Benjamin Qureshi, Jenny M. Poulton, Thomas E. Ouldridge
Far-from equilibrium molecular templating networks, like those that maintain the populations of RNA and protein molecules in the cell, are key biological motifs. These networks share the general property that assembled products are produced and degraded via complex pathways controlled by catalysts, including molecular templates. Although it has been suggested that the information propagated from templates to products sets a lower bound on the thermodynamic cost of these networks, this bound has not been explored rigorously to date. We show that, for an arbitrarily catalytic reaction network in steady state, the specificity with which a single product can dominate the ensemble is upper bounded, and the entropy of the product ensemble lower bounded, by a function of $Delta G$, the difference between the maximal and minimal free-energy changes along pathways to assembly. These simple bounds are particularly restrictive for systems with a smaller number of possible products $M$. Remarkably, however, although $Delta G$ constrains the information propagated to the product distribution, the systems that saturate the bound operate in a pseudo-equilibrium fashion, and there is no minimal entropy production rate for maintaining this non-equilibrium distribution. Moreover, for large systems, a vanishingly small subset of the possible products can dominate the product ensemble even for small values of $Delta G/ln M$.
{"title":"Information propagation in far-from-equilibrium molecular templating networks is optimised by pseudo-equilibrium systems with negligible dissipation","authors":"Benjamin Qureshi, Jenny M. Poulton, Thomas E. Ouldridge","doi":"arxiv-2404.02791","DOIUrl":"https://doi.org/arxiv-2404.02791","url":null,"abstract":"Far-from equilibrium molecular templating networks, like those that maintain\u0000the populations of RNA and protein molecules in the cell, are key biological\u0000motifs. These networks share the general property that assembled products are\u0000produced and degraded via complex pathways controlled by catalysts, including\u0000molecular templates. Although it has been suggested that the information\u0000propagated from templates to products sets a lower bound on the thermodynamic\u0000cost of these networks, this bound has not been explored rigorously to date. We\u0000show that, for an arbitrarily catalytic reaction network in steady state, the\u0000specificity with which a single product can dominate the ensemble is upper\u0000bounded, and the entropy of the product ensemble lower bounded, by a function\u0000of $Delta G$, the difference between the maximal and minimal free-energy\u0000changes along pathways to assembly. These simple bounds are particularly\u0000restrictive for systems with a smaller number of possible products $M$.\u0000Remarkably, however, although $Delta G$ constrains the information propagated\u0000to the product distribution, the systems that saturate the bound operate in a\u0000pseudo-equilibrium fashion, and there is no minimal entropy production rate for\u0000maintaining this non-equilibrium distribution. Moreover, for large systems, a\u0000vanishingly small subset of the possible products can dominate the product\u0000ensemble even for small values of $Delta G/ln M$.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bryan S. Hernandez, Patrick Vincent N. Lubenia, Eduardo R. Mendoza
This work introduces a new method for comparing two reaction networks of the same or closely related systems through their embedded networks in terms of the shared set of species. Hence, we call this method the Common Species Embedded Networks (CSEN) analysis. Using this approach, we conduct a comparison of existing reaction networks associated with Wnt signaling models (Lee, Schmitz, MacLean, and Feinberg) that we have identified. The analysis yields three important results for these Wnt models. First, the CSEN analysis of the Lee (mono-stationary) and Feinberg (multi-stationary) shows a strong similarity, justifying the study of the Feinberg model, which was a modified Lee model constructed to study an important network property called "concordance". It also challenge the absoluteness of discrimination of the models into mono-stationarity versus multi-stationarity, which is a main result of Maclean et al. (PNAS USA 2015). Second, the CSEN analysis provides evidence supporting a strong similarity between the Schmitz and MacLean models, as indicated by the "proximate equivalence" that we have identified. Third, the analysis underscores the absence of a comparable relationship between the Feinberg and MacLean models, highlighting distinctive differences between the two. Thus, our approach could be a useful tool to compare mathematical models of the same or closely related systems.
