Cellular model

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Part of the Cell cycle Signal transduction pathways.svg
Part of the Cell cycle

A cellular model is a mathematical model of aspects of a biological cell, for the purposes of in silico research.

Contents

Developing such models has been a task of systems biology and mathematical biology. It involves developing efficient algorithms, data structures, visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling. It involves the use of computer simulations of cellular subsystems, such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks.

Overview

The eukaryotic cell cycle is very complex and is one of the most studied topics, since its misregulation leads to cancers. It is possibly a good example of a mathematical model as it deals with simple calculus but gives valid results. Two research groups [1] [2] have produced several models of the cell cycle simulating several organisms. They have recently produced a generic eukaryotic cell cycle model which can represent a particular eukaryote depending on the values of the parameters, demonstrating that the idiosyncrasies of the individual cell cycles are due to different protein concentrations and affinities, while the underlying mechanisms are conserved (Csikasz-Nagy et al., 2006).

By means of a system of ordinary differential equations these models show the change in time (dynamical system) of the protein inside a single typical cell; this type of model is called a deterministic process (whereas a model describing a statistical distribution of protein concentrations in a population of cells is called a stochastic process).

To obtain these equations an iterative series of steps must be done: first the several models and observations are combined to form a consensus diagram and the appropriate kinetic laws are chosen to write the differential equations, such as rate kinetics for stoichiometric reactions, Michaelis-Menten kinetics for enzyme substrate reactions and Goldbeter–Koshland kinetics for ultrasensitive transcription factors, afterwards the parameters of the equations (rate constants, enzyme efficiency coefficients and Michaelis constants) must be fitted to match observations; when they cannot be fitted the kinetic equation is revised and when that is not possible the wiring diagram is modified. The parameters are fitted and validated using observations of both wild type and mutants, such as protein half-life and cell size.

In order to fit the parameters the differential equations need to be studied. This can be done either by simulation or by analysis.

In a simulation, given a starting vector (list of the values of the variables), the progression of the system is calculated by solving the equations at each time-frame in small increments.

In analysis, the properties of the equations are used to investigate the behavior of the system depending on the values of the parameters and variables. A system of differential equations can be represented as a vector field, where each vector described the change (in concentration of two or more protein) determining where and how fast the trajectory (simulation) is heading. Vector fields can have several special points: a stable point, called a sink, that attracts in all directions (forcing the concentrations to be at a certain value), an unstable point, either a source or a saddle point which repels (forcing the concentrations to change away from a certain value), and a limit cycle, a closed trajectory towards which several trajectories spiral towards (making the concentrations oscillate).

A better representation which can handle the large number of variables and parameters is called a bifurcation diagram (bifurcation theory): the presence of these special steady-state points at certain values of a parameter (e.g. mass) is represented by a point and once the parameter passes a certain value, a qualitative change occurs, called a bifurcation, in which the nature of the space changes, with profound consequences for the protein concentrations: the cell cycle has phases (partially corresponding to G1 and G2) in which mass, via a stable point, controls cyclin levels, and phases (S and M phases) in which the concentrations change independently, but once the phase has changed at a bifurcation event (cell cycle checkpoint), the system cannot go back to the previous levels since at the current mass the vector field is profoundly different and the mass cannot be reversed back through the bifurcation event, making a checkpoint irreversible. In particular the S and M checkpoints are regulated by means of special bifurcations called a Hopf bifurcation and an infinite period bifurcation.

Cell cycle bifurcation diagram.jpg

Molecular level simulations

Cell Collective [3] is a modeling software that enables one to house dynamical biological data, build computational models, stimulate, break and recreate models. The development is led by Tomas Helikar, [4] a researcher within the field of computational biology. It is designed for biologists, students learning about computational biology, teachers focused on teaching life sciences, and researchers within the field of life science. The complexities of math and computer science are built into the backend and one can learn about the methods used for modeling biological species, but complex math equations, algorithms, programming are not required and hence won't impede model building.

