@article{613,
abstract = {Bacteria in groups vary individually, and interact with other bacteria and the environment to produce population-level patterns of gene expression. Investigating such behavior in detail requires measuring and controlling populations at the single-cell level alongside precisely specified interactions and environmental characteristics. Here we present an automated, programmable platform that combines image-based gene expression and growth measurements with on-line optogenetic expression control for hundreds of individual Escherichia coli cells over days, in a dynamically adjustable environment. This integrated platform broadly enables experiments that bridge individual and population behaviors. We demonstrate: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell-cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria. These examples showcase the potential for real-time integration of theoretical models with measurement and control of many individual cells to investigate and engineer microbial population behavior.},
author = {Chait, Remy P and Ruess, Jakob and Bergmiller, Tobias and Tkacik, Gasper and Guet, Calin C},
issn = {20411723},
journal = {Nature Communications},
number = {1},
publisher = {Nature Publishing Group},
title = {{Shaping bacterial population behavior through computer interfaced control of individual cells}},
doi = {10.1038/s41467-017-01683-1},
volume = {8},
year = {2017},
}
@article{947,
abstract = {Viewing the ways a living cell can organize its metabolism as the phase space of a physical system, regulation can be seen as the ability to reduce the entropy of that space by selecting specific cellular configurations that are, in some sense, optimal. Here we quantify the amount of regulation required to control a cell's growth rate by a maximum-entropy approach to the space of underlying metabolic phenotypes, where a configuration corresponds to a metabolic flux pattern as described by genome-scale models. We link the mean growth rate achieved by a population of cells to the minimal amount of metabolic regulation needed to achieve it through a phase diagram that highlights how growth suppression can be as costly (in regulatory terms) as growth enhancement. Moreover, we provide an interpretation of the inverse temperature β controlling maximum-entropy distributions based on the underlying growth dynamics. Specifically, we show that the asymptotic value of β for a cell population can be expected to depend on (i) the carrying capacity of the environment, (ii) the initial size of the colony, and (iii) the probability distribution from which the inoculum was sampled. Results obtained for E. coli and human cells are found to be remarkably consistent with empirical evidence.},
author = {De Martino, Daniele and Capuani, Fabrizio and De Martino, Andrea},
issn = {24700045},
journal = { Physical Review E Statistical Nonlinear and Soft Matter Physics },
number = {1},
publisher = {American Institute of Physics},
title = {{Quantifying the entropic cost of cellular growth control}},
doi = {10.1103/PhysRevE.96.010401},
volume = {96},
year = {2017},
}
@article{959,
abstract = {In this work it is shown that scale-free tails in metabolic flux distributions inferred in stationary models are an artifact due to reactions involved in thermodynamically unfeasible cycles, unbounded by physical constraints and in principle able to perform work without expenditure of free energy. After implementing thermodynamic constraints by removing such loops, metabolic flux distributions scale meaningfully with the physical limiting factors, acquiring in turn a richer multimodal structure potentially leading to symmetry breaking while optimizing for objective functions.},
author = {De Martino, Daniele},
issn = {24700045},
journal = { Physical Review E Statistical Nonlinear and Soft Matter Physics },
number = {6},
pages = {062419},
publisher = {American Institute of Physics},
title = {{Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics}},
doi = {10.1103/PhysRevE.95.062419},
volume = {95},
year = {2017},
}
@inproceedings{652,
abstract = {We present an approach that enables robots to self-organize their sensorimotor behavior from scratch without providing specific information about neither the robot nor its environment. This is achieved by a simple neural control law that increases the consistency between external sensor dynamics and internal neural dynamics of the utterly simple controller. In this way, the embodiment and the agent-environment coupling are the only source of individual development. We show how an anthropomorphic tendon driven arm-shoulder system develops different behaviors depending on that coupling. For instance: Given a bottle half-filled with water, the arm starts to shake it, driven by the physical response of the