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PLoS Comput Biol. 2009 August; 5(8): e1000466.
Published online 2009 August 21. doi:  10.1371/journal.pcbi.1000466
PMCID: PMC2715871

Investigating CTL Mediated Killing with a 3D Cellular Automaton

Rob J. De Boer, Editor

Abstract

Cytotoxic T lymphocytes (CTLs) are important immune effectors against intra-cellular pathogens. These cells search for infected cells and kill them. Recently developed experimental methods in combination with mathematical models allow for the quantification of the efficacy of CTL killing in vivo and, hence, for the estimation of parameters that characterize the effect of CTL killing on the target cell populations. It is not known how these population-level parameters relate to single-cell properties. To address this question, we developed a three-dimensional cellular automaton model of the region of the spleen where CTL killing takes place. The cellular automaton model describes the movement of different cell populations and their interactions. Cell movement patterns in our cellular automaton model agree with observations from two-photon microscopy. We find that, despite the strong spatial nature of the kinetics in our cellular automaton model, the killing of target cells by CTLs can be described by a term which is linear in the target cell frequency and saturates with respect to the CTL levels. Further, we find that the parameters describing CTL killing on the population level are most strongly impacted by the time a CTL needs to kill a target cell. This suggests that the killing of target cells, rather than their localization, is the limiting step in CTL killing dynamics given reasonable frequencies of CTL. Our analysis identifies additional experimental directions which are of particular importance to interpret estimates of killing rates and could advance our quantitative understanding of CTL killing.

Author Summary

The immune response mediated by cytotoxic T lymphocytes (CTLs), which kill infected cells, is thought to be essential to control viral infections. Experiments offer data which allow one to address the efficacy of this cell population in vivo and to estimate characterizing parameters. However, it is unclear which mathematical description reflects the experimental situation best and leads to reliable parameter estimates that quantify CTL efficacy. We simulate the spatial interaction of CTLs and infected cells in a 3-dimensional computer model to examine different mathematical descriptions of the experimental situation, independently of experimental data. Thereby we find an appropriate mathematical term to describe the killing process. Estimates obtained so far describe CTL efficacy on a population level. By varying the individual properties of simulated CTLs, such as the velocity, we find that the time a CTL needs to kill an infected cell is probably the key factor limiting CTL killing efficacy. Our analysis identifies additional experimental directions which could advance our quantitative understanding of CTL killing for different diseases.

Introduction

Cytotoxic T lymphocytes (CTL) are some of the most important cells of our immune system. They are particularly important against viral infections or tumours. They recognize infected cells by scanning their surfaces for peptide-MHC-I complexes which present peptide fragments sampled from the cytoplasm. These complexes can tell the CTL if the cell is infected or not. Once activated and primed for a specific peptide-MHC-I complex, CD8+ T cells differentiate into effector CTL, which are able to lyse infected cells. After an infection is cleared, some specific CTL may persist as memory cells.

Immunologists are interested in quantifying the efficacy of CTL in vivo. An appropriate measure of CTL efficacy would allow us to disentangle quantitative from qualitative aspects of the CTL response: For example, such a measure should tell us whether a memory CTL response is less efficacious than an effector CTL response because there are fewer cells, or because individual memory CTL do not perform as well as effector cells. A measure of CTL efficacy represents the first step in predicting if CTL responses will be able to control an infection, and in quantifying the selection pressure CTL responses exert on the pathogen population. This selection pressure may lead to immune escape where the virus evolves to become mainly undetected by the actual immune response [1],[2]. Rates which determine how fast CTL lyse infected cells are already estimated for HIV-I in vitro [3] and indirectly via the selective advantage of escape variants in vivo [4].

The best experimental data for the estimation of the CTL efficacy in vivo so far originate from the in vivo CTL killing assay [5],[6]. In this assay, cells are prepared to display LCMV-peptides on their MHC-I molecules. The cells are then transferred into mice which harbour CTL specific for these LCMV-peptides. It is known that the transferred cells migrate to the spleen where they are targeted by CTL. These cells are mostly located either in the red pulp or in the T cell-zones (perioarteriolar lymphoid sheaths (PALS)) depending on the stage of infection [7]. While effector CTL preferentially accumulate in the red pulp, memory CTL are mostly located in the PALS. Some time after the transfer, the levels of target cells are determined in the spleen. To estimate CTL efficacy, Regoes et al. [8] and Yates et al. [9] proposed a mathematical model that takes into account the migration of target cells into the spleen, and their subsequent killing by CTL. Fitting this model to in vivo CTL killing data, we obtained a killing rate constant An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e001.jpg, and proposed this constant as a measure for CTL efficacy. We found differences between killing rate constants of effector and memory CTL, as well as for immunodominant and -subdominant epitopes (see Table 2 in [9]).

In these previous studies, we intended to compare the efficacy of distinct CTL populations whose levels differ. Therefore we assumed a mass-action killing term to disentangle quantitative from qualitative aspects of CTL killing. However, the validity of the mass-action assumption is uncertain. Furthermore, it is unclear how the killing rate constant in our mathematical model, which describes CTL killing on the level of the cell populations involved, is related to properties of individual CTL. For example, how does CTL velocity or the time needed to kill a cell influence the estimate of the killing rate constant?

