Cellular model: Difference between revisions

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{{expert|Biology|reason=Large chunks of technical material here not placed into context|date=March 2022}}

[[ImageFile:Signal transduction pathways.svg|thumb|200px|Part of the [[Cell cycle]]]]

Creating a '''cellular model''' has been a particularly challenging task of [[systems biology]] and [[mathematical biology]].

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

It involves developing efficient [[algorithms]], [[data structures]], [[Biological data visualization|visualization]] and communication tools to orchestrate the integration of large quantities of biological data with the goal of [[computer modeling]].

Developing such models has been a task of [[systems biology]] and [[mathematical biology]]. It involves developing efficient [[algorithms]], [[data structures]], [[Biological data visualization|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 simulation]]s of [[cell (biology)|cellular]] subsystems, such as the [[metabolic network|networks of metabolites]] and [[enzyme]]s which comprise [[metabolism]], [[signal transduction]] pathways and [[gene regulatory network]]s.

It is also directly associated with [[bioinformatics]], [[computational biology]] and [[Artificial life]].

== Overview ==

It involves the use of [[computer simulation]]s of the many [[cell (biology)|cellular]] subsystems such as the [[metabolic network|networks of metabolites]] and [[enzyme]]s which comprise [[metabolism]], [[signal transduction]] pathways and [[gene regulatory network]]s to both analyze and visualize the complex connections of these cellular processes.

The eukaryotic [[cell cycle]] is very complex and is one of the most studied topics, since its misregulation leads to [[Cancer|cancers]]. It is possibly a good example of a mathematical model as it deals with simple calculus but gives valid results. Two research groups<ref>{{cite web|url=http://mpf.biol.vt.edu/lab_website/ |title=The JJ Tyson Lab|publisher=[[Virginia Tech]]|date= |accessdate=2011-07-20}}</ref><ref>{{cite web|url=http://www.cellcycle.bme.hu/ |title=The Molecular Network Dynamics Research Group|publisher=[[Budapest University of Technology and Economics]]}}</ref> 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).

The complex network of biochemical reaction/transport processes and their spatial organization make the development of a predictive model of a living cell a grand challenge for the 21st century.

==Overview==

The eukaryotic [[cell cycle]] is very complex and is one of the most studied topics, since its misregulation leads to [[cancer]]s.

It is possibly a good example of a mathematical model as it deals with simple calculus but gives valid results. Two research groups<ref>{{cite web|url=http://mpf.biol.vt.edu/lab_website/ |title=The JJ Tyson Lab|publisher=[[Virginia Tech]]|date= |accessdate=2011-07-20}}</ref><ref>{{cite web|url=http://www.cellcycle.bme.hu/ |title=The Molecular Network Dynamics Research Group|publisher=[[Budapest University of Technology and Economics]]}}</ref> 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 equation]]s 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 system|deterministic process]] (whereas a model describing a statistical distribution of protein concentrations in a population of cells is called a [[stochastic process]]).

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In a simulation, given a starting [[Array data structure|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 ofon 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 [[Fixed point (mathematics)|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]].

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==Molecular level simulations==

Cell Collective<ref>{{Cite web | url=http://cellcollective.org/ | title=Interactive Modeling of Biological Networks}}</ref> 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,<ref>{{Cite web | url=http://helikarlab.org/members.html | title=Helikar Lab - Members | access-date=2016-02-15 | archive-url=https://web.archive.org/web/20191019003831/http://helikarlab.org/members.html | archive-date=2019-10-19 | url-status=dead }}</ref>, 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 <ref>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

</ref><ref>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</ref>].

Model validation

The model was constructed using local (e.g., protein–protein interaction) information from the primary literature. In other words, during the construction phase of the model, there was no attempt to determine the local interactions based on any other larger phenotypes or phenomena. However, after the model was completed, verification of the accuracy of the model involved testing it for the ability to reproduce complex input–output phenomena that have been observed in the laboratory. To do this, the T-cell model was simulated under a multitude of cellular conditions and analyzed in terms of input–output dose–response curves to determine whether the model behaves as expected, including various downstream effects as a result of activation of the TCR, G-protein-coupled receptor, cytokine, and integrin pathways.<ref>Conroy BD, Herek TA, Shew TD, Latner M, Larson JJ, Allen L, et al. Design, Assessment, and in vivo Evaluation of a Computational Model Illustrating the Role of CAV1 in CD4 T-lymphocytes. Front Immunol. 2014;5: 599 doi: 10.3389/fimmu.2014.00599</ref>

