R-3 Repository :: Browsing by Author "Teimouri, Hamid"

Browsing by Author "Teimouri, Hamid"

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All-time dynamics of continuous-time random walks on complex networks
(American Institute of Physics, 2013) Teimouri, Hamid; Kolomeisky, Anatoly B.
The concept of continuous-time random walks (CTRW) is a generalization of ordinary random walk models, and it is a powerful tool for investigating a broad spectrum of phenomena in natural, engineering, social, and economic sciences. Recently, several theoretical approaches have been developed that allowed to analyze explicitly dynamics of CTRW at all times, which is critically important for understanding mechanisms of underlying phenomena. However, theoretical analysis has been done mostly for systems with a simple geometry. Here we extend the original method based on generalized master equations to analyze all-time dynamics of CTRWmodels on complex networks. Specific calculations are performed for models on lattices with branches and for models on coupled parallelchain lattices. Exact expressions for velocities and dispersions are obtained. Generalized fluctuations theorems for CTRW models on complex networks are discussed.
Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods
(American Chemical Society, 2023) Teimouri, Hamid; Medvedeva, Angela; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
There are several classes of short peptide molecules, known as antimicrobial peptides (AMPs), which are produced during the immune responses of living organisms against various infections. In recent years, substantial progress has been achieved in applying machine-learning methods to predict the activities of AMPs against bacteria. In most investigated cases, however, the outcome is not bacterium-specific since the specific features of bacteria, such as chemical composition and structure of membranes, are not considered. To overcome this problem, we developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physicochemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient antibacterial drug therapies.
Can we understand the mechanisms of tumor formation by analyzing dynamics of cancer initiation?
(IOP Publishing, 2022) Teimouri, Hamid; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
Cancer is a collection of related genetic diseases exhibiting uncontrolled cell growth that interferes with normal functioning of human organisms. It results from accumulation of unfavorable mutations in tissues. While the biochemical picture of how cancer appears is known, the molecular mechanisms of tumor formation remain not fully understood despite tremendous efforts of researchers in multiple fields. New approaches for investigating cancer are constantly sought. In this paper, we discuss a powerful method of clarifying better a more microscopic picture of cancer by analyzing the dynamics of tumor formation. Using physics- and chemistry-inspired discrete-state stochastic description of cancer initiation, it is shown how the mechanisms of tumor formation can be uncovered. This approach is suggested as a powerful new physical-chemical tool for a better understanding of complex processes associated with cancer.
Cooperativity in Bacterial Membrane Association Controls the Synergistic Activities of Antimicrobial Peptides
(American Chemical Society, 2022) Nguyen, Thao N.; Teimouri, Hamid; Medvedeva, Angela; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
Antimicrobial peptides (AMPs), or defence peptides, are compounds naturally produced during immune responses of living organisms against bacterial infections that are currently actively considered as promising alternatives to antibiotics. Recent experimental studies uncovered that in many situations, combinations of different AMPs are much more successful in eliminating the bacterial pathogens than single peptide species. However, the microscopic origin of such synergistic activities remains not fully understood. We present and investigate a possible mechanism of synergy between AMPs. It is based on the idea that due to inter-molecular interactions, the presence of an AMP of one type stimulates the association of an AMP of another type, and this accelerates the overall association to the membrane, eventually killing the bacteria. This approach allows us to fully quantify the synergistic activities of AMPs, and it is successfully applied for several experimental systems. It is found that strong cooperativity can be achieved for relatively weak inter-molecular interactions, suggesting that the application of combinations of AMPs can be further rationally optimized to make it a powerful antibacterial treatment.
Development of morphogen gradient: The role of dimension and discreteness
(AIP Publishing, 2014) Teimouri, Hamid; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
The fundamental processes of biological development are governed by multiple signaling molecules that create non-uniform concentration profiles known as morphogen gradients. It is widely believed that the establishment of morphogen gradients is a result of complex processes that involve diffusion and degradation of locally produced signaling molecules. We developed a multi-dimensional discrete-state stochastic approach for investigating the corresponding reaction-diffusion models. It provided a full analytical description for stationary profiles and for important dynamic properties such as local accumulation times, variances, and mean first-passage times. The role of discreteness in developing of morphogen gradients is analyzed by comparing with available continuum descriptions. It is found that the continuum models prediction about multiple time scales near the source region in two-dimensional and three-dimensional systems is not supported in our analysis. Using ideas that view the degradation process as an effective potential, the effect of dimensionality on establishment of morphogen gradients is also discussed. In addition, we investigated how these reaction-diffusion processes are modified with changing the size of the source region.
