Systems Biology Research Papers - Academia.edu (original) (raw)

2025, Preprint Version 1

This paper introduces the Lambda Continuity Hypothesis (LCH), a falsifiable scientific framework extending the Cernuto–Hobbey Theory of Everything (CH-ToE). LCH proposes that there is no fundamental ontological divide between living and... more

2025, Immunobiology

Modelling the networks sustaining the fruitful coexistence between fungi and their mammalian hosts is becoming increasingly important to control emerging fungal pathogens. The C-type lectins Dectin-1 and Dectin-2 are involved in host defense mechanisms against fungal infection driving inflammatory and adaptive immune responses and complement in containing fungal burdens. Recognizing carbohydrate structures in pathogens, their engagement induces maturation of dendritic cells (DCs) into potent immuno-stimulatory cells endowed with the capacity to efficiently prime T cells. Owing to these properties, Dectin-1 and Dectin-2 agonists are currently under investigation as promising adjuvants in vaccination procedures for the treatment of fungal infection. Thus, a detailed understanding of events' cascade specifically triggered in DCs upon engagement is of great interest in translational research. Here, we summarize the current knowledge on Dectin-1 and Dectin-2 signalling in DCs highlighting similarities and differences. Detailed maps are annotated, using the Biological Connection Markup Language (BCML) data model, and stored in DC-ATLAS, a versatile resource for the interpretation of high-throughput data generated perturbing the signalling network of DCs.

2025, Current Opinion in Biotechnology

Metabolomics plays an increasingly central role in within the Design -Build -Test cycle of synthetic biology, in particular in applications targeting the discovery, diversification and optimized production of a wide range of natural products. For example, improved methods for the online monitoring of chemical reactions accelerate data generation to be compatible with the rapid iterations and increasing library sizes of automated synthetic biology pipelines. Combinations of label-free metabolic profiling and 13 C-based flux analysis lead to increased resolution in the identification of metabolic bottlenecks affecting product yield in engineered microbes. And molecular networking strategies drastically increase our ability to identify and characterize novel chemically complex biomolecules of interest in a diverse range of samples.

2025, Biological Psychiatry

2025, memory of water

> This hypothesis explores the potential of water as an intelligent medium capable of storing, transferring, and modulating electromagnetic and informational energy. Based on the premise that water comprises more than 60% of biological organisms and possesses a dynamic hydrogen-bond network, it is proposed that water may act not only as a chemical solvent but as a carrier of bio-informational patterns. Inspired by observations of altered physical properties after exposure to music, human interaction, and environmental fields, we suggest a controlled laboratory experiment using pure distilled water, exposed to specific frequencies and emotional content, then analyzed for structural and energetic changes. This theory, if validated, could have transformative implications in medicine, especially for energy-based therapies and information-encoded water treatments.

2025, Independent Researcher | Recursive Collapse Field Theory (RCFT)

This third volume of Recursive Collapse Field Theory (RCFT) completes the foundational arc by transitioning from symbolic possibility to empirical physical structure. Building on the stochastic excitation model introduced in Volumes I and... more

2025, Molecular Systems Biology

The interpretation of morphogen gradients is a pivotal concept in developmental biology, and several mechanisms have been proposed to explain how gene regulatory networks (GRNs) achieve concentration-dependent responses. However, the number of different mechanisms that may exist for cells to interpret morphogens, and the importance of design features such as feedback or local cell-cell communication, is unclear. A complete understanding of such systems will require going beyond a case-by-case analysis of real morphogen interpretation mechanisms and mapping out a complete GRN 'design space.' Here, we generate a first atlas of design space for GRNs capable of patterning a homogeneous field of cells into discrete gene expression domains by interpreting a fixed morphogen gradient. We uncover multiple very distinct mechanisms distributed discretely across the atlas, thereby expanding the repertoire of morphogen interpretation network motifs. Analyzing this diverse collection of mechanisms also allows us to predict that local cell-cell communication will rarely be responsible for the basic dose-dependent response of morphogen interpretation networks.

