OpenWorm: overview and recent advances in integrative biological simulation of
            <i>Caenorhabditis elegans</i>

Sarma, Gopal P.; Lee, Chee Wai; Portegys, Tom; Ghayoomie, Vahid; Jacobs, Travis W; Alicea, Bradly; Carandini, Matteo; Currie, Michael; Gerkin, Richard C.; Gingell, Shane; Gleeson, Padraig; Gordon, Richard; Hasani, Ramin; Idili, Giovanni; Khayrulin, Sergey; Lung, David; Palyanov, Andrey; Watts, Mark D; Larson, Stephen

doi:10.1098/rstb.2017.0382

Cited by 81 publications

(65 citation statements)

References 60 publications

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“…Despite valuable insights gained from contemplating comparative aspects of animal culture, another overlooked or forgotten nascent culturethe culture of intelligent machinesalso demands scrutiny and might better inform Veissière et al's variational approach to cognition and culture. Intelligent machines notably deliver prospective high-performance computational platforms to model and emulate all known organic forms of Earth life (e.g., Chew et al 2014;Derex & Boyd 2015;Fan & Markram 2019;Fortuna et al 2013;Fung 2015;Gillings et al 2016;Lenski et al 1999;Libin & Libin 2005;Millar et al 2019;Ranjan et al 2019;Sarma et al 2018). They, moreover, render virtually-augmented eco-evolutionary spaces to prototype, replicate, and adapt digital life or e-life (i.e., electronic life), new provocative programmable in silico forms of life thought capable of taxa-blurring sophisticated intelligent agency and culture often celebrated and feared by fantasists and futurists (cf.…”

Section: Introductionmentioning

confidence: 99%

Thinking through other minds: A variational approach to cognition and culture

et al. 2019

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The processes underwriting the acquisition of culture remain unclear. How are shared habits, norms, and expectations learned and maintained with precision and reliability across large-scale sociocultural ensembles? Is there a unifying account of the mechanisms involved in the acquisition of culture? Notions such as “shared expectations,” the “selective patterning of attention and behaviour,” “cultural evolution,” “cultural inheritance,” and “implicit learning” are the main candidates to underpin a unifying account of cognition and the acquisition of culture; however, their interactions require greater specification and clarification. In this article, we integrate these candidates using the variational (free-energy) approach to human cognition and culture in theoretical neuroscience. We describe the construction by humans of social niches that afford epistemic resources called cultural affordances. We argue that human agents learn the shared habits, norms, and expectations of their culture through immersive participation in patterned cultural practices that selectively pattern attention and behaviour. We call this process “thinking through other minds” (TTOM) – in effect, the process of inferring other agents’ expectations about the world and how to behave in social context. We argue that for humans, information from and about other people's expectations constitutes the primary domain of statistical regularities that humans leverage to predict and organize behaviour. The integrative model we offer has implications that can advance theories of cognition, enculturation, adaptation, and psychopathology. Crucially, this formal (variational) treatment seeks to resolve key debates in current cognitive science, such as the distinction between internalist and externalist accounts of theory of mind abilities and the more fundamental distinction between dynamical and representational accounts of enactivism.

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Section: Introductionmentioning

confidence: 99%

Thinking through other minds: A variational approach to cognition and culture

et al. 2019

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“…Design-build-test-learn workflows are an effective way to learn fundamental biology, design principles, and improved processes. The ideal workflow starts by using a predictive computational model, such as a whole-cell model [48] or a whole-organism model [67], to design a genome; proceeds by constructing, booting up, and testing this genome; and concludes by systematically learning from behavioral failures of the genome by using the data generated from testing the genome to improve the model, our biological knowledge, and the methods used to design, construct, and boot up the genome. One way to systematically learn better models is to identify the minimum set of changes that must be made to the model to align its predictions with observed behaviors of the synthetic genome.…”

Section: Learning Systematically From Test Resultsmentioning

confidence: 99%

Organizing genome engineering for the gigabase scale

et al. 2020

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Engineering the entire genome of an organism enables large-scale changes in organization, function, and external interactions, with significant implications for industry, medicine, and the environment. Improvements to DNA synthesis and organism engineering are already enabling substantial changes to organisms with megabase genomes, such as Escherichia coli and Saccharomyces cerevisiae. Simultaneously, recent advances in genome-scale modeling are increasingly informing the design of metabolic networks. However, major challenges remain for integrating these and other relevant technologies into workflows that can scale to the engineering of gigabase genomes.In particular, we find that a major under-recognized challenge is coordinating the flow of models, designs, constructs, and measurements across the large teams and complex technological systems that will likely be required for gigabase genome engineering. We recommend that the community address these challenges by 1) adopting and extending existing standards and technologies for representing and exchanging information at the gigabase genomic scale, 2) developing new technologies to address major open questions around data curation and quality control, 3) conducting fundamental research on the integration of modeling and design at the genomic scale, and 4) developing new legal and contractual infrastructure to better enable collaboration across multiple institutions.

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“…Ion channel data collected from WormBase ( Harris et al, 2019 ) are used to identify key genes, associated cells, and annotations. Determining the relationships between functional gene identities, specific ion channels, and functional annotations will be enhanced by using data sets (see section “Materials and Methods”) developed at the OpenWorm Foundation ( Sarma et al, 2018 ). This analysis yields information regarding ion channel types present at a specific time point in embryogenesis.…”

Section: Introductionmentioning

confidence: 99%

Raising the Connectome: The Emergence of Neuronal Activity and Behavior in Caenorhabditis elegans

Alicea

2020

Front. Cell. Neurosci.

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The differentiation of neurons and formation of connections between cells is the basis of both the adult phenotype and behaviors tied to cognition, perception, reproduction, and survival. Such behaviors are associated with local (circuits) and global (connectome) brain networks. A solid understanding of how these networks emerge is critical. This opinion piece features a guided tour of early developmental events in the emerging connectome, which is crucial to a new view on the connectogenetic process. Connectogenesis includes associating cell identities with broader functional and developmental relationships. During this process, the transition from developmental cells to terminally differentiated cells is defined by an accumulation of traits that ultimately results in neuronal-driven behavior. The well-characterized developmental and cell biology of Caenorhabditis elegans will be used to build a synthesis of developmental events that result in a functioning connectome. Specifically, our view of connectogenesis enables a first-mover model of synaptic connectivity to be demonstrated using data representing larval synaptogenesis. In a first-mover model of Stackelberg competition, potential pre- and postsynaptic relationships are shown to yield various strategies for establishing various types of synaptic connections. By comparing these results to what is known regarding principles for establishing complex network connectivity, these strategies are generalizable to other species and developmental systems. In conclusion, we will discuss the broader implications of this approach, as what is presented here informs an understanding of behavioral emergence and the ability to simulate related biological phenomena.

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OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans

Cited by 81 publications

References 60 publications

Thinking through other minds: A variational approach to cognition and culture

Thinking through other minds: A variational approach to cognition and culture

Organizing genome engineering for the gigabase scale

Raising the Connectome: The Emergence of Neuronal Activity and Behavior in Caenorhabditis elegans

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