Development of Morphogen Gradients with Spatially Varying Degradation Rates
(American Chemical Society, 2016) Teimouri, Hamid; Bozorgui, Behnaz; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
Successful biological development via spatial and temporal regulations of cell differentiation relies on the action of multiple signaling molecules that are known as morphogens. It is now well established that biological signaling molecules create nonuniform concentration profiles, called morphogen gradients, that activate different genes, leading to patterning in the developing organisms. The current view of the formation of morphogen gradients is that it is a result of complex reaction–diffusion processes that include production, diffusion, and degradation of signaling molecules. Recent studies also suggest that the degradation of morphogens is a critically important step in the whole process. We develop a theoretical model that allows us to investigate the role of a spatially varying degradation in the formation of morphogen gradients. Our analysis shows that the spatial inhomogeneities in degradation might strongly influence the dynamics of formation of signaling profiles. Physical–chemical mechanisms of the underlying processes are discussed.
Elucidating the correlations between cancer initiation times and lifetime cancer risks
(Springer Nature, 2019) Teimouri, Hamid; Kochugaeva, Maria P.; Kolomeisky, Anatoly B.
Cancer is a genetic disease that results from accumulation of unfavorable mutations. As soon as genetic and epigenetic modifications associated with these mutations become strong enough, the uncontrolled tumor cell growth is initiated, eventually spreading through healthy tissues. Clarifying the dynamics of cancer initiation is thus critically important for understanding the molecular mechanisms of tumorigenesis. Here we present a new theoretical method to evaluate the dynamic processes associated with the cancer initiation. It is based on a discrete-state stochastic description of the formation of tumors as a fixation of cancerous mutations in tissues. Using a first-passage analysis the probabilities for the cancer to appear and the times before it happens, which are viewed as fixation probabilities and fixation times, respectively, are explicitly calculated. It is predicted that the slowest cancer initiation dynamics is observed for neutral mutations, while it is fast for both advantageous and, surprisingly, disadvantageous mutations. The method is applied for estimating the cancer initiation times from experimentally available lifetime cancer risks for different types of cancer. It is found that the higher probability of the cancer to occur does not necessary lead to the faster times of starting the cancer. Our theoretical analysis helps to clarify microscopic aspects of cancer initiation processes.
Mechanisms of the formation of biological signaling profiles
(IOP Publishing, 2016) Teimouri, Hamid; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
The formation and growth of multi-cellular organisms and tissues from several genetically identical embryo cells is one of the most fundamental natural phenomena. These processes are stimulated and governed by multiple biological signaling molecules, which are also called morphogens. Embryo cells are able to read and pass genetic information by measuring the non-uniform concentration profiles of signaling molecules. It is widely believed that the establishment of concentration profiles of morphogens, commonly referred as morphogen gradients, is a result of complex biophysical and biochemical processes that might involve diffusion and degradation of locally produced signaling molecules. In this review, we discuss various theoretical aspects of the mechanisms for morphogen gradient formation, including stationary and transient dynamics, the effect of source delocalization, diffusion, different degradation mechanisms, and the role of spatial dimensions. Theoretical predictions are compared with experimental observations. In addition, we analyze the potential alternative mechanisms of the delivery of biological signals in embryo cells and tissues. Current challenges in understanding the mechanisms of morphogen gradients and future directions are also discussed.
New Model for Understanding Mechanisms of Biological Signaling: Direct Transport via Cytonemes
(America Chemical Society, 2016) Teimouri, Hamid; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
Biological signaling is a crucial natural process that governs the formation of all multicellular organisms. It relies on efficient and fast transfer of information between different cells and tissues. It has been presumed for a long time that these long-distance communications in most systems can take place only indirectly via the diffusion of signaling molecules, also known as morphogens, through the extracellular fluid; however, recent experiments indicate that there is also an alternative direct delivery mechanism. It utilizes dynamic tubular cellular extensions, called cytonemes, that directly connect cells, supporting the flux of morphogens to specific locations. We present a first quantitative analysis of the cytoneme-mediated mechanism of biological signaling. Dynamics of the formation of signaling molecule profiles, which are also known as morphogen gradients, is discussed. It is found that the direct-delivery mechanism is more robust with respect to fluctuations in comparison with the passive diffusion mechanism. In addition, we show that the direct transport of morphogens through cytonemes simultaneously delivers the information to all cells, which is also different from the diffusional indirect delivery; however, it requires energy dissipation and it might be less efficient at large distances due to intermolecular interactions of signaling molecules.