The mathematical framework behind Cell Collective is based on a common qualitative (discrete) modeling technique where the regulatory mechanism of each node is described with a logical function [for more comprehensive information on logical modeling, see [5] [6] ].

In the July 2012 issue of Cell, a team led by Markus Covert at Stanford published the most complete computational model of a cell to date. The model of the roughly 500-gene Mycoplasma genitalium contains 28 algorithmically-independent components incorporating work from over 900 sources. It accounts for interactions of the complete genome, transcriptome, proteome, and metabolome of the organism, marking a significant advancement for the field. [7] [8]

Most attempts at modeling cell cycle processes have focused on the broad, complicated molecular interactions of many different chemicals, including several cyclin and cyclin-dependent kinase molecules as they correspond to the S, M, G1 and G2 phases of the cell cycle. In a 2014 published article in PLOS computational biology, collaborators at University of Oxford, Virginia Tech and Institut de Génétique et Développement de Rennes produced a simplified model of the cell cycle using only one cyclin/CDK interaction. This model showed the ability to control totally functional cell division through regulation and manipulation only the one interaction, and even allowed researchers to skip phases through varying the concentration of CDK. [9] This model could help understand how the relatively simple interactions of one chemical translate to a cellular level model of cell division.

Projects

Multiple projects are in progress. [10]

See also

Related Research Articles

<span class="mw-page-title-main">Cell cycle</span> Series of events and stages that result in cell division

The cell cycle, or cell-division cycle, is the series of events that take place in a cell that causes it to divide into two daughter cells. These events include the duplication of its DNA and some of its organelles, and subsequently the partitioning of its cytoplasm, chromosomes and other components into two daughter cells in a process called cell division.

<span class="mw-page-title-main">Gene regulatory network</span> Collection of molecular regulators

A generegulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell. GRN also play a central role in morphogenesis, the creation of body structures, which in turn is central to evolutionary developmental biology (evo-devo).

<span class="mw-page-title-main">Systems biology</span> Computational and mathematical modeling of complex biological systems

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.

<span class="mw-page-title-main">Anaphase-promoting complex</span> Cell-cycle regulatory complex

Anaphase-promoting complex is an E3 ubiquitin ligase that marks target cell cycle proteins for degradation by the 26S proteasome. The APC/C is a large complex of 11–13 subunit proteins, including a cullin (Apc2) and RING (Apc11) subunit much like SCF. Other parts of the APC/C have unknown functions but are highly conserved.

<span class="mw-page-title-main">Cyclin-dependent kinase</span> Class of enzymes

Cyclin-dependent kinases (CDKs) are the families of protein kinases first discovered for their role in regulating the cell cycle. They are also involved in regulating transcription, mRNA processing, and the differentiation of nerve cells. They are present in all known eukaryotes, and their regulatory function in the cell cycle has been evolutionarily conserved. In fact, yeast cells can proliferate normally when their CDK gene has been replaced with the homologous human gene. CDKs are relatively small proteins, with molecular weights ranging from 34 to 40 kDa, and contain little more than the kinase domain. By definition, a CDK binds a regulatory protein called a cyclin. Without cyclin, CDK has little kinase activity; only the cyclin-CDK complex is an active kinase but its activity can be typically further modulated by phosphorylation and other binding proteins, like p27. CDKs phosphorylate their substrates on serines and threonines, so they are serine-threonine kinases. The consensus sequence for the phosphorylation site in the amino acid sequence of a CDK substrate is [S/T*]PX[K/R], where S/T* is the phosphorylated serine or threonine, P is proline, X is any amino acid, K is lysine, and R is arginine.

<span class="mw-page-title-main">Mathematical and theoretical biology</span> Branch of biology

Mathematical and theoretical biology, or biomathematics, is a branch of biology which employs theoretical analysis, mathematical models and abstractions of the living organisms to investigate the principles that govern the structure, development and behavior of the systems, as opposed to experimental biology which deals with the conduction of experiments to prove and validate the scientific theories. The field is sometimes called mathematical biology or biomathematics to stress the mathematical side, or theoretical biology to stress the biological side. Theoretical biology focuses more on the development of theoretical principles for biology while mathematical biology focuses on the use of mathematical tools to study biological systems, even though the two terms are sometimes interchanged.

