Utz-Uwe Haus

A logical model provides insights into T cell receptor signaling

Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.

Hypergraphs and cellular networks

3,41Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany, 2Institute for Mathematical Optimization, Faculty of Mathematics, Otto-von-Guericke University Magdeburg, Magdeburg, Germany, 3Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum Mu¨nchen—German Research Center forEnvironmental Health, Neuherberg, Germany, 4Max Planck Institute for Dynamics and Self-Organization, Go¨ttingen, Germany

Computing knock-out strategies in metabolic networks.

Given a metabolic network in terms of its metabolites and reactions, our goal is to efficiently compute the minimal knock-out sets of reactions required to block a given behavior. We describe an algorithm that improves the computation of these knock-out sets when the elementary modes (minimal functional subsystems) of the network are given. We also describe an algorithm that computes both the knock-out sets and the elementary modes containing the blocked reactions directly from the description of the network and whose worst-case computational complexity is better than the algorithms currently in use for these problems. Computational results are included.

Minimal cut sets in a metabolic network are elementary modes in a dual network

MOTIVATION Elementary modes (EMs) and minimal cut sets (MCSs) provide important techniques for metabolic network modeling. Whereas EMs describe minimal subnetworks that can function in steady state, MCSs are sets of reactions whose removal will disable certain network functions. Effective algorithms were developed for EM computation while calculation of MCSs is typically addressed by indirect methods requiring the computation of EMs as initial step. RESULTS In this contribution, we provide a method that determines MCSs directly without calculating the EMs. We introduce a duality framework for metabolic networks where the enumeration of MCSs in the original network is reduced to identifying the EMs in a dual network. As a further extension, we propose a generalization of MCSs in metabolic networks by allowing the combination of inhomogeneous constraints on reaction rates. This framework provides a promising tool to open the concept of EMs and MCSs to a wider class of applications. CONTACT utz-uwe.haus@math.ethz.ch; klamt@mpi-magdeburg.mpg.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

SynProt: a database for proteins of detergent-resistant synaptic protein preparations

Chemical synapses are highly specialized cell–cell contacts for communication between neurons in the CNS characterized by complex and dynamic protein networks at both synaptic membranes. The cytomatrix at the active zone (CAZ) organizes the apparatus for the regulated release of transmitters from the presynapse. At the postsynaptic side, the postsynaptic density constitutes the machinery for detection, integration, and transduction of the transmitter signal. Both pre- and postsynaptic protein networks represent the molecular substrates for synaptic plasticity. Their function can be altered both by regulating their composition and by post-translational modification of their components. For a comprehensive understanding of synaptic networks the entire ensemble of synaptic proteins has to be considered. To support this, we established a comprehensive database for synaptic junction proteins (SynProt database) primarily based on proteomics data obtained from biochemical preparations of detergent-resistant synaptic junctions. The database currently contains 2,788 non-redundant entries of rat, mouse, and some human proteins, which mainly have been manually extracted from 12 proteomic studies and annotated for synaptic subcellular localization. Each dataset is completed with manually added information including protein classifiers as well as automatically retrieved and updated information from public databases (UniProt and PubMed). We intend that the database will be used to support modeling of synaptic protein networks and rational experimental design.

Minimal conflicting sets for the consecutive ones property in ancestral genome reconstruction.

A binary matrix has the Consecutive Ones Property (C1P) if its columns can be ordered in such a way that all 1s on each row are consecutive. A Minimal Conflicting Set is a set of rows that does not have the C1P, but every proper subset has the C1P. Such submatrices have been considered in comparative genomics applications, but very little is known about their combinatorial structure and efficient algorithms to compute them. We first describe an algorithm that detects rows that belong to Minimal Conflicting Sets. This algorithm has a polynomial time complexity when the number of 1s in each row of the considered matrix is bounded by a constant. Next, we show that the problem of computing all Minimal Conflicting Sets can be reduced to the joint generation of all minimal true clauses and maximal false clauses for some monotone boolean function. We use these methods on simulated data related to ancestral genome reconstruction to show that computing Minimal Conflicting Set is useful in discriminating between true positive and false positive ancestral syntenies. We also study a dataset of yeast genomes and address the reliability of an ancestral genome proposal of the Saccharomycetaceae yeasts.

A primal all-integer algorithm based on irreducible solutions

Abstract. This paper introduces an exact primal augmentation algorithm for solving general linear integer programs. The algorithm iteratively substitutes one column in a tableau by other columns that correspond to irreducible solutions of certain linear diophantine inequalities. We prove that various versions of our algorithm are finite. It is a major concern in this paper to show how the subproblem of replacing a column can be accomplished effectively. An implementation of the presented algorithms is given. Computational results for a number of hard 0/1 integer programs from the MIPLIB demonstrate the practical power of the method.

The integral basis method for integer programming

Abstract. This paper introduces an exact algorithm for solving integer programs, neither using cutting planes nor enumeration techniques. It is a primal augmentation algorithm that relies on iteratively substituting one column by columns that correspond to irreducible solutions of certain linear diophantine inequalities. We demonstrate the algorithms potential by testing it on some instances of the MIPLIB with up to 6000 variables.

Synaptic proteome changes in mouse brain regions upon auditory discrimination learning

Changes in synaptic efficacy underlying learning and memory processes are assumed to be associated with alterations of the protein composition of synapses. Here, we performed a quantitative proteomic screen to monitor changes in the synaptic proteome of four brain areas (auditory cortex, frontal cortex, hippocampus striatum) during auditory learning. Mice were trained in a shuttle box GO/NO‐GO paradigm to discriminate between rising and falling frequency modulated tones to avoid mild electric foot shock. Control‐treated mice received corresponding numbers of either the tones or the foot shocks. Six hours and 24 h later, the composition of a fraction enriched in synaptic cytomatrix‐associated proteins was compared to that obtained from naïve mice by quantitative mass spectrometry. In the synaptic protein fraction obtained from trained mice, the average percentage (±SEM) of downregulated proteins (59.9 ± 0.5%) exceeded that of upregulated proteins (23.5 ± 0.8%) in the brain regions studied. This effect was significantly smaller in foot shock (42.7 ± 0.6% down, 40.7 ± 1.0% up) and tone controls (43.9 ± 1.0% down, 39.7 ± 0.9% up). These data suggest that learning processes initially induce removal and/or degradation of proteins from presynaptic and postsynaptic cytoskeletal matrices before these structures can acquire a new, postlearning organisation. In silico analysis points to a general role of insulin‐like signalling in this process.

Minimal Conflicting Sets for the Consecutive Ones Property in Ancestral Genome Reconstruction

A binary matrix has the Consecutive Ones Property (C1P) if its columns can be ordered in such a way that all 1s on each row are consecutive. A Minimal Conflicting Set is a set of rows that does not have the C1P, but every proper subset has the C1P. Such submatrices have been considered in comparative genomics applications, but very little is known about their combinatorial structure and efficient algorithms to compute them. We first describe an algorithm that detects rows that belong to Minimal Conflicting Sets. This algorithm has a polynomial time complexity when the number of 1s in each row of the considered matrix is bounded by a constant. Next, we show that the problem of computing all Minimal Conflicting Sets can be reduced to the joint generation of all minimal true clause and maximal false clauses for some monotone boolean function. We use these methods in preliminary experiments on simulated data related to ancestral genome reconstruction.