Optimal pathways control fixation of multiple mutations during cancer initiation
(Elsevier, 2022) Teimouri, Hamid; Spaulding, Cade; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
Cancer starts after initially healthy tissue cells accumulate several specific mutations or other genetic alterations. The dynamics of tumor formation is a very complex phenomenon due to multiple involved biochemical and biophysical processes. It leads to a very large number of possible pathways on the road to final fixation of all mutations that marks the beginning of the cancer, complicating the understanding of microscopic mechanisms of tumor formation. We present a new theoretical framework of analyzing the cancer initiation dynamics by exploring the properties of effective free-energy landscape of the process. It is argued that although there are many possible pathways for the fixation of all mutations in the system, there are only a few dominating pathways on the road to tumor formation. The theoretical approach is explicitly tested in the system with only two mutations using analytical calculations and Monte Carlo computer simulations. Excellent agreement with theoretical predictions is found for a large range of parameters, supporting our hypothesis and allowing us to better understand the mechanisms of cancer initiation. Our theoretical approach clarifies some important aspects of microscopic processes that lead to tumor formation.
Physical-chemical mechanisms of pattern formation during gastrulation
(AIP Publishing, 2018) Bozorgui, Behnaz; Kolomeisky, Anatoly B.; Teimouri, Hamid; Center for Theoretical Biological Physics
Gastrulation is a fundamental phase during the biological development of most animals when a single layer of identical embryo cells is transformed into a three-layer structure, from which the organs start to develop. Despite a remarkable progress in quantifying the gastrulation processes, molecular mechanisms of these processes remain not well understood. Here we theoretically investigate early spatial patterning in a geometrically confined colony of embryonic stem cells. Using a reaction-diffusion model, a role of Bone-Morphogenetic Protein 4 (BMP4) signaling pathway in gastrulation is specifically analyzed. Our results show that for slow diffusion rates of BMP4 molecules, a new length scale appears, which is independent of the size of the system. This length scale separates the central region of the colony with uniform low concentrations of BMP molecules from the region near the colony edge where the concentration of signaling molecules is elevated. The roles of different components of the signaling pathway are also explained. Theoretical results are consistent with recent in vitro experiments, providing microscopic explanations for some features of early embryonic spatial patterning. Physical-chemical mechanisms of these processes are discussed.
Power of stochastic kinetic models: From biological signaling and antibiotic activities to T cell activation and cancer initiation dynamics
(Wiley, 2022) Teimouri, Hamid; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
All chemical processes exhibit two main universal features. They are stochastic because chemical reactions might happen only after random successful collisions of reacting species, and they are dynamic because the amount of reactants and products changes with time. Since biological processes rely heavily on specific chemical reactions, the stochasticity and dynamics are also crucial features for all living systems. To understand the molecular mechanisms of chemical and biological processes, it is important to develop and apply theoretical methods that fully incorporate the randomness and dynamic nature of these systems. In recent years, there have been significant advances in formulating and exploring such theoretical methods. As an illustration of such developments, in this review the recent applications of stochastic kinetic models for various biological processes are discussed. Specifically, we focus on applying these theoretical approaches to investigate the biological signaling, clearance of bacteria under antibiotics, T cells activation in the immune system, and cancer initiation dynamics. The main advantage of the presented stochastic kinetic models is that they generally can be solved analytically, allowing to clarify the underlying microscopic picture, as well as to explain the existing experimental observations and to make new testable predictions. This theoretical approach becomes a powerful tool in uncovering the molecular mechanisms of complex natural phenomena.
Stochastic Mechanisms of Cell-Size Regulation in Bacteria
(American Chemical Society, 2020) Teimouri, Hamid; Mukherjee, Rupsha; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
How bacteria are able to maintain their sizes remains an open question. It is believed that cells have narrow distributions of sizes as a consequence of a homeostasis that allows bacteria to function at the optimal conditions. Several phenomenological approaches to explain these observations have been presented, but the microscopic origins of the cell-size regulation are still not understood. Here, we propose a new stochastic approach to investigate the molecular mechanisms of maintaining the cell sizes in bacteria. It is argued that the cell-size regulation is a result of coupling of two stochastic processes, cell growth and division, which eliminates the need for introducing the thresholds. Dynamic properties of the system are explicitly evaluated, and it is shown that the model is consistent with the experimentally supported adder principle of the cell-size regulation. In addition, theoretical predictions agree with experimental observations on E. coli bacteria. Theoretical analysis clarifies some important features of bacterial cell growth.