water. When attaching a brush, the arm can be manipulated into wiping a table, and when connected to a revolvable wheel it finds out how to rotate it. Thus, the robot may be said to discover the affordances of the world. When allowing two (simulated) humanoid robots to interact physically, they engage into a joint behavior development leading to, for instance, spontaneous cooperation. More social effects are observed if the robots can visually perceive each other. Although, as an observer, it is tempting to attribute an apparent intentionality, there is nothing of the kind put in. As a conclusion, we argue that emergent behavior may be much less rooted in explicit intentions, internal motivations, or specific reward systems than is commonly believed.},
author = {Der, Ralf and Martius, Georg S},
isbn = {978-150905069-7},
location = {Cergy-Pontoise, France},
publisher = {IEEE},
title = {{Dynamical self consistency leads to behavioral development and emergent social interactions in robots}},
doi = {10.1109/DEVLRN.2016.7846789},
year = {2017},
}
@article{823,
abstract = {The resolution of a linear system with positive integer variables is a basic yet difficult computational problem with many applications. We consider sparse uncorrelated random systems parametrised by the density c and the ratio α=N/M between number of variables N and number of constraints M. By means of ensemble calculations we show that the space of feasible solutions endows a Van-Der-Waals phase diagram in the plane (c, α). We give numerical evidence that the associated computational problems become more difficult across the critical point and in particular in the coexistence region.},
author = {Colabrese, Simona and De Martino, Daniele and Leuzzi, Luca and Marinari, Enzo},
issn = {17425468},
journal = { Journal of Statistical Mechanics: Theory and Experiment},
number = {9},
publisher = {IOPscience},
title = {{Phase transitions in integer linear problems}},
doi = {10.1088/1742-5468/aa85c3},
volume = {2017},
year = {2017},
}
@article{943,
abstract = {Like many developing tissues, the vertebrate neural tube is patterned by antiparallel morphogen gradients. To understand how these inputs are interpreted, we measured morphogen signaling and target gene expression in mouse embryos and chick ex vivo assays. From these data, we derived and validated a characteristic decoding map that relates morphogen input to the positional identity of neural progenitors. Analysis of the observed responses indicates that the underlying interpretation strategy minimizes patterning errors in response to the joint input of noisy opposing gradients. We reverse-engineered a transcriptional network that provides a mechanistic basis for the observed cell fate decisions and accounts for the precision and dynamics of pattern formation. Together, our data link opposing gradient dynamics in a growing tissue to precise pattern formation.},
author = {Zagórski, Marcin P and Tabata, Yoji and Brandenberg, Nathalie and Lutolf, Matthias and Tkacik, Gasper and Bollenbach, Tobias and Briscoe, James and Kicheva, Anna},
issn = {00368075},
journal = {Science},
number = {6345},
pages = {1379 -- 1383},
publisher = {American Association for the Advancement of Science},
title = {{Decoding of position in the developing neural tube from antiparallel morphogen gradients}},
doi = {10.1126/science.aam5887},
volume = {356},
year = {2017},
}
@article{955,
abstract = {Gene expression is controlled by networks of regulatory proteins that interact specifically with external signals and DNA regulatory sequences. These interactions force the network components to co-evolve so as to continually maintain function. Yet, existing models of evolution mostly focus on isolated genetic elements. In contrast, we study the essential process by which regulatory networks grow: the duplication and subsequent specialization of network components. We synthesize a biophysical model of molecular interactions with the evolutionary framework to find the conditions and pathways by which new regulatory functions emerge. We show that specialization of new network components is usually slow, but can be drastically accelerated in the presence of regulatory crosstalk and mutations that promote promiscuous interactions between network components.},
author = {Friedlander, Tamar and Prizak, Roshan and Barton, Nicholas H and Tkacik, Gasper},
issn = {20411723},
journal = {Nature Communications},
number = {1},
publisher = {Nature Publishing Group},
title = {{Evolution of new regulatory functions on biophysically realistic fitness landscapes}},
doi = {10.1038/s41467-017-00238-8},
volume = {8},
year = {2017},
}
@article{993,