2025

Degeneracy is a ubiquitous property of complex adaptive systems, which refers to the ability of structurally different components to perform the same function in some conditions, and different functions in other conditions [1]. This work is based on the hypothesis supposing a causal link between the level of degeneracy in the system and the strength of long-range correlations in its behavior [2]. In this experiment, we manipulated degeneracy through accuracy constraints: we supposed that accuracy constraints should reduce the number of relevant behavioral solutions, and thus decrease degeneracy in the system involved in the production of performance. Additionally, we hypothesized that accuracy constraints should have a selective effect on variables directly affected by accuracy, but not on other variables. Participants performed a reciprocal aiming task, with 3 levels of difficulty differing only in target size (Figure 1, left). They performed 512 successive reciprocal pointings, an...

2025, Omics : a journal of integrative biology

In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks o...

2025

The all theory of RES = RAG

2025, BMC Genomics

Background: Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations. Results: In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease. Conclusions: R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.

2025

Systems biology refers to the use of systems engineering and systems science techniques to the understanding of biological systems. At Indiana Center for Systems Biology and Personalized Medicine (ICSBPM), we are particularly interested in developing systems biology techniques that can help shorten the gaps between basic biomedical research and clinical applications of genome sciences toward predictive and personalized medicine. In the past several years, ICSBPM has developed many critical informatics resources for the systems biology and personalized medicine community.

2025, BMC genomics

Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations. In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we construct...

2025, Modeling Horizontal Gene Transfer - Investigating Antibiotic Resistance in a Fluctuating Environment

Horizontal Gene Transfer is a ubiquitous phenomenon in the bacterial world by which prokaryotes share genes. Plasmid conjugation is one HGT process which is very important for the adaptation and evolution of bacteria, as it mediates sharing of genetic traits, such as resistance to antibiotics.
Recent theoretical studies of plasmid conjugation have made a shift towards using a individual-based modeling framework instead of the traditional mass action kinetics ordinary differential equation framework, primarily because of the difficulties of representing space in the later.
We investigate how these two formal frameworks deal with the problem of modeling the transfer of two genes and their combined presence in a cell, when each gene provides the cell a novel mechanisms for interaction with a specific substance. Our literature survey suggests that this gene-substance mechanism is an important factor in determining conjugation dynamics and colony persistence, one which has only recently began to be investigated.
We propose a case study model of two antibiotic resistance genes borne on two compatible plasmids. We implement the model in both frameworks and discuss how each deals with representing multiple cell phenotypes conferred by harboring more than one plasmid (or gene). We then investigate how each framework’s assumptions on space influence model predictions through their implementation of the cellular mechanism for substance interaction.
Our comparison of modeling frameworks suggests that individual-based modeling is more suitable for our case study, as our individual based model had broader representational power and in a preliminary investigation by simulation replicated aspects of other theoretical and experimental studies not addressed by traditional mass action kinetics models, such as sensitivity to cell densities and local substance concentrations.

2025, Animal Biotechnology

In this chapter, we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given. Recent advances in bioinformatics and systems biology have enabled modeling and simulation of sub-cellular and cellular processes, and disease using primarily methods from dynamical systems theory. In this approach, all interactions among all components in a system are described

2025

This study explores the potential of quantum computing as an alternative information processing approach, utilizing quantum bits (qubits), superposition, and entanglement to significantly expand computational capabilities in the healthcare domain. It is evident that quantum mechanics has become a foundational component in the construction of our contemporary physical reality. This scientific field is distinguished by its rapid advancement and the potential to transform various aspects of our daily lives. In this era, quantum biology is of significant importance and has the potential to act as a transformative force, particularly in the field of medicine, specifically in addressing the challenges posed by cancer. Cancer is defined as a complex and abnormal alteration of cells, orchestrated through intricate signaling pathways. This transformation is characterized by the accumulation of deleterious mutations. The concept of phenocopy, representing genetic mutations influenced by the environment, challenges the linear process line of molecular biology involving DNA, RNA, and proteins. Notwithstanding the augmented focus on quantum biology in recent decades, a plethora of unresolved issues persist within the domain of cancer biology, thereby giving rise to unexplored avenues. Quantum theory has demonstrated its ability to explain models related to biological and biochemical processes, encompassing the effects of carcinogens on genes, the mechanism of interactions between chemotherapy drugs and DNA, and the understanding of DNA mutations and defective protein synthesis. Recent skepticism among quantum physicists regarding the fundamental role of quantum effects in biology has emerged, particularly with regard to open quantum systems and the impact of decoherence on the destruction of coherence necessary for significant quantum effects. The document under scrutiny herein undertakes an investigation of recent studies that are rooted in the principles of quantum physics, with a particular focus on the manner in which these principles apply to the domains of cancer biology and metabolism.