To address these questions we simulate the dynamic inside the spleen or PALS, respectively with a three-dimensional cellular automaton (CA) [10],[11]. A CA is an individual-based computer simulation of a dynamical system on a lattice (in our case a three dimensional one). This method allows us to identify a more appropriate mathematical description of CTL killing than the simple mass-action term. Additionally, by generating in vivo CTL killing data for different scenarios, we are able to relate the properties of individual cells to the population dynamics of the system.

We find that there is a parameter regime in which the behaviour of our CA model of CTL killing in the spleen is consistent with data obtained by two-photon microscopy [12][15]. Further, we find that the most appropriate mathematical description of CTL killing is linear in the target cell levels, and a saturating function of the CTL levels. However, fitting a mathematical model with such a saturating killing term does not improve the fit to the original in vivo CTL killing data consistently.

Studying the influence of single cell properties on our killing rate estimates we find that one specific experimental detail, which concerns the fate of CTL-target cell conjugates after splenectomy, is of particular importance to be able to interpret the population-level killing rate constants in terms of single cell efficacy. Nevertheless, given the CTL frequencies observed experimentally, the killing rate constant is mainly determined by the time a CTL needs to kill its target, and not the CTL's velocity.

Results

A spatial model for the T cell-zone of the spleen

The spleen is the secondary lymphoid organ which surveys the blood for foreign antigen. It consists of red pulp, which is a site of red blood cell destruction and comprises roughly 80% of the splenic volume, interspersed with lymphoid regions (white pulp). While most of the blood will bypass the lymphoid regions and remain in the direct circulation, around ten percent of the cells will diffuse through the T cell-zone (PALS) [16]. During this passage the cells are under constant surveillance by T lymphocytes.

We simulate the population dynamics of the cells in the PALS as a cellular automaton. A cellular automaton allows us to investigate the impact of individual cell properties and spatial aspects on the dynamics. Into our simulation model, we incorporate target cells, target-cell-specific CTL, splenocytes, and a limited number of large cells which correspond to dendritic cells or macrophages. In addition, we include the reticular network (RN), which defines the anatomical structure of the spleen, as well as some free space (see Fig. 1). For a detailed description of the automaton see Materials and Methods.

Figure 1
Snapshots of the simulated 3D cellular automaton.

We first simulate the specific CTL without target cell interaction to characterize their behaviour with regard to experimental observations. The simulated CTL perform a random walk (see Fig. 2B, Video S1 in the Supporting Material) consistent with observations made in lymph nodes and the spleen based on in vivo imaging techniques [17][21].

Figure 2
Velocity and motility characteristics of simulated CTL.

We are able to manipulate the motility of our simulated CTL through the rules of movement. We adjusted these rules of movement such that they display a mean velocity, velocity fluctuations, and a motility coefficient largly consistent with observations in vivo.

Miller et al. [14],[21] measure an average velocity for T cells of about An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e007.jpg in lymph nodes. It is thought that the velocity is in the range of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e008.jpg [22]. For the spleen, T cell velocities are observed which are slightly slower, even correcting for differences in the observation method [20], but comparable to those found in lymph nodes [19]. We mainly use a parametrization where the simulated CTL migrate with an average velocity of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e009.jpg.

In Fig. 2A we show the velocity fluctuations of six simulated CTL for this parametrization chosen at random for a time period of 100 min. The amplitude of the velocity fluctuation of the simulated CTL agrees with experimental observations (e.g. [12]). However, the velocity fluctuations are less rugged than those observed in experiments (compare to Fig. 5 in [12]). This is due to the discreteness of space in our cellular automaton, i.e. cells can not be arbitrarily displaced but have to occupy a node in the lattice.

As stated earlier, the simulated CTL perform a random walk. This can be seen from a projection of their normalized tracks (Fig. 2B) as well as from the relation between their mean displacement and the square root of time (Fig. 2C). The discreteness of space also affects the random walk characteristics of simulated CTL. As CTL have to “move” on given edges, there is only a discrete number of turning angles An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e010.jpg available. As cell movement involves the restructuring of the actin-filament network in the cytoskeleton [23], cells will prefer small turning angles. Therefore, in the simulation, they are programmed to preferentially choose An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e011.jpg per move. In the absence of killing and given a mean velocity of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e012.jpg, the simulated CTL show a mean turning angle of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e013.jpg where the turning angle was measured every minute. This slightly increases if we include killing activity (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e014.jpg). The distribution of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e015.jpg (see Fig. S1) differs from those observed experimentally [13]. This is provoked by the fact that the motility of simulated CTL is only affected by environmental conditions. CTL do not change their moving direction An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e016.jpg as frequently which leads to a low mean turning angle.

A second variable to characterize cell movement is the motility coefficient, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e017.jpg. Given standard parametrization, the motility coefficient of simulated CTL is approximately An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e018.jpg (see Fig. 2C, blue line), which is slightly above the range of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e019.jpg observed experimentally for T cell movement [12][15],[22]. For other parametrizations, which we consider in this paper, the motility coefficient is in the range An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e020.jpg.

