Biosystems

The paper describes a mathematical model of the molecular switch of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the current literature, we constructed a Petri net model in terms of detailed molecular reactions for the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 connecting edges. Applying Petri net analysis techniques, we found 279 pathways describing complete signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic, leading to multiple possible outcomes. Based on the Petri net, we investigated the detailed in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-{kappa}B, which controls the caspase activity in complex II and apoptosis induction.

The present study was attempted to measure whether the dynamics of elementary coordination is influenced by an overarching temporal structure that is embedded in circadian rhythms (part 1) as well as the systemic proof associated with the intelligent capabilities (part 2). For part 1, evidence of entrainment or any influence of the embedding rhythm were examined on the stability or attractor location. The estimations from the dynamics of the relative phase between the two oscillations show that while (i) circadian effects under the artificially perturbed manipulation were not straightforward along the day-night temperature cycle, (ii) the circadian effect divided by the ordinary circadian seems to be constant along the day-night cycle. Corresponding to this evidence related to performance consequences depending on the organism and environmental interaction, the part 2 determined the impact of circadian (mis)alignment on biological functions and raised the possibility that the disruption of circadian systems may contribute to physical complications. The observations entail rules that self-attunement of current performance may develop not at a single component but across many nested, inter-connected scales. These inter-dependencies from different object phase may allow a potential context-dependent explanation for goal-oriented movements and the emergent assumption of a principle of organisms embedded into their environmental contexts.

A qualitative mathematical model of the notion of immunocompetence is developed, based on the formalism of Memory Evolutive Systems (MES), from which, immunocompetence is defined as an emergent structure of a higher order arising from the signal networks that are established between effector cells and molecules of the immune response in the presence of a given antigen. In addition, a possible mechanism of functorial nature is proposed, which may explain how immunocompetence is achieved in an organism endowed with innate and adaptive components of its immune system. Finally, a practical method to measure the immunocompetence status is established, using elements of the theory of small random graphs and taking into account the characteristics of the immune networks, established through transcriptional studies, of patients with severe COVID-19 and healthy patients, assuming that both types of patients were vaccinated with an effective biological against SARS-CoV-2.

The budding yeast S. cerevisiae, a foundational model for cell cycle studies, exhibits a complex phase organisation (G1, S, G2/M) governed by checkpoints ensuring faithful cellular inheritance. However, the existence of a distinct G2 phase in yeast remains debated, with some advocating for a prometaphase instead. To address this issue, we developed a discrete-event, qualitative, and possibilistic model, the first one to our knowledge, to integrate organelle-level components (replication forks, sister chromatids, mitotic spindle, bud) while remaining parsimonious. Unlike molecular-centred or overly complex whole-cell models, this approach bridges broad systemic and finer mechanistic scales. Our results demonstrate that the model faithfully recapitulates cell cycle progression and supports the dispensable G2 phase. This possibilistic model inspired from recent applications in ecology advocates in favor of the necessity of prometaphase. This study thus provides a unifying and flexible framework to resolve long-standing ambiguities in yeast cell dynamics, while avoiding the pitfalls of excessive complexity or reductionism.

Resistance to several antibiotics against Mycobacterium tuberculosis is a serious problem to be solved worldwide. In the present work, we made the statistical analysis of the gene regulatory network of Mycobacterium tuberculosis and of the Mycobacterium tuberculosis-macrophage interactome to find the probable cause of this resistance. The results from this analysis show that both the gene regulatory network and the interactoma have a hierarchical free scale modular structure that assures a high degree of resilience of these networks against external perturbations. In particular, the interactome is a complex hybrid network that results from the formation of novel links between the Mycobacterium tuberculosis and macrophage proteins and from the modification of the previously existing links between the native macrophage proteins, which give rise to novel negative and positive feedback loops that modify the dynamical behavior of the interactome and protect the mycobacterium against the attack with antibiotics by taking control of the macrophage immune response and apoptosis. The statistical analysis of the interactome shows that the highly connected mycobacterium proteins inhA, ahpC, kasA, katG and rpsL exert this control by creating new links with the host proteins FAS and NF-{kappa}B. These new hybrid circuits embedded in the hierarchical scale-free modular molecular structure of the interactome produce its high resistance to external perturbations like antibiotics. As consequence, the present work proposes the hypothesis that Mycobacterium tuberculosis antibiotic resistance in vivo during chronic tuberculosis is only a particular case of a more complex problem that is the interactome resilience against antibiotics. Thus, new strategies of drug design are necessary to shatter the complex structure of the Mycobacterium tuberculosis-macrophage interactome.

