Visualising 2-simplex formation in metabolic reactions

Piangerelli, Marco; Maestri, Stefano; Merelli, Emanuela

doi:10.1016/j.jmgm.2020.107576

Cited by 11 publications

(9 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar results might also be reached by empowering the capabilities of the agent-based approach with methods from other disciplines. Among them, the topological data analysis, already used to better understand enzymatic reactions through ABMs 35 , may provide the current model with a many-body perspective. Shape calculus and hierarchical structures can be applied to represent molecular conformations and increase the accuracy of the interactions between cognate-partners; these approaches would allow modelling the geometry of molecules' shapes and collisions with a higher precision 43,44 .…”

Section: Discussionmentioning

confidence: 99%

“…If such a complex represents a saturated enzyme, it waits an amount of time corresponding to the reciprocal of its k cat value and then release the final product (or products) of the reaction; otherwise, it moves and acts on the environment to bind the metabolite needed to reach saturation. This modelling approach is based on the construction of a reaction automaton, introduced in a previous work 35 and formalised in Section 2.1 of the Supplementary Information.…”

Section: /16mentioning

confidence: 99%

See 1 more Smart Citation

Agent-based Models for Detecting the Driving Forces of Biomolecular Interactions

Maestri¹,

Merelli²,

Pettini³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Agent-based modelling and simulation have been effectively applied to the study of complex biological systems, especially when composed by a large number of interacting entities. Representing biomolecules as autonomous agents allows this approach to bring out the global behaviour of biochemical processes as resulting from local molecular interactions. In this paper, we leverage the capabilities of the agent paradigm to construct an in silico replica of the glycolytic pathway of baker’s yeasts; the aim is to detect the role that long-range electrodynamic forces might have on the rate of glucose oxidation. Experimental evidences have shown that random encounters and short-range potentials might not be sufficient to explain the high efficiency of biochemical reactions in living cells. However, while the latest in vitro studies are limited by the present-day technology, agent-based simulations provide an in silico support to the outcomes hitherto obtained and shed light on behaviours not yet well understood. Our results reveal to be able to grasp properties hard to uncover through other computational methods, such as the effect of electromagnetic potentials on glycolytic oscillations.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: /16mentioning

confidence: 99%

Agent-based Models for Detecting the Driving Forces of Biomolecular Interactions

Maestri¹,

Merelli²,

Pettini³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the proposed approach is strongly theoretical, it can be expanded in order to specify the behaviour of interacting molecules in a multiagent simulation [12]. Further improvements of this work will also model the reversibility of perception-enabled reaction systems and introduce a way to influence the reaction direction through measurement values [16,17].…”

Section: Discussionmentioning

confidence: 99%

“…As part of an agent-based model of a biochemical reaction, we described this pattern through a basic reaction automaton [12]. It was intended just to support the interaction phases accounted in the agent-based model and does not explicitly consider agent's perception, as well as ignores the effects of enzyme regulation on the generation of the reaction products.…”

Section: Introductionmentioning

confidence: 99%

Automaton of molecular perceptions in biochemical reactions

Maestri¹,

Merelli²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Local interactions among biomolecules, and the role played by their environment, have gained increasing attention in modelling biochemical reactions. By defining the automaton of molecular perceptions, we explore an agent-based representation of the behaviour of biomolecules in living cells. Our approach considers the capability of a molecule to perceive its surroundings a key property of bimolecular interactions, which we investigate from a theoretical perspective.Graph-based reaction systems are then leveraged to abstract enzyme regulation as a result of the influence exerted by the environment on a catalysed reaction.By combining these methods, we aim at overcoming some limitations of current kinetic models, which do not take into account local molecular interactions and the way they are affected by the reaction environment.

show abstract

“…An agent represents an active component of the system, such as an immune or a tumor cell, whereas the environment is the representation of the peritumoral and intratumoral environment. In our work, the emerging properties are observable from the RCC model simulation, whose dynamics is expressed following the interaction-as-perception paradigm (Piangerelli et al, 2020 ) declined in this context: whenever a cell agent perceives, another compatible cell agent moves toward to interact with it, activating or inhibiting the production of compatible cell agents. All the agents' interactions that characterize the immune response are a dynamic representation of the cell-cell interaction network bounded by the tumor environment and regulated by the obesity index BMI modeled as a parametrized 3D space.…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Learning Model for the Obesity Paradox in RCC

Belenchia

Rocchetti

Maestri

et al. 2021

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

A recent study on the immunotherapy treatment of renal cell carcinoma reveals better outcomes in obese patients compared to lean subjects. This enigmatic contradiction has been explained, in the context of the debated obesity paradox, as the effect produced by the cell-cell interaction network on the tumor microenvironment during the immune response. To better understand this hypothesis, we provide a computational framework for the in silico study of the tumor behavior. The starting model of the tumor, based on the cell-cell interaction network, has been described as a multiagent system, whose simulation generates the hypothesized effects on the tumor microenvironment. The medical needs in the immunotherapy design meet the capabilities of a multiagent simulator to reproduce the dynamics of the cell-cell interaction network, meaning a reaction to environmental changes introduced through the experimental data.

show abstract

Visualising 2-simplex formation in metabolic reactions

Cited by 11 publications

References 25 publications

Agent-based Models for Detecting the Driving Forces of Biomolecular Interactions

Agent-based Models for Detecting the Driving Forces of Biomolecular Interactions

Automaton of molecular perceptions in biochemical reactions

Agent-Based Learning Model for the Obesity Paradox in RCC

Contact Info

Product

Resources

About