The E-Cell Project<ref>{{Cite web | url=http://www.e-cell.org/ | title=E-Cell project}}</ref> aims "to make precise whole cell simulation at the molecular level possible".<ref>{{Cite web |url=http://www.e-cell.org/ecell/ |title=Archived copy |access-date=2010-11-09 |archive-url=https://web.archive.org/web/20101029020449/http://www.e-cell.org/ecell |archive-date=2010-10-29 |url-status=dead }}</ref>

[[CytoSolve]] - developed by [[Shiva Ayyadurai|V. A. Shiva Ayyadurai]] and C. Forbes Dewey Jr. of Department of Biological Engineering at the [[Massachusetts Institute of Technology]] - provided a method to model the whole cell by dynamically integrating multiple molecular pathway models. ."<ref>{{cite journal |last1=Al-Lazikani |first1=Bissan|last2=Banerji |first2=Udai | last3=Workman |first3=Paul |year=2012 |title= Combinatorial drug therapy for cancer in the post-genomic era|journal=Nature Biotechnology |volume=30 |issue= 7|pages=679–692 |doi= 10.1038/nbt.2284|url= |pmid=22781697|s2cid=205277160}}<!--|accessdate=14 February 2013 --></ref><ref>{{Cite journal| last1=Ayyadurai| first1=VA Shiva|first2=C. Forbes|last2=Dewey|journal=Cell Mol Bioeng.|date=March 2011|volume=4|issue=1|pages=28–45|doi=10.1007/s12195-010-0143-x|title=CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models|pmid=21423324|pmc=3032229}}</ref>

In the July 2012 issue of [[Cell (journal)|''Cell'']], a team led by [[Markus W. Covert|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.<ref>http://covertlab.stanford.edu/publicationpdfs/mgenitalium_whole_cell_2012_07_20.pdf{{dead link|date=November 2016 |bot=InternetArchiveBot |fix-attempted=yes }}</ref><ref>{{Cite web | url=http://news.stanford.edu/news/2012/july/computer-model-organism-071812.html | title=Stanford researchers produce first complete computer model of an organism| date=2012-07-19}}</ref>

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 phase|S]], [[M phase|M]], [[G1 phase|G1]] and [[G2 phase|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.<ref>{{Cite journal|title = Cell Cycle Control by a Minimal Cdk Network|journal = PLOS Comput Biol|date = 2015-02-06|pmc = 4319789|pmid = 25658582|pages = e1004056|volume = 11|issue = 2|doi = 10.1371/journal.pcbi.1004056|first1 = Claude|last1 = Gérard|first2 = John J.|last2 = Tyson|first3 = Damien|last3 = Coudreuse|first4 = Béla|last4 = Novák|bibcode = 2015PLSCB..11E4056G | doi-access=free }}</ref> 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.<ref>{{Cite journal | url=http://www.nature.com/naturejobs/2002/020627/full/nj6892-04a.html | doi=10.1038/nj6892-04a| pmid=12087360| title=Silicon dreams in the biology lab| journal=Nature| volume=417| issue=6892| pages=4–5| year=2002| last1=Gershon| first1=Diane| bibcode=2002Natur.417....4G| s2cid=10737442| url-access=subscription}}</ref>

*[http://cytosolve.com/ CytoSolve] - Commercial platform, possibly using MATLAB

*[http://www.synthecell.org/ Synthecell] - Experimental group

*[http://pubs.acs.org/doi/abs/10.1021/jp0302921 Karyote] - [[Indiana University]] - No longer active

*[http://www.e-cell.org/ E-Cell Project] - Last updated 2020

*[http://nrcam.uchc.edu/ Virtual Cell] - [[University of Connecticut Health Center]] - Simulation platform rather than a build a cell project

*[http://www.siliconcell.net/ Silicon Cell] - No longer active

*[http://simtk.org/home/wholecell WholeCell] - [[Stanford University]] - No longer active

*[http://mcell.org/ MCell] - [http://mmbios.pitt.edu/ National Center for Multiscale Modeling of Biological Systems (MMBioS)] - Active as of 2023

==See also==

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[[Category:Bioinformatics]]

[[Category:Systems biology]]

[[Category:Scientific modelingmodels]]