The mean velocity as well as the motility of CTL decreases in the presence of CTL-target cell interaction (Fig. 3) which is also observed experimentally in the spleen [19]. For CTL-target cell interaction, target cells will appear in the cellular automaton with a certain rate and are killed after encountered by CTL (see Material and Methods for details). In our simulations, the mean CTL velocity decreases exponentially in the killing duration An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e021.jpg with a rate constant of approximately −0.012 min−1, given a fixed CTL concentration. In Fig. 3B we show the change in the mean displacement per square root of time for different values of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e022.jpg in comparison to the mean displacement of simulated CTL in the absence of killing. In all cases, CTL velocity was fixed to An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e023.jpg. Given a killing duration of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e024.jpg, the motility coefficient An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e025.jpg decreases from An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e026.jpg to An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e027.jpg. The motility coefficient includes only 15% of the value measured in the absence of killing, if we assume a killing duration of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e028.jpg. The decrease of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e029.jpg is linear in An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e030.jpg.

Figure 3
Changes in CTL velocity and motility due to killing activity.

Is the mass-action killing term appropriate?

In previous studies [8],[9], it is assumed that the rate at which target cells are killed depends linearly on the frequency of the CTL, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e039.jpg, and the frequency of the targets, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e040.jpg, in the spleen. Such a dependence is commonly referred to as mass-action hypothesis. However, the mass-action assumption may be inaccurate if the system is not well-mixed and the dynamics is spatially confined. In addition, the fact that CTL cannot seek for target cells while bound in a conjugate may lead to deviations from a mass-action killing term.

To address the question whether the mass-action hypothesis appropriately describes the killing dynamics given spatial confinements, we initialized the cellular automaton with different combinations of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e041.jpg and the starting target cell frequency An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e042.jpg. The CTL frequency An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e043.jpg ranges from 0–20% of simulated cells, which covers the frequencies observed for dominant and subdominant effector and memory responses (Tab. 1). The average velocity of CTL, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e044.jpg, was fixed and the killing duration was defined by An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e045.jpg, in agreement to experimental observations [24]. The loss of target cell frequency, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e046.jpg, was calculated by

equation image
(1)

Fig. 4A shows linearity of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e048.jpg in An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e049.jpg for different levels of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e050.jpg. In contrast, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e051.jpg is not linear in An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e052.jpg (Fig. 4B), but saturates for high levels of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e053.jpg.

Figure 4
Change in the loss of target cell frequency, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e054.jpg.
Table 1
Estimates of killing rates based on a model with a saturating killing term for in vivo CTL killing data.

As an improvement over the mass-action killing term, we therefore propose the following relationship between An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e071.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e072.jpg:

equation image
(2)

Hereby, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e074.jpg denotes the maximum killing rate at high levels of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e075.jpg, and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e076.jpg denotes the CTL frequency at which the killing rate is at half of the maximum. Such terms have been suggested previously [25][27]. The saturation in the CTL frequency was observed independent of the density of the reticular network (varying the volume occupied by reticular network from 0–50% of the simulated space, data not shown). Fitting Eq. (2) to the data generated in our simulations yield An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e077.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e078.jpg. The estimate of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e079.jpg is slightly above the frequency of a subdominant effector or memory CTL response (Tab. 1, see An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e080.jpg or An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e081.jpg, respectively for GP276). This estimate of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e082.jpg suggests that — if the parametrization of our cellular automaton agrees with the situation in vivo — the saturating term Eq. (2) should be preferred over a mass-action killing term for in vivo killing data with CTL frequencies An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e083.jpg.

The result that a killing term which saturates in the CTL frequency is more appropriate stays valid if we include multiple time points at which we calculate the loss in target cell frequency An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e084.jpg. By this, we additionally include the search process and relate to the fact that CTL which are bound in conjugates are prevented from hunting other target cells.

New estimates of CTL killing rate constants

To account for the non-linear relationship between the loss of target cells and CTL frequencies, we substituted the mass-action killing term in our basic model (see Materials and Methods) by one that saturates in An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e085.jpg. In this case our equation is extended as follows:

equation image
(3)

Eq. (3) was fitted to the in vivo CTL killing data from Barber et al. [5] as described previously [8].

We obtain a reduction in the residual sum of squares compared to the previous model with a mass-action killing term, Eq. (8). This reduction is significant (as assessed by an An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e087.jpg) for the effector and memory CTL response against the NP396-LCMV epitope given a significance-level of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e088.jpg (see Tab. 1). However, even though a saturating killing term does not significantly improve the fit of our killing model to all the data, our simulations strongly suggest that a saturating term is more appropriate to describe the killing dynamics.

We therefore use a saturating term in the following analyses. Estimates for An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e089.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e090.jpg as well as An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e091.jpg for the An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e092.jpg are given in Table 1.

Influence of single cell behaviour

The saturating term An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e093.jpg and the term An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e094.jpg respectively, mathematically describe the reduction in the target cell population due to their interaction with the CTL population. How does this population-level description of the killing dynamics relate to properties at the individual-cell level, such as the velocity of CTL or the time it takes to kill a target cell?

