--- _id: '1538' abstract: - lang: eng text: Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time. acknowledgement: 'J.R., F.P., and J.L. acknowledge support from the European Commission under the Network of Excellence HYCON2 (highly-complex and networked control systems) and SystemsX.ch under the SignalX Project. J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges support from Human Frontier Science Program Grant RP0061/2011 (www.hfsp.org). ' author: - first_name: Jakob full_name: Ruess, Jakob id: 4A245D00-F248-11E8-B48F-1D18A9856A87 last_name: Ruess orcid: 0000-0003-1615-3282 - first_name: Francesca full_name: Parise, Francesca last_name: Parise - first_name: Andreas full_name: Milias Argeitis, Andreas last_name: Milias Argeitis - first_name: Mustafa full_name: Khammash, Mustafa last_name: Khammash - first_name: John full_name: Lygeros, John last_name: Lygeros citation: ama: Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 2015;112(26):8148-8153. doi:10.1073/pnas.1423947112 apa: Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., & Lygeros, J. (2015). Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1423947112 chicago: Ruess, Jakob, Francesca Parise, Andreas Milias Argeitis, Mustafa Khammash, and John Lygeros. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1423947112. ieee: J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative experiment design guides the characterization of a light-inducible gene expression circuit,” PNAS, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153, 2015. ista: Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. 2015. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 112(26), 8148–8153. mla: Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” PNAS, vol. 112, no. 26, National Academy of Sciences, 2015, pp. 8148–53, doi:10.1073/pnas.1423947112. short: J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112 (2015) 8148–8153. date_created: 2018-12-11T11:52:36Z date_published: 2015-06-30T00:00:00Z date_updated: 2021-01-12T06:51:27Z day: '30' department: - _id: ToHe - _id: GaTk doi: 10.1073/pnas.1423947112 ec_funded: 1 external_id: pmid: - '26085136' intvolume: ' 112' issue: '26' language: - iso: eng main_file_link: - open_access: '1' url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/ month: '06' oa: 1 oa_version: Submitted Version page: 8148 - 8153 pmid: 1 project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: PNAS publication_status: published publisher: National Academy of Sciences publist_id: '5633' quality_controlled: '1' scopus_import: 1 status: public title: Iterative experiment design guides the characterization of a light-inducible gene expression circuit type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 112 year: '2015' ... --- _id: '1564' article_number: '145' author: - first_name: Matthieu full_name: Gilson, Matthieu last_name: Gilson - first_name: Cristina full_name: Savin, Cristina id: 3933349E-F248-11E8-B48F-1D18A9856A87 last_name: Savin - first_name: Friedemann full_name: Zenke, Friedemann last_name: Zenke citation: ama: 'Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 2015;9(11). doi:10.3389/fncom.2015.00145' apa: 'Gilson, M., Savin, C., & Zenke, F. (2015). Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. Frontiers Research Foundation. https://doi.org/10.3389/fncom.2015.00145' chicago: 'Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience. Frontiers Research Foundation, 2015. https://doi.org/10.3389/fncom.2015.00145.' ieee: 'M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation from the interaction of different forms of plasticity,” Frontiers in Computational Neuroscience, vol. 9, no. 11. Frontiers Research Foundation, 2015.' ista: 'Gilson M, Savin C, Zenke F. 2015. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 9(11), 145.' mla: 'Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience, vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:10.3389/fncom.2015.00145.' short: M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9 (2015). date_created: 2018-12-11T11:52:45Z date_published: 2015-11-30T00:00:00Z date_updated: 2021-01-12T06:51:37Z day: '30' ddc: - '570' department: - _id: GaTk doi: 10.3389/fncom.2015.00145 ec_funded: 1 file: - access_level: open_access checksum: cea73b6d3ef1579f32da10b82f4de4fd content_type: application/pdf creator: system date_created: 2018-12-12T10:12:09Z date_updated: 2020-07-14T12:45:02Z file_id: '4927' file_name: IST-2016-479-v1+1_fncom-09-00145.pdf file_size: 187038 relation: main_file file_date_updated: 2020-07-14T12:45:02Z has_accepted_license: '1' intvolume: ' 9' issue: '11' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '11' oa: 1 oa_version: Published Version project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: Frontiers