of a dynamical system. For simple dynamical systems, knowing the trajectory is often sufficient, but most dynamical systems are too complicated to be understood in terms of individual trajectories. The difficulties arise because: The systems studied may only be known approximately—the parameters of the system may not be known precisely or terms may be missing from the equations. History[edit] Many people regard Henri Poincaré as the founder of dynamical systems.[3] Poincaré published two now classical monographs, "New Methods of Celestial Mechanics" (1892–1899) and "Lectures on Celestial Mechanics" (1905–1910). .

Cell signaling. Traditional work in biology has focused on studying individual parts of cell signaling pathways.

Cell signaling

Systems biology research helps us to understand the underlying structure of cell signaling networks and how changes in these networks may affect the transmission and flow of information. Such networks are complex systems in their organization and may exhibit a number of emergent properties including bistability and ultrasensitivity. Cell migration. Cell migration is a central process in the development and maintenance of multicellular organisms.

Cell migration

Tissue formation during embryonic development, wound healing and immune responses all require the orchestrated movement of cells in particular directions to specific locations. Errors during this process have serious consequences, including intellectual disability, vascular disease, tumor formation and metastasis. An understanding of the mechanism by which cells migrate may lead to the development of novel therapeutic strategies for controlling, for example, invasive tumour cells.

Cells often migrate in response to specific external signals, including chemical signals and mechanical signals. Due to the highly viscous environment (low Reynolds number), cells need to permanently produce forces in order to move. Cell division. Three types of cell division For simple unicellular organisms[Note 1] such as the amoeba, one cell division is equivalent to reproduction – an entire new organism is created.

Cell division

On a larger scale, mitotic cell division can create progeny from multicellular organisms, such as plants that grow from cuttings. Cell division also enables sexually reproducing organisms to develop from the one-celled zygote, which itself was produced by cell division from gametes. And after growth, cell division allows for continual construction and repair of the organism.[5] A human being's body experiences about 10,000 trillion cell divisions in a lifetime.[6] Cell division has been modeled by finite subdivision rules.[7]

Cell biology. Understanding cells in terms of their molecular components.

Cell biology

Knowing the components of cells and how cells work is fundamental to all biological sciences. Appreciating the similarities and differences between cell types is particularly important to the fields of cell and molecular biology as well as to biomedical fields such as cancer research and developmental biology. These fundamental similarities and differences provide a unifying theme, sometimes allowing the principles learned from studying one cell type to be extrapolated and generalized to other cell types.

Therefore, research in cell biology is closely related to genetics, biochemistry, molecular biology, immunology, and developmental biology.