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  • 1. Adami, C.
    et al.
    Schossau, J.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Evolution and stability of altruist strategies in microbial games2012In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 85, no 1, article id 011914Article in journal (Refereed)
    Abstract [en]

    When microbes compete for limited resources, they often engage in chemical warfare using bacterial toxins. This competition can be understood in terms of evolutionary game theory (EGT). We study the predictions of EGT for the bacterial "suicide bomber" game in terms of the phase portraits of population dynamics, for parameter combinations that cover all interesting games for two-players, and seven of the 38 possible phase portraits of the three-player game. We compare these predictions to simulations of these competitions in finite well-mixed populations, but also allowing for probabilistic rather than pure strategies, as well as Darwinian adaptation over tens of thousands of generations. We find that Darwinian evolution of probabilistic strategies stabilizes games of the rock-paper-scissors type that emerge for parameters describing realistic bacterial populations, and point to ways in which the population fixed point can be selected by changing those parameters. © 2012 American Physical Society.

  • 2. Edlund, J. A.
    et al.
    Chaumont, N.
    Hintze, Arend
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States; Michigan State University, East Lansing, United States.
    Koch, C.
    Tononi, G.
    Adami, C.
    Integrated information increases with fitness in the evolution of animats2011In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 7, no 10, article id e1002236Article in journal (Refereed)
    Abstract [en]

    One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data. © 2011 Edlund et al.

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  • 3.
    Hintze, Arend
    et al.
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States.
    Adami, C.
    Evolution of complex modular biological networks2008In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 4, no 2Article in journal (Refereed)
    Abstract [en]

    Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks. © 2008 Hintze and Adami.

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  • 4.
    Hintze, Arend
    et al.
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States.
    Adami, C.
    Modularity and anti-modularity in networks with arbitrary degree distribution2010In: Biology Direct, E-ISSN 1745-6150, Vol. 5, article id 32Article in journal (Refereed)
    Abstract [en]

    Background: Much work in systems biology, but also in the analysis of social network and communication and transport infrastructure, involves an in-depth analysis of local and global properties of those networks, and how these properties relate to the function of the network within the integrated system. Most often, systematic controls for such networks are difficult to obtain, because the features of the network under study are thought to be germane to that function. In most such cases, a surrogate network that carries any or all of the features under consideration, while created artificially and in the absence of any selective pressure relating to the function of the network being studied, would be of considerable interest.Results: Here, we present an algorithmic model for growing networks with a broad range of biologically and technologically relevant degree distributions using only a small set of parameters. Specifying network connectivity via an assortativity matrix allows us to grow networks with arbitrary degree distributions and arbitrary modularity. We show that the degree distribution is controlled mainly by the ratio of node to edge addition probabilities, and the probability for node duplication. We compare topological and functional modularity measures, study their dependence on the number and strength of modules, and introduce the concept of anti-modularity: a property of networks in which nodes from one functional group preferentially do not attach to other nodes of that group. We also investigate global properties of networks as a function of the network's growth parameters, such as smallest path length, correlation coefficient, small-world-ness, and the nature of the percolation phase transition. We search the space of networks for those that are most like some well-known biological examples, and analyze the biological significance of the parameters that gave rise to them.Conclusions: Growing networks with specified characters (degree distribution and modularity) provides the opportunity to create surrogates for biological and technological networks, and to test hypotheses about the processes that gave rise to them. We find that many celebrated network properties may be a consequence of the way in which these networks grew, rather than a necessary consequence of how they work or function.Reviewers: This article was reviewed by Erik van Nimwegen, Teresa Przytycka (nominated by Claus Wilke), and Leonid Mirny. For the full reviews, please go to the Reviewer's Comments section. © 2010 Hintze and Adami; licensee BioMed Central Ltd.