{"title":"Embedding-based comparison of reaction networks of Wnt signaling","authors":"Bryan S. Hernandez, Patrick Vincent N. Lubenia, Eduardo R. Mendoza","doi":"arxiv-2404.06515","DOIUrl":"https://doi.org/arxiv-2404.06515","url":null,"abstract":"This work introduces a new method for comparing two reaction networks of the\u0000same or closely related systems through their embedded networks in terms of the\u0000shared set of species. Hence, we call this method the Common Species Embedded\u0000Networks (CSEN) analysis. Using this approach, we conduct a comparison of\u0000existing reaction networks associated with Wnt signaling models (Lee, Schmitz,\u0000MacLean, and Feinberg) that we have identified. The analysis yields three\u0000important results for these Wnt models. First, the CSEN analysis of the Lee\u0000(mono-stationary) and Feinberg (multi-stationary) shows a strong similarity,\u0000justifying the study of the Feinberg model, which was a modified Lee model\u0000constructed to study an important network property called \"concordance\". It\u0000also challenge the absoluteness of discrimination of the models into\u0000mono-stationarity versus multi-stationarity, which is a main result of Maclean\u0000et al. (PNAS USA 2015). Second, the CSEN analysis provides evidence supporting\u0000a strong similarity between the Schmitz and MacLean models, as indicated by the\u0000\"proximate equivalence\" that we have identified. Third, the analysis\u0000underscores the absence of a comparable relationship between the Feinberg and\u0000MacLean models, highlighting distinctive differences between the two. Thus, our\u0000approach could be a useful tool to compare mathematical models of the same or\u0000closely related systems.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140594861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mingyu Jin, Haochen Xue, Zhenting Wang, Boming Kang, Ruosong Ye, Kaixiong Zhou, Mengnan Du, Yongfeng Zhang
The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions, ignoring the broader context of nonphysical connections through intermediate proteins, thus limiting their effectiveness. The emergence of Large Language Models (LLMs) provides a new opportunity for addressing this complex biological challenge. By transforming structured data into natural language prompts, we can map the relationships between proteins into texts. This approach allows LLMs to identify indirect connections between proteins, tracing the path from upstream to downstream. Therefore, we propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time. Specifically, we propose Protein Chain of Thought (ProCoT), which replicates the biological mechanism of signaling pathways as natural language prompts. ProCoT considers a signaling pathway as a protein reasoning process, which starts from upstream proteins and passes through several intermediate proteins to transmit biological signals to downstream proteins. Thus, we can use ProCoT to predict the interaction between upstream proteins and downstream proteins. The training of ProLLM employs the ProCoT format, which enhances the model's understanding of complex biological problems. In addition to ProCoT, this paper also contributes to the exploration of embedding replacement of protein sites in natural language prompts, and instruction fine-tuning in protein knowledge datasets. We demonstrate the efficacy of ProLLM through rigorous validation against benchmark datasets, showing significant improvement over existing methods in terms of prediction accuracy and generalizability. The code is available at: https://github.com/MingyuJ666/ProLLM.
{"title":"ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction","authors":"Mingyu Jin, Haochen Xue, Zhenting Wang, Boming Kang, Ruosong Ye, Kaixiong Zhou, Mengnan Du, Yongfeng Zhang","doi":"arxiv-2405.06649","DOIUrl":"https://doi.org/arxiv-2405.06649","url":null,"abstract":"The prediction of protein-protein interactions (PPIs) is crucial for\u0000understanding biological functions and diseases. Previous machine learning\u0000approaches to PPI prediction mainly focus on direct physical interactions,\u0000ignoring the broader context of nonphysical connections through intermediate\u0000proteins, thus limiting their effectiveness. The emergence of Large Language\u0000Models (LLMs) provides a new opportunity for addressing this complex biological\u0000challenge. By transforming structured data into natural language prompts, we\u0000can map the relationships between proteins into texts. This approach allows\u0000LLMs to identify indirect connections between proteins, tracing the path from\u0000upstream to downstream. Therefore, we propose a novel framework ProLLM that\u0000employs an LLM tailored for PPI for the first time. Specifically, we propose\u0000Protein Chain of Thought (ProCoT), which replicates the biological mechanism of\u0000signaling