Theoretical studies of the chemotaxis of biological cells [Elektronische Ressource] / Bidisha Nandy

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• Biologie

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• Publié par: universitat_duisburg-essen
• Publié le: 01 janvier 2008
• Nombre de lectures: 23
• Langue: English
• Poids de l'ouvrage: 3 Mo

Extrait

Theoretical Studies
of the Chemotaxis of Biological Cells
Vom Fachbereich Physik
der Universit at Duisburg - Essen
(Campus Duisburg)
zur Erlangung des akademischen Grades eines
Doktors der Naturwissenschaften
genehmigte Dissertation von
Bidisha Nandy
aus
Kolkata, India
Prof. Dr. Artur Baumg artnerReferent:
Prof. Dr. Dietrich WolfKorreferent:
Tag der mundlic hen Prufung: 17. M arz, 2008Abstract
Living organisms contain thousands of interacting complex networks of macromolecules,
and there are very few tools to understand them. The present thesis is an attempt to elu-
cidate some aspects of two interacting networks which are responsible for the coordinated
chemotactic locomotion of a cell. One of the networks, the directional sensing network,
is responsible for the reception of external molecular signals to which the cell is exposed.
This network also transforms the signal into an internal one (‘response’), which
is ampli ed and used by the the second network, the polarization network, to tune and
to guide the cellular motor which propells the cell using its polymerizing cytoskeleton.
A stochastic model, a type of cellular automata model, has been developed and employed
in order to address various questions. For example, how an external signal with a
weak spatial gradient can be translated by molecules into a strongly ampli ed and
localized response, and how this response regulates the local activity and the spatial
distribution of the actin cytoskeleton which controls the velocity and the direction of
the cell’s movements. By using a stochastic model, which includes explicit particles,
the investigations provide a link to known approaches in theoretical physics, as there
are, e.g., cooperative phenomena in many-body problems and space-time correlations in
nonlinear dynamics. Since the present study is the rst attempt employing a stochastic
model, as compared to previous kinetic and deterministic models for chemotaxis, the
achieved results contain new and important information.
It is shown, among others, that the ampli cation of the response exhibits a transition
as function of the gradient of the signal. The spatial localization of the response, repre-
sented by the distribution of activated PIP molecules along the cell membrane, depends
on the gradient and the maximum of the signal. Using the ‘Local Exciter and Global In-
hibitor’ (LEGI) model, proposed recently by other researchers for the directional sensing
network, it is shown how the spacial-temporal distributions of the two types of inhibitor
and exciter molecules are correlated to the ampli cation of the response in terms of
activated PIP molecules. The major advantage of the present approach, however, is the
combination of a particle-based LEGI network with a particle-based polarization net-
work, where the latter includes explicitly linear and branching polymerization of actin
lamen ts. Taking the two regulatory networks, including their signaling molecules and
the actin molecules together, a minimal cell model has been developed, where the cell
membrane is represented by a two-dimensional exible ring polymer. During Monte
Carlo simulations of this model, the chemotactic motion of the cell could be monitored.
The analysis of the trajectories shows that the magnitude of the drift velocity can be
tuned by the combination of the signal gradient, the signal maximum and the signal-
mediated polymerization of the lamen ts. This explains the experimentally known high
sensitivity of chemotactic cell to weak external signal gradients.Contents
1 Introduction 7
2 Biological Background of Cell Motility 11
2.1 Introduction: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Biological importance of cell motility: . . . . . . . . . . . . . . . . . . . . 11
2.3 Types of cell movements: . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 The cytoskeleton: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4.1 Microtubules: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4.2 Intermediate Filaments: . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.3 Micro lamen ts: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 Actin lamen ts: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.6 The treadmilling of actin lamen ts: . . . . . . . . . . . . . . . . . . . . . 20
2.7 Actin binding proteins: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.7.1 Thymosin beta 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.7.2 Pro lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.7.3 ADF/co lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.7.4 Gelsolin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.7.5 Capping protein, cap Z . . . . . . . . . . . . . . . . . . . . . . . . 24
2.7.6 Arp2/3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.7.7 WASP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.8 The ground state of the system : . . . . . . . . . . . . . . . . . . . . . 26
2.9 A probable protein regulatory network for signal transduction: . . . . . 26
2.9.1 Membrane receptor . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.9.2 G proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.9.3 3 ’ Phosphoinositides . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.9.4 3 ’ Phosphorylation by PI3K . . . . . . . . . . . . . . . . . . . . . 29
2.9.5 3 ’ Dephosphorylation by PTEN . . . . . . . . . . . . . . . . . . . 30
2.9.6 PH Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.9.7 Downstream e ectors:A CA . . . . . . . . . . . . . . . . . . . . . . 31
2.9.8 Chemotactic behavior . . . . . . . . . . . . . . . . . . . . . . . . 31
2.10 Some locomoting cells : . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.10.1 Keratocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.10.2 Leukocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.10.3 Amoebae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3Contents
2.10.4 Dictyostelium Discoideum . . . . . . . . . . . . . . . . . . . . . . 34
2.10.5 The neural growth cone . . . . . . . . . . . . . . . . . . . . . . . 34
2.10.6 Listeria Monocytogenes . . . . . . . . . . . . . . . . . . . . . . . 35
3 Numerical Method 37
3.1 Introduction: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 Metropolis Monte Carlo algorithm: . . . . . . . . . . . . . . . . . . . . . 38
3.3 Some discussion on Lattice MC method: . . . . . . . . . . . . . . . . . . 40
3.4 Membrane dynamics: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.5 Actin dynamics: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.6 Polymerization dynamics: . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.7 Nucleation dynamics: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.8 Summary: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4 Some General features of cell motility 49
4.1 Introduction: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 Model description: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.1 Membrane: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.2 G-actin: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.3 F-actin: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.4 Actin-associated proteins: . . . . . . . . . . . . . . . . . . . . . . 50
4.3 Probability and reaction rates: . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4 Persistent random walk and lamen t polarization . . . . . . . . . . . . . 53
4.5 Dynamic instability: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.6 Extracellular signal and cell motion: . . . . . . . . . . . . . . . . . . . . . 57
4.7 Summary: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5 Response To Uniform Homogeneous Signals in Stochastic LEGI Models 61
5.1 Introduction: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 Model description: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.3 E ect of density: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.4 Global ampli cation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.5 Binding site and di usion coe cien t: . . . . . . . . . . . . . . . . . . . . 70
5.6 Global adaptation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.7 Adaptation and di usion coe cien t : . . . . . . . . . . . . . . . . . . . . 72
5.8 Cell’s random movement: . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.9 Summary: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6 Response To Inhomogeneous Signals 77
6.1 Introduction: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.2 Homogeneous gradient of cAMP: . . . . . . . . . . . . . . . . . . . . . . 77
6.2.1 Ampli cation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4Contents
6.2.2 Molecular mechanism of ampli cation . . . . . . . . . . . . . . . . 85
6.2.3 Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.3