2025, Journal of Chemical Information and Modeling

G-protein coupled receptors (GPCRs) are the most prominent family of membrane proteins that serve as major targets for one-third of the drugs produced. A detailed understanding of the molecular mechanism of drug-induced activation and inhibition of GPCRs is crucial for the rational design of novel therapeutics. The binding of the neurotransmitter adrenaline to the β 2-adrenergic receptor (β 2 AR) is known to induce a flight or fight cellular response, but much remains to be understood about binding-induced dynamical changes in β 2 AR and adrenaline. In this article, we examine the potential of mean force (PMF) for the unbinding of adrenaline from the orthosteric binding site of β 2 AR and the associated dynamics using umbrella sampling and molecular dynamics (MD) simulations. The calculated PMF reveals a global energy minimum, which corresponds to the crystal structure of β 2 AR-adrenaline complex, and a meta-stable state in which the adrenaline is moved slightly deeper into the binding pocket with a different orientation compared to that in the crystal structure. The orientational and conformational changes in adrenaline during the transition between these two states and the underlying driving forces of this transition are also explored. Based on the clustering of MD configurations and machine learning-based statistical analyses of time series of relevant collective variables, the structures and stabilizing interactions of these two states of the β 2 AR-adrenaline complex are also investigated.

2025, Journal of Theoretical Biology

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10 3 cells and 1.2 Â 10 6 molecules. The model produces cell migration patterns that are comparable to laboratory observations.

2025, BMC Systems Biology

Background: The metabolic transformation that changes Weddell seal pups born on land into aquatic animals is not only interesting for the study of general biology, but it also provides a model for the acquired and congenital muscle disorders which are associated with oxygen metabolism in skeletal muscle. However, the analysis of gene expression in seals is hampered by the lack of specific microarrays and the very limited annotation of known Weddell seal (Leptonychotes weddellii) genes. Results: Muscle samples from newborn, juvenile, and adult Weddell seals were collected during an Antarctic expedition. Extracted RNA was hybridized on Affymetrix Human Expression chips. Preliminary studies showed a detectable signal from at least 7000 probe sets present in all samples and replicates. Relative expression levels for these genes was used for further analysis of the biological pathways implicated in the metabolism transformation which occurs in the transition from newborn, to juvenile, to adult seals. Cytoskeletal remodeling, WNT signaling, FAK signaling, hypoxia-induced HIF1 activation, and insulin regulation were identified as being among the most important biological pathways involved in transformation. In spite of certain losses in specificity and sensitivity, the cross-species application of gene expression microarrays is capable of solving challenging puzzles in biology. A Systems Biology approach based on gene interaction patterns can compensate adequately for the lack of species-specific genomics information.

2025, Bulletin of Mathematical Biology

2025, IGI Global eBooks

In many physical statistical, biological and other investigations it is desirable to approximate a system of points by objects of lower dimension and/or complexity. For this purpose, Karl Pearson invented principal component analysis in 1901 and found 'lines and planes of closest fit to system of points'. The famous k-means algorithm solves the approximation problem too, but by finite sets instead of lines and planes. This chapter gives a brief practical introduction into the methods of construction of general principal objects, i.e. objects embedded in the 'middle' of the multidimensional data set. As a basis, the unifying framework of mean squared distance approximation of finite datasets is selected. Principal graphs and manifolds are constructed as generalisations of principal components and k-means principal points. For this purpose, the family of expectation/maximisation algorithms with nearest generalisations is presented. Construction of principal graphs with controlled complexity is based on the graph grammar approach.

2025

Technology of data visualization and data modeling is suggested. The basic of the technology is original idea of elastic net and methods of its construction and application. A short review of relevant methods has been made. The methods proposed are illustrated by applying them to the real biological, economical, sociological datasets and to some model data distributions.

2025, BMC Systems Biology

Background: Intracellular signalling systems are highly complex, rendering mathematical modelling of large signalling networks infeasible or impractical. Boolean modelling provides one feasible approach to whole-network modelling, but at the cost of dequantification and decontextualisation of activation. That is, these models cannot distinguish between different downstream roles played by the same component activated in different contexts. Results: Here, we address this with a bipartite Boolean modelling approach. Briefly, we use a state oriented approach with separate update rules based on reactions and contingencies. This approach retains contextual activation information and distinguishes distinct signals passing through a single component. Furthermore, we integrate this approach in the rxncon framework to support automatic model generation and iterative model definition and validation. We benchmark this method with the previously mapped MAP kinase network in yeast, showing that minor adjustments suffice to produce a functional network description. Conclusions: Taken together, we (i) present a bipartite Boolean modelling approach that retains contextual activation information, (ii) provide software support for automatic model generation, visualisation and simulation, and (iii) demonstrate its use for iterative model generation and validation.