pathways as natural language prompts. ProCoT considers a signaling\u0000pathway as a protein reasoning process, which starts from upstream proteins and\u0000passes through several intermediate proteins to transmit biological signals to\u0000downstream proteins. Thus, we can use ProCoT to predict the interaction between\u0000upstream proteins and downstream proteins. The training of ProLLM employs the\u0000ProCoT format, which enhances the model's understanding of complex biological\u0000problems. In addition to ProCoT, this paper also contributes to the exploration\u0000of embedding replacement of protein sites in natural language prompts, and\u0000instruction fine-tuning in protein knowledge datasets. We demonstrate the\u0000efficacy of ProLLM through rigorous validation against benchmark datasets,\u0000showing significant improvement over existing methods in terms of prediction\u0000accuracy and generalizability. The code is available at:\u0000https://github.com/MingyuJ666/ProLLM.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140931165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julian B. VoitsHeidelberg University, Ulrich S. SchwarzHeidelberg University
Biological systems are remarkably susceptible to relatively small temperature changes. The most obvious example is fever, when a modest rise in body temperature of only few Kelvin has strong effects on our immune system and how it fights pathogens. Another very important example is climate change, when even smaller temperature changes lead to dramatic shifts in ecosystems. Although it is generally accepted that the main effect of an increase in temperature is the acceleration of biochemical reactions according to the Arrhenius equation, it is not clear how it effects large biochemical networks with complicated architectures. For developmental systems like fly and frog, it has been shown that the system response to temperature deviates in a characteristic manner from the linear Arrhenius plot of single reactions, but a rigorous explanation has not been given yet. Here we use a graph theoretical interpretation of the mean first passage times of a biochemical master equation to give a statistical description. We find that in the limit of large system size and if the network has a bias towards a target state, then the Arrhenius plot is generically quadratic, in excellent agreement with experimental data for developmental times in fly and frog.
{"title":"The generic temperature response of large biochemical networks","authors":"Julian B. VoitsHeidelberg University, Ulrich S. SchwarzHeidelberg University","doi":"arxiv-2403.17202","DOIUrl":"https://doi.org/arxiv-2403.17202","url":null,"abstract":"Biological systems are remarkably susceptible to relatively small temperature\u0000changes. The most obvious example is fever, when a modest rise in body\u0000temperature of only few Kelvin has strong effects on our immune system and how\u0000it fights pathogens. Another very important example is climate change, when\u0000even smaller temperature changes lead to dramatic shifts in ecosystems.\u0000Although it is generally accepted that the main effect of an increase in\u0000temperature is the acceleration of biochemical reactions according to the\u0000Arrhenius equation, it is not clear how it effects large biochemical networks\u0000with complicated architectures. For developmental systems like fly and frog, it\u0000has been shown that the system response to temperature deviates in a\u0000characteristic manner from the linear Arrhenius plot of single reactions, but a\u0000rigorous explanation has not been given yet. Here we use a graph theoretical\u0000interpretation of the mean first passage times of a biochemical master equation\u0000to give a statistical description. We find that in the limit of large system\u0000size and if the network has a bias towards a target state, then the Arrhenius\u0000plot is generically quadratic, in excellent agreement with experimental data\u0000for developmental times in fly and frog.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The parameter region of multistationarity of a reaction network contains all the parameters for which the associated dynamical system exhibits multiple steady states. Describing this region is challenging and remains an active area of research. In this paper, we concentrate on two biologically relevant families of reaction networks that model multisite phosphorylation and dephosphorylation of a substrate at $n$ sites. For small values of $n$, it had previously been shown that the parameter region of multistationarity is connected. Here, we extend these results and provide a proof that applies to all values of $n$. Our techniques are based on the study of the critical polynomial associated with these reaction networks together with polyhedral geometric conditions of the signed support of this polynomial.