Stochastic Modeling of Dynamical Processes in Biological Signaling and Cellular Transport
(2016-03-18) Teimouri, Hamid; Kolomeisky, Anatoly B.; Landes, Christy; Yakobson, Boris I.
Successful cellular function and organ development rely on the effective transport of proteins and other biomolecules to specific positions. There are two basic mechanisms for biological transport: passive diffusion and motor-driven active transport. This thesis presents theoretical investigations of several biophysical problems in the context of active and passive transport. In the matter of passive diffusion, we investigate fundamental processes of biological development that are governed by multiple signaling molecules that create non-uniform concentration profiles known as morphogen gradients. It is widely believed that the establishment of morphogen gradients is a result of complex processes that involve diffusion and degradation of locally produced signaling molecules. We have developed discrete-state stochastic and continuum mean field approaches to investigate the corresponding reaction-diffusion models. In the case of active transport, we investigate the fundamental role of local interactions between molecular motors by analyzing a new class of totally asymmetric exclusion processes where interactions are accounted for in a thermodynamically consistent fashion. This allows us to explicitly connect microscopic features of motor proteins with their collective dynamic properties. Our theoretical analysis that combines various mean-field calculations and computer simulations suggests that the dynamic properties of molecular motors strongly depend on the interactions, and that the correlations are stronger for interacting motor proteins. Furthermore, we investigate all times dynamics of continuous-time random walks (CTRWs). The concept of continuous-time random walks (CTRW) is a generalization of ordinary random walk models, and it is a powerful tool for investigating a broad spectrum of phenomena in natural, engineering, social, and economic sciences. Recently, several theoretical approaches have been developed that allowed to analyze explicitly dynamics of CTRW at all times, which is critically important for understanding mechanisms of underlying phenomena. However, theoretical analysis has been done mostly for systems with a simple geometry. Here, we extend the original method based on generalized master equations to analyze all-time dynamics of CTRW models on complex networks.
Temporal order of mutations influences cancer initiation dynamics
(IOP Publishing, 2021) Teimouri, Hamid; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
Cancer is a set of genetic diseases that are driven by mutations. It was recently discovered that the temporal order of genetic mutations affects the cancer evolution and even the nature of the decease itself. The mechanistic origin of these observations, however, remain not well understood. Here we present a theoretical model for cancer initiation dynamics that allows us to quantify the impact of the temporal order of mutations. In our approach, the cancer initiation process is viewed as a set of stochastic transitions between discrete states defined by the different numbers of mutated cells. Using a first-passage analysis, probabilities and times before the cancer initiation are explicitly evaluated for two alternative sequences of two mutations. It is found that the probability of cancer initiation is determined only by the first mutation, while the dynamics depends on both mutations. In addition, it is shown that the acquisition of a mutation with higher fitness before mutation with lower fitness increases the probability of the tumor formation but delays the cancer initiation. Theoretical results are explained using effective free-energy landscapes.
The role of spatial structures of tissues in cancer initiation dynamics
(IOP Publishing, 2022) Spaulding, Cade; Teimouri, Hamid; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
It is widely believed that biological tissues evolved to lower the risks of cancer development. One of the specific ways to minimize the chances of tumor formation comes from proper spatial organization of tissues. However, the microscopic mechanisms of underlying processes remain not fully understood. We present a theoretical investigation on the role of spatial structures in cancer initiation dynamics. In our approach, the dynamics of single mutation fixations are analyzed using analytical calculations and computer simulations by mapping them to Moran processes on graphs with different connectivity that mimic various spatial structures. It is found that while the fixation probability is not affected by modifying the spatial structures of the tissues, the fixation times can change dramatically. The slowest dynamics is observed in 'quasi-one-dimensional' structures, while the fastest dynamics is observed in 'quasi-three-dimensional' structures. Theoretical calculations also suggest that there is a critical value of the degree of graph connectivity, which mimics the spatial dimension of the tissue structure, above which the spatial structure of the tissue has no effect on the mutation fixation dynamics. An effective discrete-state stochastic model of cancer initiation is utilized to explain our theoretical results and predictions. Our theoretical analysis clarifies some important aspects on the role of the tissue spatial structures in the cancer initiation processes.