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  • 5. Iliopoulos, D.
    et al.
    Hintze, Arend
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States.
    Adami, C.
    Critical dynamics in the evolution of stochastic strategies for the iterated Prisoner's Dilemma2010In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 6, no 10, article id 1000948Article in journal (Refereed)
    Abstract [en]

    The observed cooperation on the level of genes, cells, tissues, and individuals has been the object of intense study by evolutionary biologists, mainly because cooperation often flourishes in biological systems in apparent contradiction to the selfish goal of survival inherent in Darwinian evolution. In order to resolve this paradox, evolutionary game theory has focused on the Prisoner's Dilemma (PD), which incorporates the essence of this conflict. Here, we encode strategies for the iterated Prisoner's Dilemma (IPD) in terms of conditional probabilities that represent the response of decision pathways given previous plays. We find that if these stochastic strategies are encoded as genes that undergo Darwinian evolution, the environmental conditions that the strategies are adapting to determine the fixed point of the evolutionary trajectory, which could be either cooperation or defection. A transition between cooperative and defective attractors occurs as a function of different parameters such as mutation rate, replacement rate, and memory, all of which affect a player's ability to predict an opponent's behavior. These results imply that in populations of players that can use previous decisions to plan future ones, cooperation depends critically on whether the players can rely on facing the same strategies that they have adapted to. Defection, on the other hand, is the optimal adaptive response in environments that change so quickly that the information gathered from previous plays cannot usefully be integrated for a response. © 2010 Iliopoulos et al.

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  • 6. Ostman, B.
    et al.
    Hintze, Arend
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States; Michigan State University, East Lansing, United States.
    Adami, C.
    Impact of epistasis and pleiotropy on evolutionary adaptation2012In: Proceedings of the Royal Society of London. Biological Sciences, ISSN 0962-8452, E-ISSN 1471-2954, Vol. 279, no 1727, p. 247-256Article in journal (Refereed)
    Abstract [en]

    Evolutionary adaptation is often likened to climbing a hill or peak. While this process is simple for fitness landscapes where mutations are independent, the interaction between mutations (epistasis) as well as mutations at loci that affect more than one trait (pleiotropy) are crucial in complex and realistic fitness landscapes. We investigate the impact of epistasis and pleiotropy on adaptive evolution by studying the evolution of a population of asexual haploid organisms (haplotypes) in a model of N interacting loci, where each locus interacts with K other loci. We use a quantitative measure of the magnitude of epistatic interactions between substitutions, and find that it is an increasing function of K. When haplotypes adapt at high mutation rates, more epistatic pairs of substitutions are observed on the line of descent than expected. The highest fitness is attained in landscapes with an intermediate amount of ruggedness that balance the higher fitness potential of interacting genes with their concomitant decreased evolvability. Our findings imply that the synergism between loci that interact epistatically is crucial for evolving genetic modules with high fitness, while too much ruggedness stalls the adaptive process. © 2012 The Royal Society.

  • 7. Pell, J.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Canino-Koning, R.
    Howe, A.
    Tiedje, J. M.
    Brown, C. T.
    Scaling metagenome sequence assembly with probabilistic de Bruijn graphs2012In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 109, no 33, p. 13272-13277Article in journal (Refereed)
    Abstract [en]

    Deep sequencing has enabled the investigation of a wide range of environmental microbial ecosystems, but the high memory requirements for de novo assembly of short-read shotgun sequencing data from these complex populations are an increasingly large practical barrier. Here we introduce a memory-efficient graph representation with which we can analyze the k-mer connectivity of metagenomic samples. The graph representation is based on a probabilistic data structure, a Bloom filter, that allows us to efficiently store assembly graphs in as little as 4 bits per k-mer, albeit inexactly. We show that this data structure accurately represents DNA assembly graphs in low memory.We apply this data structure to the problem of partitioning assembly graphs into components as a prelude to assembly, and show that this reduces the overall memory requirements for de novo assembly of metagenomes. On one soil metagenome assembly, this approach achieves a nearly 40-fold decrease in the maximum memory requirements for assembly. This probabilistic graph representation is a significant theoretical advance in storing assembly graphs and also yields immediate leverage on metagenomic assembly.