We find that one specific experimental detail is of particular importance for the interpretation of population-level parameters in terms of the single cell properties. This experimental detail concerns the fate of target cells in conjugates after the splenectomy. It is unknown if conjugates are simply broken up by the preparation of the spleen for the cell sorter, or if, during this preparation, killing of target cells that are bound to CTL continues. In any case, conjugates are not observed when the splenocytes are analysed by fluorescent-activated cell sorting (FACS).

Influence of CTL velocity

We perform simulations with varying CTL velocities and killing durations An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e095.jpg. The CTL velocity is indirectly manipulated via the rules of cell movement in our cellular automaton.

We generate data with the same structure as Barber et al. [5] (see Materials and Methods). We choose a frequency of target-specific CTL with An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e096.jpg which is orientated at the CTL frequencies observed ([5] and Tab. 1). While the population of target-specific CTL is kept constant according to the assumptions made in the analysis [8],[9], target cells as well as control cells appear in the cellular automaton with a certain rate out of a restricted pool of cells (see Fig. 5 and Video S2 for a time course of one simulation). Each simulation represents the dynamics of CTL killing in a single mouse followed over 300 minutes. We perform 36 simulations for each combination of CTL velocity and killing duration. A bootstrap analysis with 1000 replicates is performed by sampling the number of free and bound target cells, control cells and CTL in 6 randomly chosen “mice” per indicated time-point (at either 15, 30, 60, 90, 120 or 240 minutes) per replicate.

Figure 5
Cell density dynamics for one simulation.

We consider two measures of CTL velocity: (i) An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e099.jpg, the mean “hunting velocity” based only on CTL that are not bound to target cells, and (ii) An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e100.jpg, the mean velocity of all CTL regardless of whether they are bound to target cells or not.

In Fig. 6A, we plot the killing parameter An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e101.jpg versus the hunting velocity An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e102.jpg. Hereby, we assume that target cells which are bound to CTL remain alive and are counted by the cell sorter. Estimates of Spearman's rank correlation coefficient (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e103.jpg) show a weak correlation between An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e104.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e105.jpg (see Tab. 2). For the mean velocity An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e106.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e107.jpg, we observe a correlation coefficient of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e108.jpg (see Fig. 6B). The finding of a weak influence of the CTL velocity on An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e109.jpg is further corroborated if we analyze the lifespan, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e110.jpg, of a single target after it appears in the cellular automaton. For a killing duration of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e111.jpg, the average lifespan An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e112.jpg, and 80% of all target cells are killed 20 min after their appearance regardless of the CTL velocity. Similar observations are made for the other levels of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e113.jpg. Most target cells are recognized immediately after their appearance in the cellular automaton.

Figure 6
Plots of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e114.jpg, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e115.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e116.jpg against the estimates of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e117.jpg.
Table 2
Correlation between killing rate constants and properties of simulated CTL.

Assuming that all target cells bound to CTL are killed before the FACS analysis leads to slightly different conclusions regarding the influence of CTL velocity on the parameter estimates that describe the killing dynamics. We find a stronger correlation between An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e144.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e145.jpg (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e146.jpg), and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e147.jpg (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e148.jpg) (see Fig. 6D–E).

The correlation between the CTL velocities and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e149.jpg are analogous but inverse to those observed in An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e150.jpg.

Influence of killing duration An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e151.jpg

The second property of a CTL, which might influence the estimate of our killing parameters An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e152.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e153.jpg, is the time a single CTL needs to kill a target cell, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e154.jpg. This time includes the establishing of a contact between the CTL and the target cell, the actual killing process and the time required for the detachment of the CTL. Although the conjugate formation and lysis of the target cell can happen very rapidly within 10–25 min, CTL might remain in contact to dead target cells for even longer periods [24],[28],[29]. We choose five different values of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e155.jpg in the range of 5–60 min.

In Fig. 6C, we plot estimates of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e156.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e157.jpg, again based on the assumption that free and bound target cells are counted in the FACS analysis. This correlation is the strongest of all the variables: An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e158.jpg. If we assume that only free target cells are counted, we find a slightly lower, but still high, correlation coefficient of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e159.jpg (Fig. 6F). During a simulated time period of 300 min, a CTL kills on average 2–7 target cells depending on the velocity and the killing duration. Thereby, the occurence of multiple killing, where one CTL kills several target cells simultaneously, is rather rare (see Fig. S2). Therefore, we think that this mechanism does not affect our conclusions.

For all the analyses above, we simulate data that reflects the dataset of Barber et al. [5] in terms of number of mice and time-points sampled. To generate such data, we produce detailed time-series of target cells and control cells in each simulation, and then sample only at one time-point. If we use the entire time-series we obtain from a single simulation to estimate An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e160.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e161.jpg, we eliminate the variation in estimates across different simulation runs. With this approach, our conclusions are qualitatively equivalent to those above.