Time-lapse microscopy is a principal tool to unravel the mystery of how cells form and maintain organisms. The complexity of the domain of cellular dynamics demands a conceptual architecture as a solid theoretical foundation that supports the integration of knowledge obtained across experiments and theories. In this work, we outline the ontological foundation of cellular genealogies, a key concept for describing and representing of cellular development. We build the conceptual framework following the onto-axiomatic method: We first analyse the domain within the context of a top-level ontology (GFO). The resulting domain-specification provides the basis for a conceptualisation where we introduce concepts and relations. From these conceptualisations, we then construct model-structures adhering to the principles of model-theory. We finally elaborate axioms based on these model-structures. The developed framework provides the fundamental concepts underlying a Cell Tracking Ontology (CTO) that supports extraction and integration of biological knowledge from systems-level experiments across different types of observations at the single-cell level.

How did the complex structure of telencephalon evolve? Existing explanations are based on phenomena and lack the first principle. The Darwinian dynamics and the endogenous network theory established a few years ago provide a mathematical and theoretical framework of a dynamical structure, and a general constitutive structure for theory-experiment coupling, respectively, for answering this question from the first principle perspective. By revisiting a gene network that explains the anterior-posterior patterning of the vertebrate telencephalon, we found that with the increase of the cooperative effect in this network, the fixed points gradually evolve, accompanied by the occurrence of two bifurcations. The dynamic behavior of this network consists with the knowledge obtained from experiments on telencephalon evolution. Furtherly, our work drew an answer quantitatively of how the telencephalon anterior-posterior patterning evolved from the pre-vertebrate chordate to the vertebrate and gave a series of verifiable predictions in a first principle manner. Figure Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

AbstractionStarting from the unified topological geometric analysis framework of four ecosystem organization rules, including the Klebers 3/4 law, population -3/2 self-thinning law, ecological niche theory, and community neutrality principle, Ecosystem Metabolic Self-organization (EMS) is defined as a process in which individual or characteristic individual in group spontaneously construct their biological space in a three-dimensional resource space, within the redefined framework of ecosystem metabolic network. Under the conditions of metabolic steady (or metabolic conservation), resource diffusion dimension (D) and metabolic network dimension (D+1), which together define the biological space by at most three space parameters (x, y, z) and one direct parameter (d), unified the topological geometry principle and metabolic scaling function of the four rules, B{propto}MD/D+1. The results indicated that EMS follows a Fibonacci sequence (f(n), n) evolution law at kingdom level, from neutral system (prokaryote 1, 1), niche system (unicellular eukaryote 1, 2), modular similarity system (plant 2, 3), to internal metabolic system (animal 3, 4), as indicated by (D, D+1). Further derivation revealed the positions of superbody system (fungi 5, 5) and primitive soup system (0, 0) in this evolutionary sequence. Thus, a conceptual model of macroevolution with the connotation of metabolism co-evolution between life and ecosystem was proposed. This article took the four principles of ecosystem organization as theory prototypes and the analysis paradigm of biological space developed by metabolic theory of ecology (MTE) as referential methodology. It preliminarily constructed the logical, conceptual, and methodological system of EMS hypothesis, and provided a deducible Fibonacci roadmap for macroevolution. In any case, macroevolution should be simple and beautiful, just as the model of nature. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/678403v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@564071org.highwire.dtl.DTLVardef@a0c62borg.highwire.dtl.DTLVardef@1326eecorg.highwire.dtl.DTLVardef@104997_HPS_FORMAT_FIGEXP M_FIG C_FIG

The purpose of our studies is to elucidate the nature of massive control of whole genome expression with a particular emphasis on cell-fate change. Whole genome expression is coordinated by the emergence of a critical point (CP: a peculiar set of bi-phasic genes) through the genome-engine. In response to stimuli, the genome expression self-organizes three critical states, each exhibiting distinct collective behaviors with its center of mass acting as a local attractor, coexisting with whole genome attractor (GA). Genome-engine mechanism accounts for local attractors interaction in phase space. The CP acts as the organizing center of cell-fate change, and its activation makes local perturbation spread over the genome affecting GA. The activation of CP is in turn elicited by hot-spots, genes with elevated temporal variance, normally in charge to keep genome expression at pace with microenvironment fluctuations. When hot-spots oscillation exceeds a given threshold, the CP synchronizes with the GA driving genome expression state transition. The expression synchronization wave invading the entire genome depends on the power law fusion-bursting dynamics of silencing pericentromere-associated heterochromatin domains and the consequent folding-unfolding status of transcribing euchromatin domains. The proposed mechanism is a unified step toward a time-evolutional transition theory of biological regulation.