{"title":"Connectivity of Parameter Regions of Multistationarity for Multisite Phosphorylation Networks","authors":"Nidhi Kaihnsa, Máté L. Telek","doi":"arxiv-2403.16556","DOIUrl":"https://doi.org/arxiv-2403.16556","url":null,"abstract":"The parameter region of multistationarity of a reaction network contains all\u0000the parameters for which the associated dynamical system exhibits multiple\u0000steady states. Describing this region is challenging and remains an active area\u0000of research. In this paper, we concentrate on two biologically relevant\u0000families of reaction networks that model multisite phosphorylation and\u0000dephosphorylation of a substrate at $n$ sites. For small values of $n$, it had\u0000previously been shown that the parameter region of multistationarity is\u0000connected. Here, we extend these results and provide a proof that applies to\u0000all values of $n$. Our techniques are based on the study of the critical\u0000polynomial associated with these reaction networks together with polyhedral\u0000geometric conditions of the signed support of this polynomial.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140303383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The essence of life is a frequency distribution conversion process of molecules accompanied by changes in information. In wet-dry cycling hot springs, RNA of different sequences continuously undergoes polymerization and decomposition reactions, products with stable configurations will accumulate their frequency among all RNA molecules and finally become an important source of DNA coding and non-coding regions in primitive cells. This periodic extreme environmental change narrows the frequency distribution of macromolecules through the distillation process and new potential catalysts are searched. When enough macromolecules are accumulated and reliable reaction pathways are built, phospholipids randomly wrap the macromolecules in the hot springs which I would call it the pioneer pools, and these protocells become parallel calculators of molecule frequency distribution. Through the selective permeability of the cell membrane to different molecular weights and properties, cells with appropriate distribution have more opportunities to absorb small molecule substances and increase its intracellular frequencies of molecules. This will strongly induce the occurrence of macromolecules that can widely catalyze the synthesis of other macromolecules or themselves, such as ribosomes, etc. Rupture and fusion during cell division make all protocells share the same frequency distribution of molecules during the origin of life, thus ensuring that all present cells have very similar genetic materials and protein translation systems. This also suggests that viruses may have originated and evolved together with cells.
{"title":"Stability distillation hypothesis for the origin of life","authors":"Cheng Bi","doi":"arxiv-2403.17072","DOIUrl":"https://doi.org/arxiv-2403.17072","url":null,"abstract":"The essence of life is a frequency distribution conversion process of\u0000molecules accompanied by changes in information. In wet-dry cycling hot\u0000springs, RNA of different sequences continuously undergoes polymerization and\u0000decomposition reactions, products with stable configurations will accumulate\u0000their frequency among all RNA molecules and finally become an important source\u0000of DNA coding and non-coding regions in primitive cells. This periodic extreme\u0000environmental change narrows the frequency distribution of macromolecules\u0000through the distillation process and new potential catalysts are searched. When\u0000enough macromolecules are accumulated and reliable reaction pathways are built,\u0000phospholipids randomly wrap the macromolecules in the hot springs which I would\u0000call it the pioneer pools, and these protocells become parallel calculators of\u0000molecule frequency distribution. Through the selective permeability of the cell\u0000membrane to different molecular weights and properties, cells with appropriate\u0000distribution have more opportunities to absorb small molecule substances and\u0000increase its intracellular frequencies of molecules. This will strongly induce\u0000the occurrence of macromolecules that can widely catalyze the synthesis of\u0000other macromolecules or themselves, such as ribosomes, etc. Rupture and fusion\u0000during cell division make all protocells share the same frequency distribution\u0000of molecules during the origin of life, thus ensuring that all present cells\u0000have very similar genetic materials and protein translation systems. This also\u0000suggests that viruses may have originated and evolved together with cells.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The first-passage time (FPT) is the time it takes a system variable to cross a given boundary for the first time. In the context of Markov networks, the FPT is the time a random walker takes to reach a particular node (target) by hopping from one node to another. If the walker pauses at each node for a period of time drawn from a continuous distribution, the FPT will be a continuous variable; if the pauses last exactly one unit of time, the FPT will be discrete and equal to the number of hops. We derive an exact analytical expression for the discrete first-passage time (DFPT) in Markov networks. Our approach is as follows: first, we divide each edge (connection between two nodes) of the network into $h$ unidirectional edges connecting a cascade of $h$ fictitious nodes and compute the continuous FPT (CFPT). Second, we set the transition rates along the edges to $h$, and show that as $htoinfty$, the distribution of travel times between any two nodes of the original network approaches a delta function centered at 1, which is equivalent to pauses lasting 1 unit of time. Using this approach, we also compute the joint-probability distributions for the DFPT, the target node, and the node from which the target node was reached. A comparison with simulation confirms the validity of our approach.