Theoretical analysis of degradation mechanisms in the formation of morphogen gradients
(AIP Publishing LLC., 2015) Bozorgui, Behnaz; Teimouri, Hamid; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
Fundamental biological processes of development of tissues and organs in multicellular organisms are governed by various signaling molecules, which are called morphogens. It is known that spatial and temporal variations in the concentration profiles of signaling molecules, which are frequently referred as morphogen gradients, lead to a cell differentiation via activating specific genes in a concentration-dependent manner. It is widely accepted that the establishment of the morphogen gradients involves multiple biochemical reactions and diffusion processes. One of the critical elements in the formation of morphogen gradients is a degradation of signaling molecules. We develop a new theoretical approach that provides a comprehensive description of the degradation mechanisms. It is based on the idea that the degradation works as an effective potential that drives the signaling molecules away from the source region. Utilizing the method of first-passage processes, the dynamics of the formation of morphogen gradients for various degradation mechanisms is explicitly evaluated. It is found that linear degradation processes lead to a dynamic behavior specified by times to form the morphogen gradients that depend linearly on the distance from the source. This is because the effective potential due to the degradation is quite strong. At the same time, nonlinear degradation mechanisms yield a quadratic scaling in the morphogen gradients formation times since the effective potentials are much weaker. Physical-chemical explanations of these phenomena are presented.
Theoretical analysis of dynamic processes for interacting molecular motors
(IOP Publishing, 2015) Teimouri, Hamid; Kolomeisky, Anatoly B.; Mehrabiani, Kareem; Center for Theoretical Biological Physics
Biological transport is supported by the collective dynamics of enzymatic molecules that are called motor proteins or molecular motors. Experiments suggest that motor proteins interact locally via short-range potentials. We investigate the fundamental role of these interactions by carrying out an analysis of a new class of totally asymmetric exclusion processes, in which interactions are accounted for in a thermodynamically consistent fashion. This allows us to explicitly connect microscopic features of motor proteins with their collective dynamic properties. A theoretical analysis that combines various mean-field calculations and computer simulations suggests that the dynamic properties of molecular motors strongly depend on the interactions, and that the correlations are stronger for interacting motor proteins. Surprisingly, it is found that there is an optimal strength of interactions (weak repulsion) that leads to a maximal particle flux. It is also argued that molecular motor transport is more sensitive to attractive interactions. Applications of these results for kinesin motor proteins are discussed.
Theoretical investigation of stochastic clearance of bacteria: first-passage analysis
(The Royal Society, 2019) Teimouri, Hamid; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
Understanding mechanisms of bacterial eradication is critically important for overcoming failures of antibiotic treatments. Current studies suggest that the clearance of large bacterial populations proceeds deterministically, while for smaller populations, the stochastic effects become more relevant. Here, we develop a theoretical approach to investigate the bacterial population dynamics under the effect of antibiotic drugs using a method of first-passage processes. It allows us to explicitly evaluate the most important characteristics of bacterial clearance dynamics such as extinction probabilities and extinction times. The new meaning of minimal inhibitory concentrations for stochastic clearance of bacterial populations is also discussed. In addition, we investigate the effect of fluctuations in population growth rates on the dynamics of bacterial eradication. It is found that extinction probabilities and extinction times generally do not correlate with each other when random fluctuations in the growth rates are taking place. Unexpectedly, for a significant range of parameters, the extinction times increase due to these fluctuations, indicating a slowing in the bacterial clearance dynamics. It is argued that this might be one of the initial steps in the pathway for the development of antibiotic resistance. Furthermore, it is suggested that extinction times is a convenient measure of bacterial tolerance.
Theoretical understanding of evolutionary dynamics on inhomogeneous networks
(IOP Publishing, 2023) Teimouri, Hamid; Khavas, Dorsa Sattari; Spaulding, Cade; Li, Christopher; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
Evolution is the main feature of all biological systems that allows populations to change their characteristics over successive generations. A powerful approach to understand evolutionary dynamics is to investigate fixation probabilities and fixation times of novel mutations on networks that mimic biological populations. It is now well established that the structure of such networks can have dramatic effects on evolutionary dynamics. In particular, there are population structures that might amplify the fixation probabilities while simultaneously delaying the fixation events. However, the microscopic origins of such complex evolutionary dynamics remain not well understood. We present here a theoretical investigation of the microscopic mechanisms of mutation fixation processes on inhomogeneous networks. It views evolutionary dynamics as a set of stochastic transitions between discrete states specified by different numbers of mutated cells. By specifically considering star networks, we obtain a comprehensive description of evolutionary dynamics. Our approach allows us to employ physics-inspired free-energy landscape arguments to explain the observed trends in fixation times and fixation probabilities, providing a better microscopic understanding of evolutionary dynamics in complex systems.

Browsing by Author "Teimouri, Hamid"

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