In this work, we establish and evolve an artificial metabolic system in silicon to shed light on how the metabolic mechanism emerged. This system is composed of two subsystems: the artificial genome subsystem (AGS) and the artificial metabolite subsystem (AMS). The whole system is designed to be capable of being autonomous: the dynamics of AGS is capable of situating itself to the dynamics of AMS to provide it with enzymes in the right time and quantity; the dynamics of AMS is capable of implementing the metabolic function and harvest energy so as to pay back the energy consumption of AGS. This kind of autonomous state requires an intricate structure of the AGS. So it is almost impossible to be predetermined manually. With the help of an evolutionary computational method that has a hierarchical mutational structure, the artificial metabolic system with this kind of autonomous state eventually emerged in silicon. We find that ATP and ADP molecules have an important role in making the state of the system autonomous. We also find that the emerged structure of AGS ensemble existing biological structures in the natural cells.

This study explores the intricate world of protein-protein interaction (PPI) networks and the critical role of hub proteins within them. PPIs, mediated by various biomolecular forces, orchestrate diverse cellular processes through transient or stable interactions. The study delves into the unique ability of hubs to connect with numerous partners within the network. This research investigates the network properties and focuses on hub-centric questions. This study explores information-carrying capacity, power laws in protein circuits, connectivity profiles of hub proteins, and network expansion or shrinkage during evolution. The study validates the power law for connectivity, identifies a marginal network compression in evolution, unveils hub connectivity patterns, and encourages the exploration of the correlation between protein structure, connectivity, and function. These findings offer valuable insights into network design principles and the critical role of hub proteins in cellular function and evolution.

Human-Brain Artificial-Intelligence Matrix is a new technology aims to connect the human brain with the machine for the purpose of enabling the human brain to perform defined functions even if it becomes unable to perform them such as performing the function of vision in case of blindness, the function of hearing in case of deafness, Performing the function of motion in case of paralysis and many other functions. This technology will be based on the Cognition Theory which I argue about that the whole process of cognition can be treated quantum-mechanically. The cognition starts when a neuron sends data to be processed in the brain and ends in an effector to respond. The data "action potential" is a current of particles which can be described quantum-mechanically as a wave-impulse based on the dual nature of the particles. The neurons are a net of entangled cells classically and quantum-mechanically. When the action potential changes the potential of the neurons, it creates quantum mechanical potential wells and barriers. The action potential perfectly transmits in and out the neurons through quantum mechanical tunnels. The form of energy before processing is not the same after, but the amount of energy is always conserved. Since the neurons are entangled during the action potential transmission, the brain and effector will be entangled during the action potential processing. The effectors cognition of data must be a discrete cognition of single-valued data from its self-adjoint matrix which entangled with brain matrix.

In this study, with the use of the information theory, we have proposed and proved a mathematical theorem by which we argue the reason for the existence of human diseases. To introduce our theoretical frame of reference, first, we put forward a modification of Shannons entropy, computed for all available proteomes, as a tool to compare systems complexity and distinguish between the several levels of biological organizations. We establish a new approach to differentiate between several taxa and corroborate our findings through the latest tree of life. Furthermore, we found that human proteins with higher mutual information, derived from our theorem, are more prone to be involved in human diseases. We further discuss the dynamics of protein network stability and offer probable scenarios for the existence of human diseases and their varying occurrence rates. Moreover, we account for the reasoning behind our mathematical theorem and its biological inferences.

Transcription factors play a key role in controlling which proteins are made by a cell. As transcription factors are themselves proteins, they are part of a complex interconnected and self-regulated network. We define the transcription factor basal regulatory network (TFBRN) as the network formed by the interactions between transcription factors (TFs) as proteins acting on target genes which are themselves TFs. The question then becomes as to whether topological features of this network are important in determining phenotypes caused by perturbations in TFs. To explore this, we developed two simple TFBRN models; one based on data from human TFs, and the other on the budding yeast. Even from this basic model we did find some very clear correlations between local topological measures and phenotypes seen in cancer and rare genetic diseases. This strongly suggests that the local network architecture of the TFBRN provides important information around the roles of transcription factors and the impacts to an organisation of their perturbation.\n\nAuthor SummaryThe human body is controlled by proteins whose production is coordinated by proteins known as transcription factors. These transcription factors can control multiple proteins, including other transcription factors. Does this network itself play any role in determining the properties of the transcription factors and their roles in cancer and disease? In this paper we find that there is a relationship between the local structures in the network and processes such as cancer and rare genetic diseases. We also found a similar relationship between local network characteristics and budding yeast phenotypes. This work therefore shows that simple properties of the network of interactions between transcription factors and their targets can be useful in determining the effects caused by changes in transcription factors (whether through deletion or allelic variation).

Replication fork progression complex plays an essential role during DNA replication. It travels along with the DNA with a particular speed called replication fork speed. Faithful duplication of the genome requires strict control over replication fork speed. Both acceleration and pausing mechanisms of the replication fork complex are regulated at the molecular level. Based on the experimental evidence, DNA replicates faster in normal cells than cancer cells, whereas cancer cells duplicate themselves more quickly than normal cells. Then in principle, accelerating the replication fork complex in cancer cells beyond a specific threshold speed limit can cause DNA damage and plausibly kill them. A modular mathematical model is proposed to explain the dynamics of replication fork control during DNA replication using the underlying molecular mechanisms in yeast which can extend to the mammalian system in the future.