G<sub>2</sub> phase Second growth phase in the eukaryotic cell cycle, prior to mitosis

G2 phase, Gap 2 phase, or Growth 2 phase, is the third subphase of interphase in the cell cycle directly preceding mitosis. It follows the successful completion of S phase, during which the cell’s DNA is replicated. G2 phase ends with the onset of prophase, the first phase of mitosis in which the cell’s chromatin condenses into chromosomes.

<span class="mw-page-title-main">Cell cycle checkpoint</span> Control mechanism in the eukaryotic cell cycle

Cell cycle checkpoints are control mechanisms in the eukaryotic cell cycle which ensure its proper progression. Each checkpoint serves as a potential termination point along the cell cycle, during which the conditions of the cell are assessed, with progression through the various phases of the cell cycle occurring only when favorable conditions are met. There are many checkpoints in the cell cycle, but the three major ones are: the G1 checkpoint, also known as the Start or restriction checkpoint or Major Checkpoint; the G2/M checkpoint; and the metaphase-to-anaphase transition, also known as the spindle checkpoint. Progression through these checkpoints is largely determined by the activation of cyclin-dependent kinases by regulatory protein subunits called cyclins, different forms of which are produced at each stage of the cell cycle to control the specific events that occur therein.

<span class="mw-page-title-main">Flux balance analysis</span>

Flux balance analysis (FBA) is a mathematical method for simulating metabolism in genome-scale reconstructions of metabolic networks. In comparison to traditional methods of modeling, FBA is less intensive in terms of the input data required for constructing the model. Simulations performed using FBA are computationally inexpensive and can calculate steady-state metabolic fluxes for large models in a few seconds on modern personal computers. The related method of metabolic pathway analysis seeks to find and list all possible pathways between metabolites.

Systems immunology is a research field under systems biology that uses mathematical approaches and computational methods to examine the interactions within cellular and molecular networks of the immune system. The immune system has been thoroughly analyzed as regards to its components and function by using a "reductionist" approach, but its overall function can't be easily predicted by studying the characteristics of its isolated components because they strongly rely on the interactions among these numerous constituents. It focuses on in silico experiments rather than in vivo.

<span class="mw-page-title-main">René Thomas (biologist)</span>

René Thomas (14 May 1928 - 9 January 2017 was a Belgian scientist. His research included DNA biochemistry and biophysics, genetics, mathematical biology, and finally dynamical systems. He devoted his life to the deciphering of key logical principles at the basis of the behaviour of biological systems, and more generally to the generation of complex dynamical behaviour. He was professor and laboratory head at the Université Libre de Bruxelles, and taught and inspired several generations of researchers.

<span class="mw-page-title-main">Cyclin-dependent kinase 6</span> Protein-coding gene in the species Homo sapiens

Cell division protein kinase 6 (CDK6) is an enzyme encoded by the CDK6 gene. It is regulated by cyclins, more specifically by Cyclin D proteins and Cyclin-dependent kinase inhibitor proteins. The protein encoded by this gene is a member of the cyclin-dependent kinase, (CDK) family, which includes CDK4. CDK family members are highly similar to the gene products of Saccharomyces cerevisiae cdc28, and Schizosaccharomyces pombe cdc2, and are known to be important regulators of cell cycle progression in the point of regulation named R or restriction point.

A series of biochemical switches control transitions between and within the various phases of the cell cycle. The cell cycle is a series of complex, ordered, sequential events that control how a single cell divides into two cells, and involves several different phases. The phases include the G1 and G2 phases, DNA replication or S phase, and the actual process of cell division, mitosis or M phase. During the M phase, the chromosomes separate and cytokinesis occurs.

Biological applications of bifurcation theory provide a framework for understanding the behavior of biological networks modeled as dynamical systems. In the context of a biological system, bifurcation theory describes how small changes in an input parameter can cause a bifurcation or qualitative change in the behavior of the system. The ability to make dramatic change in system output is often essential to organism function, and bifurcations are therefore ubiquitous in biological networks such as the switches of the cell cycle.