abstract = {In real-world applications, observations are often constrained to a small fraction of a system. Such spatial subsampling can be caused by the inaccessibility or the sheer size of the system, and cannot be overcome by longer sampling. Spatial subsampling can strongly bias inferences about a system’s aggregated properties. To overcome the bias, we derive analytically a subsampling scaling framework that is applicable to different observables, including distributions of neuronal avalanches, of number of people infected during an epidemic outbreak, and of node degrees. We demonstrate how to infer the correct distributions of the underlying full system, how to apply it to distinguish critical from subcritical systems, and how to disentangle subsampling and finite size effects. Lastly, we apply subsampling scaling to neuronal avalanche models and to recordings from developing neural networks. We show that only mature, but not young networks follow power-law scaling, indicating self-organization to criticality during development.},
author = {Levina (Martius), Anna and Priesemann, Viola},
issn = {20411723},
journal = {Nature Communications},
publisher = {Nature Publishing Group},
title = {{Subsampling scaling}},
doi = {10.1038/ncomms15140},
volume = {8},
year = {2017},
}
@article{658,
abstract = {With the accelerated development of robot technologies, control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unknown dynamics or which are difficult to model. Reasons are the expected scalability, robustness, and resilience of self-organizing systems. The paper presents a self-learning neurocontroller based on extrinsic differential plasticity introduced recently, applying it to an anthropomorphic musculoskeletal robot arm with attached objects of unknown physical dynamics. The central finding of the paper is the following effect: by the mere feedback through the internal dynamics of the object, the robot is learning to relate each of the objects with a very specific sensorimotor pattern. Specifically, an attached pendulum pilots the arm into a circular motion, a half-filled bottle produces axis oriented shaking behavior, a wheel is getting rotated, and wiping patterns emerge automatically in a table-plus-brush setting. By these object-specific dynamical patterns, the robot may be said to recognize the object's identity, or in other words, it discovers dynamical affordances of objects. Furthermore, when including hand coordinates obtained from a camera, a dedicated hand-eye coordination self-organizes spontaneously. These phenomena are discussed from a specific dynamical system perspective. Central is the dedicated working regime at the border to instability with its potentially infinite reservoir of (limit cycle) attractors "waiting" to be excited. Besides converging toward one of these attractors, variate behavior is also arising from a self-induced attractor morphing driven by the learning rule. We claim that experimental investigations with this anthropomorphic, self-learning robot not only generate interesting and potentially useful behaviors, but may also help to better understand what subjective human muscle feelings are, how they can be rooted in sensorimotor patterns, and how these concepts may feed back on robotics.},
author = {Der, Ralf and Martius, Georg S},
issn = {16625218},
journal = {Frontiers in Neurorobotics},
number = {MAR},
publisher = {Frontiers Research Foundation},
title = {{Self organized behavior generation for musculoskeletal robots}},
doi = {10.3389/fnbot.2017.00008},
volume = {11},
year = {2017},
}
@article{2016,
abstract = {The Ising model is one of the simplest and most famous models of interacting systems. It was originally proposed to model ferromagnetic interactions in statistical physics and is now widely used to model spatial processes in many areas such as ecology, sociology, and genetics, usually without testing its goodness-of-fit. Here, we propose an exact goodness-of-fit test for the finite-lattice Ising model. The theory of Markov bases has been developed in algebraic statistics for exact goodness-of-fit testing using a Monte Carlo approach. However, this beautiful theory has fallen short of its promise for applications, because finding a Markov basis is usually computationally intractable. We develop a Monte Carlo method for exact goodness-of-fit testing for the Ising model which avoids computing a Markov basis and also leads to a better connectivity of the Markov chain and hence to a faster convergence. We show how this method can be applied to analyze the spatial organization of receptors on the cell membrane.},
author = {Martin Del Campo Sanchez, Abraham and Cepeda Humerez, Sarah A and Uhler, Caroline},
issn = {03036898},
journal = {Scandinavian Journal of Statistics},
number = {2},
pages = {285 -- 306},
publisher = {Wiley-Blackwell},
title = {{Exact goodness-of-fit testing for the Ising model}},
doi = {10.1111/sjos.12251},
volume = {44},
year = {2017},
}