Theoretical Biology, Utrecht University

http://theory.bio.uu.nl/vitaly/

A quantitative framework for estimation of per capita killing rates of antigen-specific CD8 T cells in vivo: a LCMV case

Despite recent advances in immunology, several key immunological parameters determining virus dynamics in infected hosts remain largely unknown. For example, the rate at which effector and memory CD8 T cells specific to a particular antigen clear virus-infected cells in vivo, is hardly known for any viral infection. We propose a new framework to quantify CD8 T cell mediated killing of virus-infected or peptide-pulsed target cells in vivo. We develop a specific model for a short term T cell mediated killing of targets in a mouse spleen. As an example of the new technique, we reanalyze recently published data on killing of peptide-pulsed splenocytes by cytotoxic T lymphocytes and memory CD8 T cells specific to NP396 and GP276 epitopes of LCMV. Using this novel framework, we estimate that the half-life time of the NP396- and GP276-pulsed targets in the spleens of acutely infected mice is 2 and 6 minutes, respectively, while in the spleens of memory mice, half of NP396- and GP276-expressing targets are eliminated in 49 minutes and 1.3 hour, respectively. While as a population, effectors are highly effective at eliminating peptide-pulsed cells, on average they kill less than one cell per day. In contrast, each memory CD8 T cell can eliminate on average five of pulsed targets per day. We also show that killing of peptide-pulsed targets in neither purely frequency- or density-dependent, and on the per cell basis, the killing efficacy of LCMV-specific memory and effector CD8 T cells is similar.

Instituto Gulbenkian de Ciência (Theoretical Immunology)

Diversity and shape of peripheral effector and regulatory T cell repertoires

A healthy immune system involves a fine balance between effector T cells (Teffs) that mount immune responses, and regulatory T cells (Tregs) that suppress them. When this balance is perturbed immunopathologies arise. Understanding this balance requires to know how diverse are the repertoires of Teffs and Tregs and how they relate to each other. A too large intersection between the repertoires could lead to deleterious inhibition of specific immune responses against harmful microorganisms, while a too small overlap may open the way to autoimmune responses. Here we address this issue by re-analyzing available data (e.g., Immunity, 2004, 21:267-277). Our results show that both Tregs and Teffs are similar diverse with Lognormal abundances distributions. Moreover, the repertoires might be the same with abundances being at best positively correlated. The causes and the implications of these results need further investigation.

KDBIO group / INESC-ID and FCM/UNL

A challenge to nonlinear estimation: dynamic modeling of metabolic networks

Systems Biology is an emerging field that uses a global and integrative perspective to capture the behavior of complex living organisms. An important challenge in this area is the dynamic modeling, optimization and control of metabolic networks. A top-down approach can be conducted using experimental multivariate time series of metabolite concentrations, obtained through Nuclear Magnetic Resonance (NMR), and defining the model structure, given as a system of non-linear coupled differential equations. An important class of equations under Biochemical Systems Theory is commonly used, where rates are modeled with power-law functions. This methodology has proven to be flexible enough to be able to handle the most dissimilar metabolic networks and has the advantage that every single parameter can be directly interpreted biochemically, offering insight into the topological structure of the network and into the kinetic orders of the chemical reactions involved. However, the estimation of the parameters still constitutes a major difficulty, given the innumerous local minima and rough error surface, and is the bottleneck in the whole modeling process. As a case study, a preliminary model of glycolysis in Lactococcus lactis will be presented that could nevertheless provide important insights into the design of the pathway and the function of specific feedforward and feedback activations and inhibitions. Future work will be developed through project DyanMo, from the National Portuguese Science Foundation (FCT), with a multidisciplinary team constituted by INESC-ID, IST, ITQB-UNL, MDAnderson Cancer Center and Georgia Tech.