in Computational Neuroscience publication_status: published publisher: Frontiers Research Foundation publist_id: '5607' pubrep_id: '479' quality_controlled: '1' scopus_import: 1 status: public title: 'Editorial: Emergent neural computation from the interaction of different forms of plasticity' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 9 year: '2015' ... --- _id: '1570' abstract: - lang: eng text: Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no systemspecific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking,which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution. author: - first_name: Ralf full_name: Der, Ralf last_name: Der - first_name: Georg S full_name: Martius, Georg S id: 3A276B68-F248-11E8-B48F-1D18A9856A87 last_name: Martius citation: ama: Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112 apa: Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1508400112 chicago: Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1508400112. ieee: R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy of Sciences, pp. E6224–E6232, 2015. ista: Der R, Martius GS. 2015. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 112(45), E6224–E6232. mla: Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112. short: R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232. date_created: 2018-12-11T11:52:47Z date_published: 2015-11-10T00:00:00Z date_updated: 2021-01-12T06:51:40Z day: '10' department: - _id: ChLa - _id: GaTk doi: 10.1073/pnas.1508400112 ec_funded: 1 external_id: pmid: - '26504200' intvolume: ' 112' issue: '45' language: - iso: eng main_file_link: - open_access: '1' url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/ month: '11' oa: 1 oa_version: Submitted Version page: E6224 - E6232 pmid: 1 project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: PNAS publication_status: published publisher: National Academy of Sciences publist_id: '5601' quality_controlled: '1' scopus_import: 1 status: public title: Novel plasticity rule can explain the development of sensorimotor intelligence type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 112 year: '2015' ... --- _id: '1658' abstract: - lang: eng text: Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space. alternative_title: - LNCS author: - first_name: Sergiy full_name: Bogomolov, Sergiy id: 369D9A44-F248-11E8-B48F-1D18A9856A87 last_name: Bogomolov orcid: 0000-0002-0686-0365 - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000−0002−2985−7724 - first_name: Andreas full_name: Podelski, Andreas last_name: Podelski - first_name: Jakob full_name: Ruess, Jakob id: 4A245D00-F248-11E8-B48F-1D18A9856A87 last_name: Ruess orcid: 0000-0003-1615-3282 - first_name: Christian full_name: Schilling, Christian last_name: Schilling citation: ama: Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. Adaptive moment closure for parameter inference of biochemical reaction networks. 2015;9308:77-89. doi:10.1007/978-3-319-23401-4_8 apa: 'Bogomolov, S., Henzinger, T. A., Podelski, A., Ruess, J., & Schilling, C. (2015). Adaptive moment closure for parameter inference of biochemical reaction networks. Presented at the CMSB: Computational Methods in Systems Biology, Nantes, France: Springer. https://doi.org/10.1007/978-3-319-23401-4_8' chicago: Bogomolov, Sergiy, Thomas A Henzinger, Andreas Podelski, Jakob Ruess, and Christian Schilling. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Lecture Notes in Computer Science. Springer, 2015. https://doi.org/10.1007/978-3-319-23401-4_8. ieee: S. Bogomolov, T. A. Henzinger, A. Podelski, J. Ruess, and C. Schilling, “Adaptive moment closure for parameter inference of biochemical reaction networks,” vol. 9308. Springer, pp. 77–89, 2015. ista: Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. 2015. Adaptive moment closure for parameter inference of biochemical reaction networks. 9308, 77–89. mla: Bogomolov, Sergiy, et al. Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks. Vol. 9308, Springer, 2015, pp. 77–89, doi:10.1007/978-3-319-23401-4_8. short: S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015) 77–89. conference: end_date: 2015-09-18 location: Nantes, France name: 'CMSB: Computational Methods in Systems Biology' start_date: 2015-09-16 date_created: 2018-12-11T11:53:18Z date_published: 2015-09-01T00:00:00Z date_updated: 2023-02-21T16:17:24Z day: '01' department: - _id: ToHe - _id: GaTk doi: 10.1007/978-3-319-23401-4_8 ec_funded: 1 intvolume: ' 9308' language: - iso: eng month: '09' oa_version: None page: 77 - 89 project: - _id: 25EE3708-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '267989' name: Quantitative Reactive Modeling - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize - _id: 25832EC2-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: S 