The effect of varying CTL velocities and killing durations on An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e162.jpg is rather weak in terms of absolute values. The difference which is observed between the estimates of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e163.jpg based on simulated and experimental data (Fig. 6 and Tab. 1) might be explained by the varying CTL levels between mice in the experiment which affects the estimation of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e164.jpg and thus An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e165.jpg. We performed the same analysis with a mass-action term in the killing using either Eq. (8) or Eq. (9) to estimate the per-capita killing rate An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e166.jpg. The estimates of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e167.jpg are in the same order of magnitude as in the experiment if we assume that the killing in conjugates does not continue after splenectomy. Given realistic CTL velocities and killing durations An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e168.jpg, we obtain estimates for An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e169.jpg which are close to those estimated based on experimental data [8],[9]. The pattern for An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e170.jpg against An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e171.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e172.jpg stays the same as for An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e173.jpg (see Fig. 7 and Fig. S3). The influence of the velocity on the estimates increases given very low killing durations but depends again on the way the target cells are counted. However, the difference in terms of absolute values is more apparent in An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e174.jpg than in An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e175.jpg.

Figure 7
Plots of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e176.jpg, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e177.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e178.jpg against the estimates of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e179.jpg.

These general results are not affected by several changes in our assumptions to simulate CTL-target cell interaction. We assumed that a CTL will always recognize a neighbouring target cell and that both cells will immediately stop movement after forming a conjugate. The main influence of the killing duration An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e183.jpg on the killing rate estimates is still observed if we decrease the probability of a CTL to recognize a target cell to 0.5 or allow conjugates to continue migration for a while as observed experimentally [24] (data not shown). This observation is also robust to changes in the density of the reticular network varying the density up to 50% of the simulated volume.

Discussion

Quantifying CTL efficacy based on data of an in vivo CTL killing assay requires models which capture the anatomical complexities inside the mice and reflect experimental conditions. Previously, we proposed a population-level model [8],[9], in which the killing dynamics was seperated into migration to and killing in the spleen. However, uncertainties remained concerning the most appropriate mathematical description of the killing term and how to interpret the population-level killing parameter. To address these questions, we constructed a 3-dimensional cellular automaton model of a T cell zone in the spleen.

Unlike previous studies [12],[30], we do not model the biophysical processes involved in cell movement. Rather, we impose simple rules of directed movement and position swapping. We find that our cellular automaton model can recapitulate experimentally observed CTL motility. This has recently been established in a more general context by Bogle and Dunbar [31].

We find that the most appropriate description of the killing term is linear in the frequency of target cells and saturates in the frequency of CTL. This saturation is observed irrespective of the various densities of the reticular network we considered (0%–50%). This is both expected and surprising: Expected, because it is well established that killing terms will saturate in CTL frequency [25],[32] as CTL, while bound to a target cell, are prevented from hunting and killing others. Surprising, because our cellular automaton is spatially structured, and we expected this spatial structure, which was not considered in previous work [25],[32], to percolate into the most appropriate killing term. Additionally, the conditions, under which the saturating term is theoretically derived, namely that there conjugates are in a quasi steady state, are not fulfilled in our simulations.

The main aim of our study was to identify the most appropriate killing term, rather than to obtain new estimates for killing. Nevertheless, we re-analyzed the data by Barber et al. [5] using a model with a saturating killing term. Using a saturating killing term improves the fit significantly for the immunodominant NP396-epitopes in the effector and memory response. This suggests that the CTL levels specific for NP396 are in the saturating regime. The improvement of the fit in the case of the effector NP396 response is consistent with the critical CTL frequency An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e184.jpg which we derived from our simulations. (Above this critical CTL frequency the killing rate is saturated.) The improvement of the fit in the case of the memory NP396 response is also consistent with the critical CTL frequency if we factor in the finding that memory CTL are mostly located in and around the T cell zones [7]. That means that, in the case of memory responses, it is more appropriate to compare the critical CTL frequency with the proportion of epitope-specific CTL in the pool of CD8+ T cells (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e185.jpg in Tab. 1), rather than the entire spleen (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e186.jpg in Tab. 1).

The killing term allows us to estimate populational-level parameters which quantify CTL efficacy. By varying the velocity of CTL and the time a CTL needs to kill a target cell, we were able to determine the influence of these single-cell properties on the population-level killing parameters. We based our analysis on a CTL frequency of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e187.jpg which is in the range of the frequencies observed experimentally ([5] and Tab. 1). We find that the population-level parameters are mostly affected by the killing duration An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e188.jpg. The longer An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e189.jpg, the lower the killing rate constant An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e190.jpg or An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e191.jpg. The impact of the CTL velocity on the killing rate constant An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e192.jpg or An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e193.jpg varies depending on our assumptions regarding the fate of target cells in conjugates after the splenectomy. The impact of CTL velocity is weak if we assume that target cells in conjugates are still alive and counted by the cell sorter. If we assume that killing in conjugates continues and, therefore, target cells bound in conjugates are not detected by the cell sorter, the impact of CTL velocity is stronger. To clearly separate the relative effects of killing duration and CTL velocity it is necessary to determine the fate of target cells in conjugates during the in vivo killing assay.