  • 8. Pell, J.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Canino-Koning, R.
    Howe, A.
    Tiedje, J. M.
    Titus Brown, C.
    Workshop: Graph compression approaches in assembly2012In: 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012, 2012Conference paper (Refereed)
    Abstract [en]

    Using a probabilistic data structure to store DNA assembly graphs results in a significant memory savings over other methods. As long as the Bloom filter remains below a specific false positive rate, it remains possible to traverse the graph. Using a Bloom filter has many applications in metagenomics, mRNAseq, read filtering, and error correction. We are currently exploring these possibilities and more. © 2012 IEEE.

  • 9. Qian, J.
    et al.
    Ferguson, T. M.
    Shinde, D. N.
    Ramírez-Borrero, A. J.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Adami, C.
    Niemz, A.
    Sequence dependence of isothermal DNA amplification via EXPAR2012In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 40, no 11Article in journal (Refereed)
    Abstract [en]

    Isothermal nucleic acid amplification is becoming increasingly important for molecular diagnostics. Therefore, new computational tools are needed to facilitate assay design. In the isothermal EXPonential Amplification Reaction (EXPAR), template sequences with similar thermodynamic characteristics perform very differently. To understand what causes this variability, we characterized the performance of 384 template sequences, and used this data to develop two computational methods to predict EXPAR template performance based on sequence: a position weight matrix approach with support vector machine classifier, and RELIEF attribute evaluation with Nave Bayes classification. The methods identified well and poorly performing EXPAR templates with 6770 sensitivity and 7780 specificity. We combined these methods into a computational tool that can accelerate new assay design by ruling out likely poor performers. Furthermore, our data suggest that variability in template performance is linked to specific sequence motifs. Cytidine, a pyrimidine base, is over-represented in certain positions of well-performing templates. Guanosine and adenosine, both purine bases, are over-represented in similar regions of poorly performing templates, frequently as GA or AG dimers. Since polymerases have a higher affinity for purine oligonucleotides, polymerase binding to GA-rich regions of a single-stranded DNA template may promote non-specific amplification in EXPAR and other nucleic acid amplification reactions. © 2012 The Author(s).

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  • 10. Sheneman, L.
    et al.
    Schossau, J.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    The evolution of neuroplasticity and the effect on integrated information2019In: Entropy, E-ISSN 1099-4300, Vol. 21, no 5, article id 524Article in journal (Refereed)
    Abstract [en]

    Information integration theory has been developed to quantify consciousness. Since conscious thought requires the integration of information, the degree of this integration can be used as a neural correlate (Φ) with the intent to measure degree of consciousness. Previous research has shown that the ability to integrate information can be improved by Darwinian evolution. The value Φ can change over many generations, and complex tasks require systems with at least a minimum Φ. This work was done using simple animats that were able to remember previous sensory inputs, but were incapable of fundamental change during their lifetime: actions were predetermined or instinctual. Here, we are interested in changes to Φ due to lifetime learning (also known as neuroplasticity). During lifetime learning, the system adapts to perform a task and necessitates a functional change, which in turn could change Φ. One can find arguments to expect one of three possible outcomes: Φ might remain constant, increase, or decrease due to learning. To resolve this, we need to observe systems that learn, but also improve their ability to learn over the many generations that Darwinian evolution requires. Quantifying Φ over the course of evolution, and over the course of their lifetimes, allows us to investigate how the ability to integrate information changes. To measure Φ, the internal states of the system must be experimentally observable. However, these states are notoriously difficult to observe in a natural system. Therefore, we use a computational model that not only evolves virtual agents (animats), but evolves animats to learn during their lifetime. We use this approach to show that a system that improves its performance due to feedback learning increases its ability to integrate information. In addition, we show that a system's ability to increase Φ correlates with its ability to increase in performance. This suggests that systems that are very plastic regarding Φ learn better than those that are not. © 2019 by the authors.

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