The ability of some microorganisms to switch from respiration to fermentation in the presence of oxygen-the so-called Crabtree effect-has been a fascinating subject of study at the theoretical and experimental fronts. Game-theoretical approaches have been routinely used to examine and explain the way a microorganism, such as yeast, would switch between the two ATP-producing pathways, i.e., respiration and fermentation. Here we attempt to explain the switch between respiration and fermentation in yeast by constructing a simple metabolic switch. We then utilise an individual-based model, in which each individual is equipped with all the relevant chemical reactions, to see how cells equipped with such metabolic switch would behave in different conditions. We further investigate our proposed metabolic switch using the game-theoretical approach. Based on this model, we postulate that individuals play a mixed game of glucose metabolism in the population. This approach not only sheds some light in the varieties of metabolic regulations that can be utilised by the individual in the population in competition with others for a common resource, it would also allow a better understanding of the causes of the Warburg effect and similar phenomena observed in nature.

Biological phenomena include unrecognized events. These unrecognized events often unknowingly increase observer bias, which inhibits open and reproducible science. In this study, we focused on the recognition procedures underlying primary data and modeled cell population behavior. Using agent-based modeling (ABM), we demonstrated that cellular behaviors can be categorized into 11 distinct types, a framework we defined as the Behavioral Eleven Cell Class (BECC). We further validated BECC by describing the differential STAT3 phosphorylation patterns in leukocyte subsets and polarized T cell differentiation in OT-II transgenic mice. BECC serves as a novel descriptive method with three defining features: (i) it functions as a symbolic system independent of numerical or linguistic constraints, (ii) it represents the minimal unit of recognition, and (iii) it enables the expression of inherently unrecognizable phenomena. BECC allows for observer subjectivity and the relativity of results while enforcing rigorous recognition. This unique approach provides a practical and conceptual basis for advancing open and reproducible science.

Published research articles are rich sources of data when the knowledge is incorporated into models. Complex biological systems benefit from computational modelings ability to elucidate dynamics, explain data and address hypotheses. Modeling of pancreatic cancer could guide treatment of this devastating disease that has a known mutational profile disrupting signaling pathways but no reliable therapies. The approach described here is to utilize discrete modeling of the major signaling pathways, metabolism and the tumor microenvironment including macrophages. This modeling approach allows for abstraction in order to assemble large networks to capture numerous facets of the biological system under investigation. The Hallmarks of Cancer are represented as the processes of apoptosis, autophagy, cell cycle progression, inflammation, immune response, oxidative phosphorylation and proliferation. The model is initialized with pancreatic cancer receptors and mutations and simulated in time. The model portrays the hallmarks of cancer and suggests combinations of inhibitors as therapies.

Lung cancer is one of the most common cancers in human. However, it is still lack of understanding the mechanisms of a normal cell developing to the cancer cell. Here we develop the chromosome dynamic structural model and quantify the important characteristics of the chromosome structural ensemble of the normal lung cell and the lung cancer A549 cell. Our results demonstrate the essential relationship among the chromosome ensemble, the epigenetic marks, and the gene expressions, which suggests the linkage between chromosome structure and function. The analysis reveals that the lung cancer cell may have higher level of relative ensemble fluctuation as well as higher degree of the phase separation between the two compartments than the normal lung cells. In addition, the significant conformational "switching off" events (from compartment A to B) are more than the significant conformational "switching on" events during the lung cancerization. The kinetic lung cancerization pathway is not the same as the reversion pathway by characterizing the hot spots and interaction networks of the lung cancer transitions. These investigations have revealed the cell fate determination mechanism of the lung cancer process, which will be helpful for the further prevention and control of cancers.

1Reprogramming of the standard genetic code in order to include non-canonical amino acids (ncAAs) opens a new perspective in medicine, industry and biotechnology. There are several methods of engineering the code, which allow us for storing new genetic information in DNA sequences and transmitting it into the protein world. Here, we investigate the problem of optimal genetic code extension from theoretical perspective. We assume that the new coding system should encode both canonical and new ncAAs using 64 classical codons. What is more, the extended genetic code should be robust to point nucleotide mutation and minimize the possibility of reversion from new to old information. In order to do so, we follow graph theory to study the properties of optimal codon sets, which can encode 20 canonical amino acids and stop coding signal. Finally, we describe the set of vacant codons that could be assigned to new amino acids. Moreover, we discuss the optimal number of the newly incorporated ncAAs and also the optimal size of codon blocks that are assigned to ncAAs.