Yates et al. [9] showed a significant difference between estimates of the killing rate constant An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e194.jpg in effector and memory responses. Effector CTL are more efficacious than memory CTL indicated by a higher value of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e195.jpg. Our analysis suggests that the difference between effector and memory CTL can be explained by a difference in the time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e196.jpg which a single CTL needs to kill a target cell. This hypothesis is in line with the observation that memory CTL store intermediate or low level of perforin and granzyme in comparison to effector CTL, which could prolong the killing process [33]. The difference between the killing rate constants for NP396- and GP276-specific CTL could be explained by different binding rates between the T cell receptor of the CTL and the peptide-MHC complex of the target cells. It is known that the binding of the T cell receptor specific for NP396 to NP396-MHC is stronger than that of the T cell receptor specific for GP276 to GP276-MHC [34]. We find that lower probabilities of recognition, which correspond to low binding rates, lead to lower estimates of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e197.jpg in our simulations (see Fig. S4). To test these hypotheses about the killing process experimentally, one could combine in vivo CTL killing assays with two-photon microscopy as it is performed for the analysis of T cell activation [12][15],[22],[24].

In a recent study, Ganusov and De Boer [35] calculated target cell half-lives using a mathematical model that did not control for differences in CTL levels. The reason that these authors neglected CTL levels was that the exact form of the killing term is unknown. However, to predict the protection afforded by CD8+ T cell responses it is necessary to extrapolate the efficacy of a CTL population of varying size. Further, to decide if effector CTL are more efficacious than memory CTL, or CTL in acute infections are more efficacious than CTL in persistent infections, it is necessary to disentangle quantitative from qualitative aspects. Therefore, the dependence of the killing rate on the level of CTL can, in the long run, not be ignored.

The point of the present study was to derive a more appropriate mathematical description of the killing term from a model that incorporates more of the spatial complexities of the spleen as previous population level descriptions. Based on our analysis, we showed that a killing term which saturates in the CTL frequency would be more appropriate to describe the experimental situation.

There is no clear answer to the question which of the different killing rates for LCMV epitopes presented in this study and published so far [8],[9] should be preferred. Much more detail about the killing process is required to clearly favour one estimate. Our study is a first step to improve the estimation of per-capita killing rates based on a population-level and to enhance their interpretation in terms of single cell properties.

Materials and Methods

The cellular automaton

We use a three-dimensional lattice of nodes and edges to simulate the T cell zone of the spleen. Recent analysis showed the suitability of a lattice based approach to simulate T cell movement [31],[36]. We define periodic boundary conditions in which a cell leaving the simulated space on the one side of the lattice will reappear at the opposite side. Each node of the lattice represents a cell or a part of a cell. We consider target cells, target-specific CTL and splenocytes, which occupy a single node. Macrophages and dendritic cells are larger than CTL and have an average diameter of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e198.jpg [12],[37]. These cells are modelled as occupying four nodes connected in no regular shape. The shapes are not stable, we only require that each cell-part has at least one other part of the cell as its direct neighbour. Some nodes are occupied by reticular network which does not change position over time and represents spatial obstacles to moving cells. Lastly, a few nodes are left unoccupied and define free space.

Each node has 26 neighbours. As cell movement requires a complex restructuring of the actin cytoskeleton [23], each cell in our cellular automaton is assumed to have a preferred moving direction An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e199.jpg. The direction can change upon encounter of another CTL, a target cell, or reticular network (see below). A cell will only have a moving direction of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e200.jpg while it is bound in a conjugate. The cellular automaton was implemented in the C++ programming language.

Cellular automaton - scaling and initialization

We consider a lattice of 30×30×30 nodes, which makes 27000 nodes in total. To set the spatial scale of the simulation we assume that each edge of the lattice has a size of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e201.jpg, the average diameter of a T cell [38],[39]. The reticular network occupies ≈4500 nodes (≈17% of the space). To achieve the actual structure of a network, we seed the lattice at random nodes from which the network grows until the assigned volume is occupied. As the spleen is a densely packed organ, free space is set to ≈1300 nodes (≈5%). We consider only a small number of large cells (macrophages and dendritic cells) (≈24–40 nodes, <0.1% of space). To determine the appropriateness of the mass-action term, target cells and target-specific CTL are randomly positioned in the lattice according to their assigned frequency. The rest of the lattice is filled with unspecified splenocytes.

For the analysis of the influence of the CTL velocity and killing duration on our estimates, we simulate the migration of target cells into the spleen, in addition to target cell killing in the spleen, in accordance with the events in an in vivo CTL killing assay [5]. In these simulations, 450 CTL (≈2% of the total number of cells without reticular network) are randomly positioned in the lattice. Target and control cells (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e202.jpg each) appear in the lattice at a rate An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e203.jpg [8]. They can either appear on a free node or replace an unspecified splenocyte, which, in turn, is simply deleted.

At each time-step of the simulation the position and other properties of each cell are updated. A time-step corresponds to 30 seconds of real time. In the simulations in which we increase or decrease the velocity of CTL, we update them more or less often than the other cells, respectively. In these simulations, a time-step corresponds to 12–40 s real time. We initialize our simulations by a burn-in phase of 20 minutes real time before target cells are allowed to migrate into the spleen, and target cells and CTL are allowed to interact. After the burn-in phase a simulation is run for 300 min real time.

