Modeling Dynamics of Biological Systems with Deep Generative Neural Networks
Biological data often contains measurements of dynamic entities such as cells or organisms in various states of progression. However, biological systems are notoriously difficult to describe analytically due to their many interacting components, and in many cases, the technical challenge of taking longitudinal measurements. This leads to difficulties in studying the features of the dynamics, for examples the drivers of the transition. To address this problem, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. DyMoN is a neural network framework trained as a deep generative Markov model whose next state is a probability distribution based on the current state. DyMoN is well-suited to the idiosyncrasies of biological data, including noise, sparsity, and the lack of longitudinal measurements in many types of systems. Thus, DyMoN can be trained using probability distributions derived from the data in any way, such as trajectories derived via dimensionality reduction methods, and does not require longitudinal measurements. We show the advantage of learning deep models over shallow models such as Kalman filters and hidden Markov models that do not learn representations of the data, both in terms of learning embeddings of the data and also in terms training efficiency, accuracy and ability to multitask. We perform three case studies of applying DyMoN to different types of biological systems and extracting features of the dynamics in each case by examining the learned model.
 This paper describes a NN method called DyMoN for predicting transition vectors between states with a Markov process, and learning dynamics of stochastic systems. Three biological case studies are presented, it is unclear if any new biology was learned from these cases that we could not have learned using other methods, and how accurate they are. The empirical validations are all on nonbiological data and disconnected from the first part of the paper, making the main application/advantage of this method confusing. I agree with the computational advantages mentioned in the paper, however, interpretation of the representational aspect is challenging especially in the context of biological systems. Regarding denoising, what are the guarantees that this approach does now remove real biological heterogeneity? Also, a denoising method (MAGIC) was still used to preprocess the data prior to DyMon, there is no discussion about any contradictory assumptions. Overall, the main shortcoming of the paper is lack of performance evaluation, comparison to other methods and clarifying advantages or novel results over other methods. The description of the method could also be improved and clarified with presenting an algorithm.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Modeling Dynamics of Biological Systems with Deep Generative Neural Networks
Biological data often contains measurements of dynamic entities such as cells or organisms in various states of progression. However, biological systems are notoriously difficult to describe analytically due to their many interacting components, and in many cases, the technical challenge of taking longitudinal measurements. This leads to difficulties in studying the features of the dynamics, for examples the drivers of the transition. To address this problem, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. DyMoN is a neural network framework trained as a deep generative Markov model whose next state is a probability distribution based on the current state. DyMoN is well-suited to the idiosyncrasies of biological data, including noise, sparsity, and the lack of longitudinal measurements in many types of systems. Thus, DyMoN can be trained using probability distributions derived from the data in any way, such as trajectories derived via dimensionality reduction methods, and does not require longitudinal measurements. We show the advantage of learning deep models over shallow models such as Kalman filters and hidden Markov models that do not learn representations of the data, both in terms of learning embeddings of the data and also in terms training efficiency, accuracy and ability to multitask. We perform three case studies of applying DyMoN to different types of biological systems and extracting features of the dynamics in each case by examining the learned model.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper describes a NN method called DyMoN for predicting transition vectors between states with a Markov process, and learning dynamics of stochastic systems. Three biological case studies are presented, it is unclear if any new biology was learned from these cases that we could not have learned using other methods, and how accurate they are. The empirical validations are all on nonbiological data and disconnected from the first part of the paper, making the main application/advantage of this method confusing. I agree with the computational advantages mentioned in the paper, however, interpretation of the representational aspect is challenging especially in the context of biological systems. Regarding denoising, what are the guarantees that this approach does now remove real biological heterogeneity? Also, a denoising method (MAGIC) was still used to preprocess the data prior to DyMon, there is no discussion about any contradictory assumptions. Overall, the main shortcoming of the paper is lack of performance evaluation, comparison to other methods and clarifying advantages or novel results over other methods. The description of the method could also be improved and clarified with presenting an algorithm.
assistant
 This paper describes a NN method called DyMoN for predicting transition vectors between states with a Markov process, and learning dynamics of stochastic systems. Three biological case studies are presented, it is unclear if any new biology was learned from these cases that we could not have learned using other methods, and how accurate they are. The empirical validations are all on nonbiological data and disconnected from the first part of the paper, making the main application/advantage of this method confusing. I agree with the computational advantages mentioned in the paper, however, interpretation of the representational aspect is challenging especially in the context of biological systems. Regarding denoising, what are the guarantees that this approach does now remove real biological heterogeneity? Also, a denoising method (MAGIC) was still used to preprocess the data prior to DyMon, there is no discussion about any contradictory assumptions. Overall, the main shortcoming of the paper is lack of performance evaluation, comparison to other methods and clarifying advantages or novel results over other methods. The description of the method could also be improved and clarified with presenting an algorithm.