PCOMPBIOL-D-25-01869

AgentBasedModeling.jl: a tool for stochastic simulation of structured population dynamics

PLOS Computational Biology

Dear Dr. Thomas,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Kind regards,

Qing Nie

Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: In this paper, the authors (Piho and Thomas) proposed a novel computational package for cell populations involving intracellular dynamics, cell division, cell-cell interactions, etc. The details of the package are clearly explained and demonstrated through several numerical examples.

In general, this paper is clearly written, and the package is useful for cell biological studies. The manuscript can be further improved by making the following points clearer.

— Eq. (2) and the interaction rules are not so clear to me. I can roughly understand that the interaction mechanisms could remove certain agents from the system and produce new ones. However, it is unclear what are removed and produced. This is particularly because the definition of S[x] is not given.

— Also, the difference between agents and agent types is confusing. It should be made clear whether Eq. (1) represents the dynamics of one agent of type S_j, or the j-th agent. If it is the former case, then the noise term W_j and Y_{r,j} should contain another notation to indicate that different agents are corrupted with different noise.

Reviewer #2: The manuscript “AgentBasedModeling.jl: a tool for stochastic simulation of structured population Dynamics” presents an agent based simulation tool that incorporates the stochasticity at both the single cell and population levels. The approach is technically correct and the applicability is wide. I have some observations on the setup for the examples of use, but in general I would recommend for publication with minor changes.

Main comments:

As our understanding of gene noise and its phenotypic consequences at the individual cell level deepens, and the importance of this at the population dynamics level becomes clearer, the need for simulation tools that allow for the exploration of this interplay becomes apparent. Previous tools such as SGNS2 and PyEcoLib have incorporated the stochasticity at the individual cell level, but are trickier to incorporate in a population level simulation. AgentBasedModeling.jl is a simulation tool that natively takes into account the interplay of the stochastic processes at both scales, so I believe it to be a useful, novel contribution.

Where I would suggest some changes is in the setup for the examples of use. In particular, in section 2.5.1 the authors simulate growth via an adder mechanism. This is a phenomenological model, not applicable in all cases, and is an emergent property which comes about as a statistical consequence of a specific underlying stochastic process. In particular, if the probability of division is proportional to the current size (with an exponent to include cases that are sizer-like or timer-like rather than pure adder) the adder effect comes out naturally. I mention this because this is precisely the kind of situation where an agent based stochastic model can naturally model the underlying process, so it seems wrong to use the emergent statistics as part of the base model rather than the stochastic process, as it requires a separate distribution of added volume that is independent of the cell state. It would in itself be a simple test/demonstration of the tool to have the adder come out of the cell state description.

Minor comments:

In the author summary the phrase “watch how their interactions give rise to collective behavior” seems more informal than the rest of the text

I would expand on the difficulties arising from the mismatch between stochastic timings when trying to use existing ABM tools within a larger stochastic population simulation. Many people don´t have an intuition for simulations without predefined time steps and might not realize the issue.

Reviewer #3: The manuscript "AgentBasedModeling.jl: a tool for stochastic simulation of structured population dynamics" presents a very general framework that integrates intracellular state dynamics with population-level interactions, allowing a wide range of biological models to be examined within a unified stochastic simulation framework. Overall, the structure of the manuscript is clear, and the proposed framework is well motivated and technically sound. The examples effectively demonstrate the flexibility and potential usefulness of the tool. While the work is valuable and generally well presented, several issues still need attention. I would strongly recommend publication once these issues have been addressed.

1. The notation system used in the manuscript is somewhat confusing and would benefit from clarification. For example, in Equation (2), the indices of the agent type labels and the corresponding state vectors are written in the same way, which may cause ambiguity. When considering processes such as cell division (e.g., Equation (3)), the two daughter cells generally have different state vectors, yet the notation inherited from Equation (2) may give the impression that the daughter cells share identical states. In Equation (7), both the extracellular and intracellular inducer molecules are denoted by $x$, which makes them difficult to distinguish. I recommend revising the notation to more clearly distinguish cell identities from their associated state vectors.

2. There are numerous typos throughout the manuscript that should be corrected to avoid confusion. In particular, the following issues need attention:

In Equation (1), $\sigma(x_j(s))$ should be $\sigma(x_j(t))$;

The caption for Figure 2 is missing descriptions for panels (e) and (f);

In Figure 3, the legend colors in panel (b) are reversed;

In Section 2.5.3, the symbols for intracellular and extracellular nutrients and phages are incorrect;

In Equation (10), the meaning of $\tau^+\sim\tau$ is unclear and should be clarified;

In Equation (12), the age $\tau$ of the agent participating in the reaction should be $0$;

In Equation (17), $S[a]$ should be $R[a]$ since the SIR model is considered here rather than the SIS model;

On Page 25, third line from the bottom, "Fig.5d" should be "Fig.5b".

3. The three sampling methods described in the manuscript are all based on Markovian dynamics. However, in Equation (11), the lysis time of an infected cell is assumed to follow a gamma distribution, which violates the Markov property. This inconsistency should be addressed or clarified.

4. The framework introduced at the beginning of the manuscript is quite abstract. I suggest adding 1-2 illustrative examples in Section 2.1 to help readers better understand the concepts. For instance, it would be helpful to clarify what an "agent" typically represents, how "agent types" are defined, and what is meant by a "channel" in this context.

5. After gene replication, the gene copy number increases from 1 to 2 in haploid cells (and from 2 to 4 in diploid cells). As a result, the gene expression dynamics may change substantially, since two gene copies are actively transcribed after replication, whereas only one is active before replication. Several modeling studies have explicitly incorporated this effect, such as

- Coupling gene expression dynamics to cell size dynamics and cell cycle events: exact and approximate solutions of the extended telegraph model, iScience 26(1):105746, 2023;

- Frequency domain analysis of fluctuations of mRNA and protein copy numbers within a cell lineage: theory and experimental validation, Physical Review X 11(2):021032, 2021.

Could the current modeling framework be extended to account for this important phenomenon?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: None

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Dear Dr Thomas,

We are pleased to inform you that your manuscript 'AgentBasedModeling.jl: a tool for stochastic simulation of structured population dynamics' has been provisionally accepted for publication in PLOS Computational Biology.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Qing Nie

Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have addressed all my concerns.

Reviewer #3: The authors have satisfactorily addressed my previous comments, as well as those raised by Reviewer #2. I therefore strongly recommend this paper for publication in PCB.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: None

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Reviewer #1: No

Reviewer #3: Yes: Chen Jia