Mitotic exit is an important transition point that signifies the end of mitosis and the onset of new G1 phase for a cell, and the cell needs to rely on specific control mechanisms to ensure that once it exits mitosis, it never returns to mitosis until it has gone through G1, S, and G2 phases and passed all the necessary checkpoints. Many factors including cyclins, cyclin-dependent kinases (CDKs), ubiquitin ligases, inhibitors of cyclin-dependent kinases, and reversible phosphorylations regulate mitotic exit to ensure that cell cycle events occur in correct order with fewest errors. The end of mitosis is characterized by spindle breakdown, shortened kinetochore microtubules, and pronounced outgrowth of astral (non-kinetochore) microtubules. For a normal eukaryotic cell, mitotic exit is irreversible.

Virtual Cell (VCell) is an open-source software platform for modeling and simulation of living organisms, primarily cells. It has been designed to be a tool for a wide range of scientists, from experimental cell biologists to theoretical biophysicists.

<span class="mw-page-title-main">John J. Tyson</span> American mathematical biologist

John J. Tyson is an American systems biologist and mathematical biologist who serves as University Distinguished Professor of Biology at Virginia Tech, and is the former president of the Society for Mathematical Biology. He is known for his research on biochemical switches in the cell cycle, dynamics of biological networks and on excitable media.

Whi5 is a transcriptional regulator in the budding yeast cell cycle, notably in the G1 phase. It is an inhibitor of SBF, which is involved in the transcription of G1-specific genes. Cln3 promotes the disassociation of Whi5 from SBF, and its disassociation results in the transcription of genes needed to enter S phase.

The Novak–Tyson Model is a non-linear dynamics framework developed in the context of cell-cycle control by Bela Novak and John J. Tyson. It is a prevalent theoretical model that describes a hysteretic, bistable bifurcation of which many biological systems have been shown to express.

libRoadRunner is a C/C++ software library that supports simulation of SBML based models.. It uses LLVM to generate extremely high-performance code and is the fastest SBML-based simulator currently available. Its main purpose is for use as a reusable library that can be hosted by other applications, particularly on large compute clusters for doing parameter optimization where performance is critical. It also has a set of Python bindings that allow it to be easily used from Python.

References

  1. "The JJ Tyson Lab". Virginia Tech . Retrieved 2011-07-20.
  2. "The Molecular Network Dynamics Research Group". Budapest University of Technology and Economics. Archived from the original on 2019-10-30. Retrieved 2011-07-20.
  3. "Interactive Modeling of Biological Networks".
  4. "Helikar Lab - Members". Archived from the original on 2019-10-19. Retrieved 2016-02-15.
  5. Morris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA.. Logic-based models for the analysis of cell signaling networks. Biochemistry (2010) 49(15):3216–24.10.1021/bi902202q
  6. Helikar T, Kowal B, Madrahimov A, Shrestha M, Pedersen J, Limbu K, et al. Bio-Logic Builder: a nontechnical tool for building dynamical, qualitative models. PLoS One (2012) 7(10):e46417.10.1371/journal.pone.0046417
  7. http://covertlab.stanford.edu/publicationpdfs/mgenitalium_whole_cell_2012_07_20.pdf%5B%5D
  8. "Stanford researchers produce first complete computer model of an organism". 2012-07-19.
  9. Gérard, Claude; Tyson, John J.; Coudreuse, Damien; Novák, Béla (2015-02-06). "Cell Cycle Control by a Minimal Cdk Network". PLOS Comput Biol. 11 (2): e1004056. Bibcode:2015PLSCB..11E4056G. doi:10.1371/journal.pcbi.1004056. PMC   4319789 . PMID   25658582.
  10. Gershon, Diane (2002). "Silicon dreams in the biology lab". Nature. 417 (6892): 4–5. Bibcode:2002Natur.417....4G. doi:10.1038/nj6892-04a. PMID   12087360. S2CID   10737442.