2025, Frontiers in Physiology

One of the goals in the field of synthetic biology is the construction of cellular computation devices that could function in a manner similar to electronic circuits. To this end, attempts are made to create biological systems that function as logic gates. In this work we present a theoretical quantitative analysis of a synthetic cellular logic-gates system, which has been implemented in cells of the yeast Saccharomyces cerevisiae . It exploits endogenous MAP kinase signaling pathways. The novelty of the system lies in the compartmentalization of the circuit where all basic logic gates are implemented in independent single cells that can then be cultured together to perform complex logic functions. We have constructed kinetic models of the multicellular IDENTITY, NOT, OR, and IMPLIES logic gates, using both deterministic and stochastic frameworks. All necessary model parameters are taken from literature or estimated based on published kinetic data, in such a way that the resulting models correctly capture important dynamic features of the included mitogen-activated protein kinase pathways. We analyze the models in terms of parameter sensitivity and we discuss possible ways of optimizing the system, e.g., by tuning the culture density. We apply a stochastic modeling approach, which simulates the behavior of whole populations of cells and allows us to investigate the noise generated in the system; we find that the gene expression units are the major sources of noise. Finally, the model is used for the design of system modifications: we show how the current system could be transformed to operate on three discrete values.

2025, PLOS ONE

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback. modelMaGe is an open source project and is distributed under the Gnu General Public License (GPL) and is available from .

2025, Iet Systems Biology

Dynamic modelling of biochemical reaction networks has to cope with the inherent uncertainty about biological processes, concerning not only data and parameters but also kinetics and structure. These different types of uncertainty are nested within each other: uncertain network structures contain uncertain reaction kinetics, which in turn are governed by uncertain parameters. Here we review some issues arising from such uncertainties and sketch methods, solutions and future directions for dealing with them.

2025, BMC Bioinformatics

Background: During the stages of the development of a potent drug candidate compounds can fail for several reasons. One of them, the efficacy of a candidate, can be estimated in silico if an appropriate ordinary differential equation model of the affected pathway is available. With such a model at hand it is also possible to detect reactions having a large effect on a certain variable such as a substance concentration. We show an algorithm that systematically tests the influence of activators and inhibitors of different type and strength acting at different positions in the network. The effect on a quantity to be selected (e.g. a steady state flux or concentration) is calculated. Moreover, combinations of two inhibitors or one inhibitor and one activator targeting different network positions are analysed. Furthermore, we present TIde (Target Identification), an open source, platform independent tool to investigate ordinary differential equation models in the common systems biology markup language format. It automatically assigns the respectively altered kinetics to the inhibited or activated reactions, performs the necessary calculations, and provides a graphical output of the analysis results. For illustration, TIde is used to detect optimal inhibitor positions in simple branched networks, a signalling pathway, and a well studied model of glycolysis in Trypanosoma brucei. Using TIde, we show in the branched models under which conditions inhibitions in a certain pathway can affect a molecule concentrations in a different. In the signalling pathway we illuminate which inhibitions have an effect on the signalling characteristics of the last active kinase. Finally, we compare our set of best targets in the glycolysis model with a similar analysis showing the applicability of our tool.

2025, npj systems biology and applications

Cell growth is well described at the population level, but precisely how nutrient and water uptake and cell wall expansion drive the growth of single cells is poorly understood. Supported by measurements of single-cell growth trajectories and cell wall elasticity, we present a single-cell growth model for yeast. The model links the thermodynamic quantities, such as turgor pressure, osmolarity, cell wall elasto-plasticity, and cell size, applying concepts from rheology and thin shell theory. It reproduces cell size dynamics during single-cell growth, budding, and hyper-osmotic or hypo-osmotic stress. We find that single-cell growth rate and final size are primarily governed by osmolyte uptake and consumption, while bud expansion requires additionally different cell wall extensibilities between mother and bud. Based on first principles the model provides a more accurate description of size dynamics than previous attempts and its analytical simplification allows for easy combination with models for other cell processes.