11407_N23 name: Rigorous Systems Engineering - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication_status: published publisher: Springer publist_id: '5492' quality_controlled: '1' related_material: record: - id: '1148' relation: later_version status: public scopus_import: 1 series_title: Lecture Notes in Computer Science status: public title: Adaptive moment closure for parameter inference of biochemical reaction networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 9308 year: '2015' ... --- _id: '1697' abstract: - lang: eng text: Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits. acknowledgement: 'This work was supported by grants EY 014196 and EY 017934 to MJB, ANR OPTIMA, the French State program Investissements d’Avenir managed by the Agence Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65], and by a EC grant from the Human Brain Project (CLAP) to OM, the Austrian Research Foundation FWF P25651 to VBS and GT. VBS is partially supported by contracts MEC, Spain (Grant No. AYA2010- 22111-C03-02, Grant No. AYA2013-48623-C2-2 and FEDER Funds).' article_number: e1004304 author: - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Vicente full_name: Botella Soler, Vicente id: 421234E8-F248-11E8-B48F-1D18A9856A87 last_name: Botella Soler orcid: 0000-0002-8790-1914 - first_name: Kristina full_name: Simmons, Kristina last_name: Simmons - first_name: Thierry full_name: Mora, Thierry last_name: Mora - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Michael full_name: Berry, Michael last_name: Berry citation: ama: Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 2015;11(7). doi:10.1371/journal.pcbi.1004304 apa: Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., & Berry, M. (2015). High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004304 chicago: Marre, Olivier, Vicente Botella Soler, Kristina Simmons, Thierry Mora, Gašper Tkačik, and Michael Berry. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” PLoS Computational Biology. Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004304. ieee: O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, and M. Berry, “High accuracy decoding of dynamical motion from a large retinal population,” PLoS Computational Biology, vol. 11, no. 7. Public Library of Science, 2015. ista: Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. 2015. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 11(7), e1004304. mla: Marre, Olivier, et al. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” PLoS Computational Biology, vol. 11, no. 7, e1004304, Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004304. short: O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS Computational Biology 11 (2015). date_created: 2018-12-11T11:53:31Z date_published: 2015-07-01T00:00:00Z date_updated: 2021-01-12T06:52:35Z day: '01' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1004304 file: - access_level: open_access checksum: 472b979f3f1cffb37b3e503f085115ca content_type: application/pdf creator: system date_created: 2018-12-12T10:16:25Z date_updated: 2020-07-14T12:45:12Z file_id: '5212' file_name: IST-2016-455-v1+1_journal.pcbi.1004304.pdf file_size: 4673930 relation: main_file file_date_updated: 2020-07-14T12:45:12Z has_accepted_license: '1' intvolume: ' 11' issue: '7' language: - iso: eng month: '07' oa: 1 oa_version: Published Version project: - _id: 254D1A94-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 25651-N26 name: Sensitivity to higher-order statistics in natural scenes publication: PLoS Computational Biology publication_status: published publisher: Public Library of Science publist_id: '5447' pubrep_id: '455' quality_controlled: '1' scopus_import: 1 status: public title: High accuracy decoding of dynamical motion from a large retinal population tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 11 year: '2015' ... --- _id: '1701' abstract: - lang: eng text: 'The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. ' acknowledgement: "Research was supported in part by National Science Foundation Grants PHY-1305525, PHY-1451171, and CCF-0939370, by National Institutes of Health Grant R01 EY14196, and by Austrian Science Foundation Grant FWF P25651. Additional support was provided by the\r\nFannie and John Hertz Foundation, by the Swartz Foundation, by the W. M. Keck Foundation, and by the Simons Foundation." author: - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Thierry full_name: Mora, Thierry last_name: Mora - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Dario full_name: Amodei, Dario last_name: Amodei - first_name: Stephanie full_name: Palmer, Stephanie last_name: Palmer - first_name: Michael full_name: Berry Ii, Michael last_name: Berry Ii - first_name: William full_name: Bialek, William last_name: Bialek citation: ama: Tkačik G, Mora T, Marre O, et al. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 2015;112(37):11508-11513. doi:10.1073/pnas.1514188112 apa: Tkačik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., & Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of neurons. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1514188112 chicago: Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer, Michael Berry Ii, and William Bialek. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1514188112. ieee: G. Tkačik et al., “Thermodynamics and signatures of criticality in a network of neurons,” PNAS, vol. 112, no. 37. National Academy of Sciences, pp. 11508–11513, 2015. ista: Tkačik G, Mora T, Marre O, Amodei D, Palmer S, Berry Ii M, Bialek W. 2015. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 112(37), 11508–11513. mla: Tkačik, Gašper, et al. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” PNAS, vol. 112, no. 37, National Academy of Sciences, 2015, pp. 11508–13, doi:10.1073/pnas.1514188112. short: G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek, PNAS 112 (2015) 11508–11513. date_created: 2018-12-11T11:53:33Z date_published: 2015-09-15T00:00:00Z date_updated: 2021-01-12T06:52:37Z day: '15' department: - _id: GaTk doi: 10.1073/pnas.1514188112 external_id: pmid: - '26330611' intvolume: ' 112' issue: '37' language: - iso: eng main_file_link: - open_access: '1' url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/ month: '09' oa: 1 oa_version: Submitted Version page: 11508 - 11513 pmid: 1 project: - _id: 254D1A94-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 25651-N26 name: Sensitivity to higher-order statistics in natural scenes publication: PNAS publication_status: published publisher: National Academy of Sciences publist_id: '5440' quality_controlled: '1' scopus_import: 1 status: public title: Thermodynamics and signatures of criticality in a network of neurons type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 112 year: '2015' ... --- _id: '1861' abstract: - lang: eng text: Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance. acknowledgement: "HYCON2; EC; European Commission\r\n" article_number: '8' author: - first_name: Jakob full_name: Ruess, Jakob id: 4A245D00-F248-11E8-B48F-1D18A9856A87 last_name: Ruess orcid: 0000-0003-1615-3282 - first_name: John full_name: Lygeros, John last_name: Lygeros citation: ama: Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 2015;25(2). doi:10.1145/2688906 apa: Ruess, J., & Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. ACM. https://doi.org/10.1145/2688906 chicago: Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation. ACM, 2015. https://doi.org/10.1145/2688906. ieee: J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2. ACM, 2015. ista: Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2), 8. mla: Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2, 8, ACM, 2015, doi:10.1145/2688906. short: J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015). date_created: 2018-12-11T11:54:25Z date_published: 2015-02-01T00:00:00Z date_updated: 2021-01-12T06:53:41Z day: '01' department: - _id: ToHe - _id: GaTk doi: 10.1145/2688906 intvolume: ' 25' issue: '2' language: - iso: eng month: '02' oa_version: None publication: ACM Transactions on Modeling and Computer Simulation publication_status: published publisher: ACM publist_id: '5238' quality_controlled: '1' scopus_import: 1 status: public title: Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 25 year: '2015' ... --- _id: '1885' abstract: - lang: eng text: 'The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail. ' author: - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Julien full_name: Dubuis, Julien last_name: Dubuis - first_name: Mariela full_name: Petkova, Mariela last_name: Petkova - first_name: Thomas full_name: Gregor, Thomas last_name: Gregor citation: ama: 'Tkačik G, Dubuis J, Petkova M, Gregor T. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. 2015;199(1):39-59. doi:10.1534/genetics.114.171850' apa: 'Tkačik, G., Dubuis, J., Petkova, M., & Gregor, T. (2015). Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.114.171850' chicago: 'Tkačik, Gašper, Julien Dubuis, Mariela Petkova, and Thomas Gregor. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” Genetics. Genetics Society of America, 2015. https://doi.org/10.1534/genetics.114.171850.' ieee: 'G. Tkačik, J. Dubuis, M. Petkova, and T. Gregor, “Positional information, positional error, and readout precision in morphogenesis: A mathematical framework,” Genetics, vol. 199, no. 1. Genetics Society of America, pp. 39–59, 2015.' ista: 'Tkačik G, Dubuis J, Petkova M, Gregor T. 2015. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. 199(1), 39–59.' mla: 'Tkačik, Gašper, et al. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” Genetics, vol. 199, no. 1, Genetics Society of America, 2015, pp. 39–59, doi:10.1534/genetics.114.171850.' short: G. Tkačik, J. Dubuis, M. Petkova, T. Gregor, Genetics 199 (2015) 39–59. date_created: 2018-12-11T11:54:32Z date_published: 2015-01-01T00:00:00Z date_updated: 2021-01-12T06:53:50Z day: '01' department: - _id: GaTk doi: 10.1534/genetics.114.171850 intvolume: ' 199' issue: '1' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1404.5599 month: '01' oa: 1 oa_version: Preprint page: 39 - 59 publication: Genetics publication_status: published publisher: Genetics Society of America publist_id: '5210' quality_controlled: '1' scopus_import: 1 status: public title: 'Positional information, positional error, and readout precision in morphogenesis: A mathematical framework' type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 199 year: '2015' ... --- _id: '1940' abstract: - lang: eng text: We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the "input") by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively new regulatory strategy emerges where individual cells respond to the input in a nearly step-like fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models. article_number: '062710' author: - first_name: Thomas R full_name: Sokolowski, Thomas R id: 3E999752-F248-11E8-B48F-1D18A9856A87 last_name: Sokolowski orcid: 0000-0002-1287-3779 - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: Sokolowski TR, Tkačik G. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. 2015;91(6). doi:10.1103/PhysRevE.91.062710 apa: Sokolowski, T. R., & Tkačik, G. (2015). Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.91.062710 chicago: Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics, 2015. https://doi.org/10.1103/PhysRevE.91.062710. ieee: T. R. Sokolowski and G. Tkačik, “Optimizing information flow in small genetic networks. IV. Spatial coupling,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 91, no. 6. American Institute of Physics, 2015. ista: Sokolowski TR, Tkačik G. 2015. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. 91(6), 062710. mla: Sokolowski, Thomas R., and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 91, no. 6, 062710, American Institute of Physics, 2015, doi:10.1103/PhysRevE.91.062710. short: T.R. Sokolowski, G. Tkačik, Physical Review E Statistical Nonlinear and Soft Matter Physics 91 (2015). date_created: 2018-12-11T11:54:49Z date_published: 2015-06-15T00:00:00Z date_updated: 2021-01-12T06:54:13Z day: '15' department: - _id: GaTk doi: 10.1103/PhysRevE.91.062710 intvolume: ' 91' issue: '6' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1501.04015 month: '06' oa: 1 oa_version: Preprint publication: Physical Review E Statistical Nonlinear and Soft Matter Physics publication_status: published publisher: American Institute of Physics publist_id: '5145' quality_controlled: '1' scopus_import: 1 status: public title: Optimizing information flow in small genetic networks. IV. Spatial coupling type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 91 year: '2015' ... --- _id: '9718' article_processing_charge: No author: - first_name: Tamar full_name: Friedlander, Tamar id: 36A5845C-F248-11E8-B48F-1D18A9856A87 last_name: Friedlander - first_name: Avraham E. full_name: Mayo, Avraham E. last_name: Mayo - first_name: Tsvi full_name: Tlusty, Tsvi last_name: Tlusty - first_name: Uri full_name: Alon, Uri last_name: Alon citation: ama: Friedlander T, Mayo AE, Tlusty T, Alon U. Supporting information text. 2015. doi:10.1371/journal.pcbi.1004055.s001 apa: Friedlander, T., Mayo, A. E., Tlusty, T., & Alon, U. (2015). Supporting information text. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004055.s001 chicago: Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Supporting Information Text.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.s001. ieee: T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Supporting information text.” Public Library of Science, 2015. ista: Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Supporting information text, Public Library of Science, 10.1371/journal.pcbi.1004055.s001. mla: Friedlander, Tamar, et al. Supporting Information Text. Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004055.s001. short: T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015). date_created: 2021-07-26T08:35:23Z date_published: 2015-03-23T00:00:00Z date_updated: 2023-02-23T10:16:13Z day: '23' department: - _id: GaTk doi: 10.1371/journal.pcbi.1004055.s001 month: '03' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '1827' relation: used_in_publication status: public status: public title: Supporting information text type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2015' ...