The 27000 nodes of the cellular automaton comprise approximately 21000 (biological) cells. As the total number of splenocytes of a mouse spleen is estimated to be around 2×107–108 cells [5],[40], the modelled compartment comprises roughly 0.01%–0.1% of the white pulp of the spleen.

Cell movement

Each cell is able to move. We distinguish between two types of movement. The first is movement into free space. A cell can move into a neighbouring unoccupied node if it has the appropriate moving direction An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e204.jpg. If several cells are able to move into the free spot, one cell is chosen at random. The second type of movement is defined as neighbour swapping. As we are not interested in knowing if an unspecified splenocyte changes its place with another unspecified splenocyte (and to speed up computation), neighbour swapping is performed by target cells and target-specific CTL only. Hereby, such a cell will swap its place with a splenocyte irrespectively of the moving direction An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e205.jpg of the splenocyte while two CTL or target cells only swap their places if they move towards each other.

Movement of cells consisting of several nodes involves the restructuring of their shape. Such a cell is simulated to “diffuse” into an unoccupied node by placing its cell-part ( = node) farthest from the unoccupied node into this node. The node which was occupied by the moved cell-part becomes free space. The same procedure is performed for neighbour-swapping with a CTL or target cell. Position changes become effective after all cells updated their position.

If a CTL or target cell is not able to move, it randomly chooses a new moving direction An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e206.jpg. This new direction becomes effective in the next round of updating. The new moving direction is sampled from the set of the 26 possible directions defined by the next neighbours according to the following method. The former moving direction An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e207.jpg is translated into cartesian coordinates An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e208.jpg, with An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e209.jpg. One coordinate An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e210.jpg is chosen at random and updated dependent on the former value. As cells prefer small changes in their direction, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e211.jpg with probability An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e212.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e213.jpg otherwise if An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e214.jpg (analogous for An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e215.jpg). If An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e216.jpg, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e217.jpg at random. If the cells hit the reticular network, there is a higher chance to make larger turns in our simulations, as there is a high chance that a node in a direction similar to the previous moving direction will also be occupied by reticular network (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e218.jpg). By controling the number of changes and moves per time step, we are able to regulate the velocity of cells. With these rules, CTL will perform random walks as described above (see Fig. 2B).

CTL scanning and target killing

Before each update of the lattice, all the target-specific CTL scan their direct neighbourhood for target cells. If a CTL encounters a target cell it recognizes it with a certain probability (the probability of recognition). Upon recognition both cells will form a conjugate. Unless it is stated otherwise, we assume the probability of recognition to be one. It is observed, that conjugate-formation is followed by a period where T cells and bound target cells migrate together before they finally stop [24],[41]. However, it is not clear how the direction of the conjugate is determined and what happens if several CTL are bound to one target or vice versa. We assume that conjugates will immediately stop migrating after conjugate formation and stay immobile during the time of the killing, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e219.jpg. Allowing conjugates to migrate together for a certain time does not generally affect our results.

We allow multiple killing of CTL which is in agreement with observations in vitro where CTL kill multiple targets simultaneously [28]. When the target cell is killed, the CTL chooses a new moving direction An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e220.jpg at random and proceeds.

Quantification of CTL movement and motility

The average velocity of a CTL in a simulation with An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e221.jpg CTL is defined by An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e222.jpg, with An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e223.jpg as the average velocity of CTL An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e224.jpg over time. We distinguish between two different types of velocities in the presence of killing. The “hunting” velocity An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e225.jpg is calculated based on all CTL that are not bound to target cells. The second velocity, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e226.jpg, describes the average velocity over all CTL regardless of them being bound to target cells or not.

The motility coefficient An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e227.jpg measures the temporal displacement of a cell. If An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e228.jpg denotes the position of a cell at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e229.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e230.jpg its displacement during this time, the motility coefficient, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e231.jpg, in three dimensions is estimated according to: An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e232.jpg. For a graphical representation, we plot the mean displacement against the square root of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e233.jpg, which denotes the time interval on which the calculation of the displacement is based. The motility coefficient can then be calculated from the slope of the curve (Fig. 2C) (see e.g. [18]).

The in vivo CTL killing assay

Our research was motivated by the in vivo CTL killing assay presented in Barber et al. [5]. The experimental details are comprehensively described in this paper. Briefly, mice are infected by LCMV to generate CTL responses. Eight days after infection the mice harbour effector CTL, whereas 30 days after infection or later the mice harbour memory CTL. A mixture of fluorescently labelled cells is then injected intravenously into the tail vene of the mice. This mixture consists of equal proportions of target cells expressing either of the two LCMV epitopes (NP396 and GP276) and control cells, which do not express LCMV peptides and are therefore assumed to be unaffected by the CTL response. The frequencies of CTL, target and control cells are measured in the spleen after sacrificing the mice at different time points up to 270 min after the transfer of the target cells.

The basic model - the dynamic of target cells in blood and spleen

According to previously published mathematical models [8],[9], the data obtained by an in vivo CTL killing assay are analysed in two steps. First, we consider the migration of target cells into the spleen after injection. Second, we analyse the killing of target cells in the spleen. The model assumes that killing only occurs in the spleen and that the frequency of target-specific CTL, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e234.jpg, is constant during the short time period of the experiment.