{"title":"Exact analytic expressions for discrete first-passage time probability distributions in Markov networks","authors":"Jaroslav Albert","doi":"arxiv-2403.14149","DOIUrl":"https://doi.org/arxiv-2403.14149","url":null,"abstract":"The first-passage time (FPT) is the time it takes a system variable to cross\u0000a given boundary for the first time. In the context of Markov networks, the FPT\u0000is the time a random walker takes to reach a particular node (target) by\u0000hopping from one node to another. If the walker pauses at each node for a\u0000period of time drawn from a continuous distribution, the FPT will be a\u0000continuous variable; if the pauses last exactly one unit of time, the FPT will\u0000be discrete and equal to the number of hops. We derive an exact analytical\u0000expression for the discrete first-passage time (DFPT) in Markov networks. Our\u0000approach is as follows: first, we divide each edge (connection between two\u0000nodes) of the network into $h$ unidirectional edges connecting a cascade of $h$\u0000fictitious nodes and compute the continuous FPT (CFPT). Second, we set the\u0000transition rates along the edges to $h$, and show that as $htoinfty$, the\u0000distribution of travel times between any two nodes of the original network\u0000approaches a delta function centered at 1, which is equivalent to pauses\u0000lasting 1 unit of time. Using this approach, we also compute the\u0000joint-probability distributions for the DFPT, the target node, and the node\u0000from which the target node was reached. A comparison with simulation confirms\u0000the validity of our approach.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Ali Al-Radhawi, Krishna Manoj, Dhruv D. Jatkar, Alon Duvall, Domitilla Del Vecchio, Eduardo D. Sontag
In the context of epigenetic transformations in cancer metastasis, a puzzling effect was recently discovered, in which the elimination (knock-out) of an activating regulatory element leads to increased (rather than decreased) activity of the element being regulated. It has been postulated that this paradoxical behavior can be explained by activating and repressing transcription factors competing for binding to other possible targets. It is very difficult to prove this hypothesis in mammalian cells, due to the large number of potential players and the complexity of endogenous intracellular regulatory networks. Instead, this paper analyzes this issue through an analogous synthetic biology construct which aims to reproduce the paradoxical behavior using standard bacterial gene expression networks. The paper first reviews the motivating cancer biology work, and then describes a proposed synthetic construct. A mathematical model is formulated, and basic properties of uniqueness of steady states and convergence to equilibria are established, as well as an identification of parameter regimes which should lead to observing such paradoxical phenomena (more activator leads to less activity at steady state). A proof is also given to show that this is a steady-state property, and for initial transients the phenomenon will not be observed. This work adds to the general line of work of resource competition in synthetic circuits.
{"title":"Competition for binding targets results in paradoxical effects for simultaneous activator and repressor action -- Extended Version","authors":"M. Ali Al-Radhawi, Krishna Manoj, Dhruv D. Jatkar, Alon Duvall, Domitilla Del Vecchio, Eduardo D. Sontag","doi":"arxiv-2403.14820","DOIUrl":"https://doi.org/arxiv-2403.14820","url":null,"abstract":"In the context of epigenetic transformations in cancer metastasis, a puzzling\u0000effect was recently discovered, in which the elimination (knock-out) of an\u0000activating regulatory element leads to increased (rather than decreased)\u0000activity of the element being regulated. It has been postulated that this\u0000paradoxical behavior can be explained by activating and repressing\u0000transcription factors competing for binding to other possible targets. It is\u0000very difficult to prove this hypothesis in mammalian cells, due to the large\u0000number of potential players and the complexity of endogenous intracellular\u0000regulatory networks. Instead, this paper analyzes this issue through an\u0000analogous synthetic biology construct which aims to reproduce the paradoxical\u0000behavior using standard bacterial gene expression networks. The paper first\u0000reviews the motivating cancer biology work, and then describes a proposed\u0000synthetic construct. A mathematical model is formulated, and basic properties\u0000of uniqueness of steady states and convergence to equilibria are established,\u0000as well as an identification of parameter regimes which should lead to\u0000observing such paradoxical phenomena (more activator leads to less activity at\u0000steady state). A proof is also given to show that this is a steady-state\u0000property, and for initial transients the phenomenon will not be observed. This\u0000work adds to the general line of work of resource competition in synthetic\u0000circuits.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Steady state non-monotonic ("biphasic") dose responses are often observed in experimental biology, which raises the control-theoretic question of identifying which possible mechanisms might underlie such behaviors. It is well known that the presence of an incoherent feedforward loop (IFFL) in a network may give rise to a non-monotonic response. It has been conjectured that this condition is also necessary, i.e. that a non-monotonic response implies the existence of an IFFL. In this paper, we show that this conjecture is false, and in the process prove a weaker version: that either an IFFL must exist or both a positive loop and a negative feedback loop must exist. Towards this aim, we give necessary and sufficient conditions for when minors of a symbolic matrix have mixed signs. Finally, we study in full generality when a model of immune T-cell activation could exhibit a steady state non-monotonic dose response.
