A general framework dedicated to computational morphogenesis Part II – Knowledge representation and architecture

Siregar, Pridi; Julen, Nathalie; Hufnagl, Peter; Mutter, George L.

doi:10.1016/j.biosystems.2018.07.002

Cited by 3 publications

(5 citation statements)

References 78 publications

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“…On the other hand, as per the words of Siregar et al (2018), the inclusion of Knowledge representation framework also creates a positive impact on the efficiency level of CAISOR agenda through the ability to summarize the collected data. However, the implication of this framework also supports the AI system to understand the prospective CAISOR agenda that supports them to work according to the proposed agenda.…”

Section: Resultsmentioning

confidence: 99%

“…Both of these components demonstrate intelligence in computer people. Both components are autonomous, but yet interconnected however, planning and implementation depend on the examination of representation and reasoning of knowledge.On the other hand, as per the words ofSiregar et al (2018), the inclusion of Knowledge representation framework also creates a positive impact on the efficiency level of CAISOR agenda through the ability to summarize the collected data. However, the implication of this framework also supports the AI system to understand the prospective CAISOR agenda that…”

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confidence: 99%

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Aspect of the Knowledge Representation Framework (KR Framework) for Offering the Basis for a Number of Research Activities in the Caisor

Swetha¹

2022

TTIDMKD

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The representation of knowledge refers to the technical challenge of capturing human understanding and thinking in a conceptual framework that enables computer networks to handle information. This research study is aimed to critically analyse the impact of the knowledge representation framework on the efficiency level of CAISOR activity. The research study has provided a sustainable viewpoint about the way KRF presents visualization data that is easy to understand for the human as well as the CAISOR agenda. A wide variety of different initiatives are founded on the CAISOR agenda through the Knowledge Representation Framework. The KR model covers many features of structured information representation ranging from simple facts to sophisticated formulations and programs. In addition, the research study has followed a secondary qualitative data collection method to collect the secondary sources of data regarding the research topic. Keyword : CAISOR, framework, AI system, Knowledge representation framework.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Aspect of the Knowledge Representation Framework (KR Framework) for Offering the Basis for a Number of Research Activities in the Caisor

Swetha¹

2022

TTIDMKD

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“…Finally, recent computational methods modeling yet more specialized aspects of intra-cellular processes lead to whole cell modeling, typically combining a large number of different methods into one complex algorithmic framework, requiring tuning hundreds or thousands of individual parameters [ 10 , 34 , 38 ], with high computational demands and the consequent computational infeasibility. By contrast, here a model is shown where the interplay between biochemical and morphogenetical control of growth processes can result in a much simpler model with just a few parameters, while still providing fairly realistic macroscopic observables consistent with the modeled biological phenomenon, both qualitatively and quantitatively as well.…”

Section: Methodsmentioning

confidence: 99%

“…Building computational models of morphogenesis has been explored ever since in a variety of ways and directions. They range from highly specific and arguably physically realistic models for specific morphogenetic mechanisms or organisms [ 3 , 4 , 5 , 6 ], to general approaches and models of entire biological units such as a living cell or a full organism [ 7 , 8 ], to broadly encompassing theoretical frameworks to address this fundamental question [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Self-Controlled and Self-Healing Model of Bacterial Cells

et al. 2022

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A new kind of self-assembly model, morphogenetic (M) systems, assembles spatial units into larger structures through local interactions of simpler components and enables discovery of new principles for cellular membrane assembly, development, and its interface function. The model is based on interactions among three kinds of constitutive objects such as tiles and protein-like elements in discrete time and continuous 3D space. It was motivated by achieving a balance between three conflicting goals: biological, physical-chemical, and computational realism. A recent example is a unified model of morphogenesis of a single biological cell, its membrane and cytoskeleton formation, and finally, its self-reproduction. Here, a family of dynamic M systems (Mbac) is described with similar characteristics, modeling the process of bacterial cell formation and division that exhibits bacterial behaviors of living cells at the macro-level (including cell growth that is self-controlled and sensitive to the presence/absence of nutrients transported through membranes), as well as self-healing properties. Remarkably, it consists of only 20 or so developmental rules. Furthermore, since the model exhibits membrane formation and septic mitosis, it affords more rigorous definitions of concepts such as injury and self-healing that enable quantitative analyses of these kinds of properties. Mbac shows that self-assembly and interactions of living organisms with their environments and membrane interfaces are critical for self-healing, and that these properties can be defined and quantified more rigorously and precisely, despite their complexity.

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“…How does a developing embryo self-organize into a patterned structure arranging the various differentiated morphological features using input information from its own cells [ 1 , 2 ]? In essence, how does an embryo compute its own pattern [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ]? Morphogenesis, whether embryonic or regenerative, is an intriguing paradigm for computer science, in addition to biology and biomedicine, because it provides proof-of-principles of a dynamic, strongly embodied computational architecture [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

Manicka

Levin

2022

Entropy

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What information-processing strategies and general principles are sufficient to enable self-organized morphogenesis in embryogenesis and regeneration? We designed and analyzed a minimal model of self-scaling axial patterning consisting of a cellular network that develops activity patterns within implicitly set bounds. The properties of the cells are determined by internal ‘genetic’ networks with an architecture shared across all cells. We used machine-learning to identify models that enable this virtual mini-embryo to pattern a typical axial gradient while simultaneously sensing the set boundaries within which to develop it from homogeneous conditions—a setting that captures the essence of early embryogenesis. Interestingly, the model revealed several features (such as planar polarity and regenerative re-scaling capacity) for which it was not directly selected, showing how these common biological design principles can emerge as a consequence of simple patterning modes. A novel “causal network” analysis of the best model furthermore revealed that the originally symmetric model dynamically integrates into intercellular causal networks characterized by broken-symmetry, long-range influence and modularity, offering an interpretable macroscale-circuit-based explanation for phenotypic patterning. This work shows how computation could occur in biological development and how machine learning approaches can generate hypotheses and deepen our understanding of how featureless tissues might develop sophisticated patterns—an essential step towards predictive control of morphogenesis in regenerative medicine or synthetic bioengineering contexts. The tools developed here also have the potential to benefit machine learning via new forms of backpropagation and by leveraging the novel distributed self-representation mechanisms to improve robustness and generalization.

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A general framework dedicated to computational morphogenesis Part II – Knowledge representation and architecture

Cited by 3 publications

References 78 publications

Aspect of the Knowledge Representation Framework (KR Framework) for Offering the Basis for a Number of Research Activities in the Caisor

Aspect of the Knowledge Representation Framework (KR Framework) for Offering the Basis for a Number of Research Activities in the Caisor

A Self-Controlled and Self-Healing Model of Bacterial Cells

Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

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