Estimates of migration parameters are based on absolute numbers of control cells in the blood, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e235.jpg, and in the spleen, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e236.jpg. The dynamics is described by:

equation image
(4)
equation image
(5)

This leads to

equation image
(6)

where An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e240.jpg refers to the number of control cells transferred at the start of the experiment. The parameter An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e241.jpg defines the migration rate of cells into the spleen and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e242.jpg the natural loss rate of cells in the blood. To estimate the actual killing rate An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e243.jpg, we assume that target and control cells migrate into the spleen following the same rate An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e244.jpg.

If An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e245.jpg denotes the frequency of target cells in the spleen then the basic model is given by:

equation image
(7)

The solution of the above differential equation is:

equation image
(8)

Here An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e248.jpg represents the total number of splenocytes.

To fit experimental data to the model, Regoes et al. [8] used Eq. (6) and Eq. (8). Assuming that most of the experimental error arises from different number of cells injected into the mice, this method can be refined [9]. In Yates et al. [9] we used the proportion of target cells that have been killed, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e249.jpg, to estimate the killing rate constant An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e250.jpg. The proportion of target cells that have been killed, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e251.jpg, is given by:

equation image
(9)

Here An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e253.jpg.

We showed in Yates et al. [9] that Eq. (9) provides a less biased estimator based on simulated data if there are large variations in the number of injected cells, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e254.jpg. As we control An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e255.jpg in our simulations, both methods lead to the same results for An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e256.jpg given a mass-action assumption in the killing term (see Fig. 7 and Fig. S3). However, the latter method seems to be less robust if we assume a killing term, which is linear in the target cell frequency and saturates in the CTL frequency. This is surprising as we expect to reduce variation in the estimates with the revised method [9]. We do not understand the lower robustness of the fitting method yet. However, we mainly show results for An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e257.jpg using Eq. (8) because the estimates are more robust for our simulated data.

To perform the statistical analysis we used the An external file that holds a picture, illustration, etc.
Object name is pcbi.1000466.e258.jpg language of statistical computing [42].

Supporting Information

Figure S1

Distribution for the turning angle θ. The distribution is shown in the absence of killing for the CTL velocities vh = 3.65 µm/min (A) and vh = 10.8 µm/min (B) and in the presence of killing for vh = 10.8 µm/min and tD = 15 min (C). Reduced velocity as well as killing activity slightly increases the mean turning angle.

(0.10 MB TIF)

Figure S2

Investigating the influence of multiple killing on the simulations. (A) Histogram for the number of target cells killed per CTL for different CTL velocities. Killing duration of tD = 15 min is fixed and a CTL frequency of C~0.02 ( = 450 cells) is used. Each simulation comprises 300 min (vh = 3.73 µm/min (red), vh = 6.91 µm/min (blue), vh = 10.54 µm/min (black), vh = 13.45 µm/min (green), vh = 19.45 µm/min (orange)). The average number of target cells killed per CTL increases with velocity. (B) The same data as in (A) analyzed for the time a CTL spents to perform multiple killing relative to the time it is bound in a conjugate.

(0.16 MB TIF)

Figure S3

Plots of vh, vc and tD against the estimates of k. The estimation was based on either counting free and bound target cells (shown in A–C) or only free target cells (shown in D–F). The killing rate constant was estimated using the revised estimation method based on the proportion of target cells killed. The mean, the minimum and the maximum over 1000 bootstrap estimates are shown (neglecting outliers), as the 95% confidence intervals are, in most cases, too small to be plotted. Spearman's rank correlation coefficient, ρ, is given. The color coding corresponds to the different levels of tD (5 min (orange), 10 min (green), 15 min (black), 30 min (blue), 60 min (red)). In each group of tD in C and F, the level of velocity is increasing from left to right.

(0.16 MB TIF)

Figure S4

Estimates for k given different probabilities of recognition of CTL for targets. The killing duration and the CTL velocity were kept constant (vh~10.8 µm/min, tD = 15 min). Each dot represents the estimate for one simulation follwed over a time period of 300 min using the revised estimation method based on the proportion of target cells killed. Blue squares represent the mean values per probability of recognition.

(0.11 MB TIF)

Video S1

Movement of simulated CTL in the absence of killing. A time period of 60 min is shown. The CTL velocity is set to vh = 10.8 µm/min.

(6.90 MB MPG)

Video S2

Dynamics for the interaction of CTL (blue) and target cells (red) for one simulation. Ths simulation was parameterized with vh = 10.8 µm/min and tD = 15 min. CTL and target cells bound in conjugates are shown in lightblue and orange, respectively. The movie shows the first 100 min of a simulated time period of 300 min. The size of the full movie is too large to be shown.

(9.43 MB MPG)

Acknowledgments

We thank Trevor Hinkley, Roger Kouyos, Annette Oxenius, Kirsten Richter and Andrew Yates for helpful discussions. We would like to thank the referees for helpful comments.

Footnotes

The authors have declared that no competing interests exist.

Both authors gratefully acknowledge the financial support of the Swiss National Science Foundation (SNF-grant 315200-114148). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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