Structural Biochemistry/Systems Biology/Analysis of Programmed Cell Death

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Overview[edit]

Systems biology is a computational way of modeling and analyzing complex biological systems and pathways. It has recently contributed largely to the field of biochemistry by serving as a tool to understanding complex systems at large, especially the analysis of programmed cell death (PCD), which has so many interconnected networks with loops and feedbacks that the task of modeling the entire system has been largely avoided until this time. There are largely three main processes of programmed cell death that have been successfully analyzed using systems biology: apoptosis, autophagy, and necrosis. All of these forms of cell death are controlled by the cell, whether they are stimulated or inhibited based on different factors, which are named "inputs" in systems biology. Inputs include protein concentrations, localizations, enzyme activation states, and kinetic factors of the environment.

Systems biology-Why and How[edit]

Generally, to understand a process of cell death, it would be logical to look at one factor, such as the exposure of the cell to one type of enzyme or a signal, and look at one output, such as cell lysis. However, it turns out that the global PCD network is much more complex and is non-linear. That is, there are multiple independent and dependent variables that affect and determine cell behavior at large. Therefore, one cannot just conclude that a change in one input will cause the cell to die, because there are many other interconnected inputs that may affect the cell more than that specific input. [1]

The systems biological approach is to gather data of numerous single pathway networks provided by bioinformatics or biochemistry, and to computationally connect those networks together into one large system to model cell behavior. To do this, systems biology uses multiple original differential equations(ODEs), which describe a change over a single independent variable, and partial differential equations(PDEs), which integrate multiple independent variables. ODEs generally focus on time as their independent variable, and it looks at the enzymatic activity that is determined experimentally. PDEs are used to look at spatial-temporal reactions(basically where the position of an object or a molecule is at a given time) such as diffusion. Using these equations, a Boolean logic is used to assign a number to each network(called nodes). A value of 1 simply means the network is on, and a value of 0 means the network is off. Thus, using the Boolean logic, one can determine which networks are on or off given certain conditions, or inputs. Combining the boolean values for all the networks, the final output, which for programmed cell death is the activation of caspases that initiate cell death, can be determined. [1]

There is also a data-driven approach to systems biology, which simply uses a large number of experimental data and statistically find correlations between each of the experimental data sets. This method is advantageous in that it does not require using complex differential equations, but the disadvantages lies in that it is limited to and dependent on the number and validity of experimental data. IN this method, linear algebra is used, instead of differential equations, to find correlation between the experimental data sets, clustering techniques are used to group and simplify the data, and partial least square analysis is used to predict data.[1]

3 Types of Programmed Cell Death[edit]

Apoptosis is mainly characterized by chromatin condensation and fragmentation, followed by blebbing, which causes the cell to be fragmented into apoptotic bodies. The apoptotic bodies are then finally disintegrated by caspase family of cysteine proteases.[2] Apoptosis is the most studied and the best characterized of the three cell death types, and both its intrinsic and extrinsic pathways have been successfully modeled using systems biology. Some of the main contributions to modeling apoptosis have come from the works of Krammer and Eils, who used ordinary differential equations to explain Fas-induced apoptosis. They wanted to predict the death output of the cells(output), in response to the concentrations of the Fas activating antibodies(input). Starting with a complex network, they clustered networks of signaling pathways that behaved similarly into submodules to simplify the system. In doing so, they also found the importance of an intracellular inhibitor pathway named c-FLIP, which induced apoptosis. Through their work, they showed that systems biology can successfully model a complex system of signaling pathways using submodules.

Autophagy is a process in which intracellular contents are engulfed and consumed by autophagosomes, which are multimembrane vesicles. Most of the characterization of autophagy have been made due to yeasst genetics, where a lot of autophagy genes and their functions were identified.[3] It plays an important role in homeostasis of the cell, because it gets removes damaged organelles and misfolded proteins. However, this engulfing process of intracellular contents can actually lead to cell death, and the pathway for this process has been modeled with the help of systems biology.

Necrosis is a process that involves cell swelling, organelle dysfunction, and cell lysis. Originally, it was defined as an uncontrolled event, or an accidental death that did not actually require any gene activity. However, recent research has shown that it is actually a genetically controlled event with specific pathways. Some of the identified regulators include c-Jun N-terminal kinase, apoptosis inducing factor, death-associated protein kinase, and reactive oxygen species.[1] The pathways for these regulators have been also modeled with systems biology, but are not yet fully understood.

Intrinsic and Extrinsic Pathways[edit]

Intrinsic Pathway is an activation of apoptosis caused by signals originating within the cell. A main trigger of the formation of apoptosome, which triggers apoptosis, is cytochrome c. Cytochrome c is found in between the inner and the outer membrane of the mitochondria. The intrinsic pathway increases the permeability of the mitochondrial outer membrane, which releases cytochrome c into the cytosol, thereby causing apoptosis. Therefore, the mitochondrial outer membrane permeabilization(MOMP) became crucial for understanding the intrinsic pathway to programmed cell death, and the stimili that activates this intrinsic death pathway is known as staurosporine.

Extrinsic Pathway is an activation of apoptosis due to a signal from an external source outside the cell. The stimuli that activates the extrinsic death pathway is called TRAIL, which stands for tumor necrosis factor apoptosis-inducing ligand.

Generation of the spatiotemporal model using partial differential equations for both of these pathways, and a comparison of this model to experimental data have shown that cytochrome c redistribution to the cytosol took longer for the intrinsic pathway than the extrinsic pathway, leading to the hypothesis that apoptosis occurs faster kinetically for the extrinsic pathway.[1]

References[edit]

  1. a b c d e Shani Bialik, Einat Zalckvar, Yaara Ber, Assaf D. Rubinstein, Adi Kimchi, Systems biology analysis of programmed cell death, Trends in Biochemical Sciences, Volume 35, Issue 10, October 2010, Pages 556-564, http://www.sciencedirect.com/science/article/pii/S0968000410000861
  2. Cohen, G.M. (1997) Caspases: the executioners of apoptosis. Biochem. J. 326, 1–16
  3. Nakatogawa, H. et al. (2009) Dynamics and diversity in autophagy mechanisms: lessons from yeast. Nat. Rev. Mol. Cell Biol. 10, 458–467