UC San Diego, USA

A New Approach to the Identification of Proteins and Post-translational Modifications

The ongoing success of the proteomics endeavor is the result of a prolific symbiosis between experimental ingenuity and efficient bioinformatics. But despite valuable contributions, the road to a better understanding of protein behavior is still hurdled by significant difficulties in the extensive identification of unexpected post-translational modifications and highly modified peptides. Recently, tandem mass spectrometry (MS/MS) based approaches seemed to be reaching the limit on the amount of information that could be extracted from MS/MS spectra. However, a closer look reveals that a common limiting procedure is to analyze each spectrum in isolation, even though high throughput mass spectrometry regularly generates many spectra from related peptides. By capitalizing on this redundancy we show that, similarly to the alignment of protein sequences, unidentified MS/MS spectra can also be aligned for the identification of modified and unmodified variants of the same peptide. Moreover, this alignment procedure can be iterated for the accurate grouping of multiple peptide variants. In fact, when applied to a set of spectra from cataractous lenses proteins from a 93-year old patient, spectral networks were able to capitalize on the highly correlated peaks in spectra from variants of the same peptide to rediscover the modifications identified by database search methods and additionally discovered several novel modification events.

TAGC (INSERM ERM 2006) http://tagc.univ-mrs.fr

Decision Diagrams for the Representation and Analysis of Logical Models of Genetic Networks

The complexity of biological regulatory networks calls for the development of proper mathematical methods to model their structures and to obtain insight in their dynamical behaviours. The generalised logical formalism is a qualitative approach consisting in modelling regulatory networks in terms of logical equations (using either Boolean or multi-valued discretisation).

We show that the use of Multi-valued Decision Diagrams enables the development of efficient algorithms for the analysis of specific dynamical properties of the regulatory graphs. In particular, we address the question of determining conditions insuring the functionality of feedback circuits, as well as the identification of stable states.

Finally, we apply these algorithms to logical models of T cell activation and differentiation.

Instituto Gulbenkian de Ciência (Theoretical Immunology)

The Stochastic Basis of Somatic Variation

Isogenic cell populations exhibit a surprising amount of heterogeneity, such as variability in protein copy numbers or in methylation patterns, in situations where this could hardly be attributed to genetic or external environmental factors. Moreover, this variability was found to be somewhat heritable leading to the concept of non-genetic individuality. Very soon, it became apparent that the ultimate cause of this variability in isogenic cells was the stochastic nature of chemical reactions within the cell, particularly at the level of gene transcription. However, it is not clear how stochastic gene expression, per se, leads to these manifestations of somatic variation and what impact this variability has for the population.

In particular, we turn our attention to a manifestation of non-genetic individuality at the level of the gene: stochastic monoallelic expression. We mathematically formalize and challenge against quantitative experimental data several proposed mechanisms that aim to explain this phenomenon and, as result, we propose a general model of transcription regulation that relates stochastic transcriptional activation and epigenetic chromatin modifications, which provide another source of somatic variation.

Next, we show how variability in cellular components leads to the observed distributions of protein copy numbers in cell populations. Then, in order to seek the implications for the response of the population, we model several common signalling modules and analyze their sensitivity to changes in total concentration of key components. We identify mechanisms that, due to their structure, promote an uniform response from each cell in the population, thereby negating the effects of heterogeneity and mechanisms that enable this heterogeneity to be manifested in novel ways, such as level and timing of response and how many cells actually respond.

Since this heterogeneity of protein levels is created by dynamical mechanisms and hence they fluctuate, we ask if different fluctuation rates and structures confer a competitive advantage in a competition model between populations with different fluctuation characteristics of a receptor for a growth factor. We conclude that higher variances confer an advantage for the same fluctuation structure (i.e., steady state distribution). We also compare different fluctuation structures showing that they have different impacts on the fitness of the population providing a basis for the selection of the generative mechanism of these fluctuations.

Finally, we show how the individual history of a cell in terms of stochastic interactions with its environment can lead to variability of intracellular components and how this can be used as a homeostasis mechanism in a population.

In light of these results we argue that stochastic effects inherent to the cells metabolism generate non-genetic individuality in an isogenic population. Moreover, we investigated the implications of such heterogeneity at the level of signal transduction, gene expression and population dynamics. We tried to convey a new way to look at cell populations and their relation to their environment based on the stochastic events at the single cell level. This view represents an emerging perspective in which determinism is based not on the dynamics of the single cell but on the evolution of a distribution of probabilities, in which each cell is a realization of a stochastic process. Within this perspective, programs of differentiation, cellular identity and individuality acquire new meanings.