{"title":"A necessary condition for non-monotonic dose response, with an application to a kinetic proofreading model -- Extended version","authors":"Polly Y. Yu, Eduardo D. Sontag","doi":"arxiv-2403.13862","DOIUrl":"https://doi.org/arxiv-2403.13862","url":null,"abstract":"Steady state non-monotonic (\"biphasic\") dose responses are often observed in\u0000experimental biology, which raises the control-theoretic question of\u0000identifying which possible mechanisms might underlie such behaviors. It is well\u0000known that the presence of an incoherent feedforward loop (IFFL) in a network\u0000may give rise to a non-monotonic response. It has been conjectured that this\u0000condition is also necessary, i.e. that a non-monotonic response implies the\u0000existence of an IFFL. In this paper, we show that this conjecture is false, and\u0000in the process prove a weaker version: that either an IFFL must exist or both a\u0000positive loop and a negative feedback loop must exist. Towards this aim, we\u0000give necessary and sufficient conditions for when minors of a symbolic matrix\u0000have mixed signs. Finally, we study in full generality when a model of immune\u0000T-cell activation could exhibit a steady state non-monotonic dose response.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoxia Liu, Robert R Butler III, Prashnna K Gyawali, Frank M Longo, Zihuai He
Alzheimer's disease (AD) is a pervasive neurodegenerative disorder that leads to memory and behavior impairment severe enough to interfere with daily life activities. Understanding this disease pathogenesis can drive the development of new targets and strategies to prevent and treat AD. Recent advances in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of massive amounts of transcriptomic data at the single-cell level provided remarkable insights into understanding the molecular pathogenesis of Alzheimer's disease. In this study, we introduce ScAtt, an innovative Attention-based architecture, devised specifically for the concurrent identification of cell-type specific AD-related genes and their associated gene regulatory network. ScAtt incorporates a flexible model capable of capturing nonlinear effects, leading to the detection of AD-associated genes that might be overlooked by traditional differentially expressed gene (DEG) analyses. Moreover, ScAtt effectively infers a gene regulatory network depicting the combined influences of genes on the targeted disease, as opposed to examining correlations among genes in conventional gene co-expression networks. In an application to 95,186 single-nucleus transcriptomes from 17 hippocampus samples, ScAtt shows substantially better performance in modeling quantitative changes in expression levels between AD and healthy controls. Consequently, ScAtt performs better than existing methods in the identification of AD-related genes, with more unique discoveries and less overlap between cell types. Functional enrichments of the corresponding gene modules detected from gene regulatory network show significant enrichment of biologically meaningful AD-related pathways across different cell types.
阿尔茨海默病（AD）是一种普遍的神经退行性疾病，会导致严重的记忆和行为障碍，以至于影响日常生活。了解这种疾病的发病机制可以推动开发预防和治疗阿尔茨海默病的新靶点和策略。近年来，高通量单细胞 RNA 测序技术（scRNA-seq）的进步使得在单细胞水平上生成大量转录组数据成为可能，这为了解阿尔茨海默病的分子发病机制提供了重要启示。在这项研究中，我们介绍了基于注意力的创新架构 ScAtt，它是专为同时鉴定细胞类型特异性 AD 相关基因及其相关基因调控网络而设计的。ScAtt 采用了一个灵活的模型，能够捕捉非线性效应，从而检测出传统的差异表达基因（DEG）分析可能会忽略的 AD 相关基因。此外，ScAtt 还能有效地推断基因调控网络，描述基因对目标疾病的综合影响，而不是研究传统基因共表达网络中基因之间的相关性。在对来自17个海马体样本的95,186个单核转录组的应用中，ScAtt在模拟AD和健康对照组之间表达水平的定量变化方面表现出了更好的性能。从基因调控网络中检测到的相应基因模块的功能富集显示，不同细胞类型中具有生物学意义的AD相关通路显著富集。
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