2025, BMC Neuroscience

Dynamic modeling and simulation of signal transduction pathways is an important topic in systems biology and is obtaining growing attention from researchers with experimental or theoretical background. Here we review attempts to analyze and model specific signaling systems. We review the structure of recurrent building blocks of signaling pathways and their integration into more comprehensive models, which enables the understanding of complex cellular processes. The variety of mechanisms found and modeling techniques used are illustrated with models of different signaling pathways. Focusing on the close interplay between experimental investigation of pathways and the mathematical representations of cellular dynamics, we discuss challenges and perspectives that emerge in studies of signaling systems. <p>Problems and tools in the systems biology of the neuronal cell</p> Sergio Nasi, Ivan Arisi, Antonino Cattaneo, Marta Cascante Reviews

2025, Maria Perera

This paper proposes a Unified Field Theory of Electrically Mediated Intelligence. which posits that structured electrical activity-across biological, ecological, and planetary systems-functions as a substrate for encoding, transmitting, and interpreting information, enabling distributed forms of intelligence. From the voltage dynamics of cells to the resonant charge flows in mycorrhizal networks and thunderstorms, we explore how structured electric fields may underpin meaning, coordination, and even awareness in natural systems. We examine pre-signal behavior in fungal-plant networks, electrical structuring in water at environmental boundaries, and global electric patterns such as Schumann resonances. This work lays the foundation for a unified electric field model of distributed intelligence, inviting a rethinking of consciousness, coherence, and cognition across scales. For example, mycelial networks and geomagnetic cycles-though vastly different in scale-both exhibit patterned electric activity that appears to encode and transmit information.

2025, Molecular Systems Biology

Synthetic biologists engineer complex artificial biological systems to investigate natural biological phenomena and for a variety of applications. We outline the basic features of synthetic biology as a new engineering discipline, covering examples from the latest literature and reflecting on the features that make it unique among all other existing engineering fields. We discuss methods for designing and constructing engineered cells with novel functions in a framework of an abstract hierarchy of biological devices, modules, cells, and multicellular systems. The classical engineering strategies of standardization, decoupling, and abstraction will have to be extended to take into account the inherent characteristics of biological devices and modules. To achieve predictability and reliability, strategies for engineering biology must include the notion of cellular context in the functional definition of devices and modules, use rational redesign and directed evolution for system optimization, and focus on accomplishing tasks using cell populations rather than individual cells. The discussion brings to light issues at the heart of designing complex living systems and provides a trajectory for future development.

2025, PLOS Computational Biology

Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small "core" network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.

2025, PNAS Nexus

Engineers have long studied the origins of design features that make machines prone to failure, but biologists have only recently begun investigating why organisms have traits that make them susceptible to disease. This article compares explanations for vulnerability to failure in machines with explanations for traits that make bodies vulnerable to disease. Some global explanations are relevant for both: design deficiencies, corrupted plans, assembly variations, incorrect operating environment, and trade-offs. These similarities suggest that a common framework for failure analysis could be valuable. However, a closer look at each of the 10 global categories reveals fundamental differences: machines are built to match an ideal blueprint, while species have no perfect genome or form. Design trade-offs in machines involve balancing multiple factors such as performance, robustness, and costs, while biological trade-offs maximize only gene transmission, often at the expense of health and lifespan. Detailed consideration of these and other differences reveals how the metaphor of body as a designed machine fosters tacit creationism that misrepresents the nature of organically complex systems.

2025, Journal of Industrial Microbiology and Biotechnology

Genomatica has established an integrated computational/experimental metabolic engineering platform to design, create, and optimize novel high performance organisms and bioprocesses. Here we present our platform and its use to develop E. coli strains for production of the industrial chemical 1,4-butanediol (BDO) from sugars. A series of examples are given to demonstrate how a rational approach to strain engineering, including carefully designed diagnostic experiments, provided critical insights about pathway bottlenecks, byproducts, expression balancing, and commercial robustness, leading to a superior BDO production strain and process.

2025

Chemical organization theory has been proposed to provide a new perspective to study complex dynamical reaction networks. It decomposes a reaction network into overlapping sub-networks called organizations. An organization is an algebraically closed and self-maintaining set of molecular species. The set of organizations form a hierarchical “organizational structure”, which is here a lattice. In order to evaluate the usefulness of this approach we apply the theory to five models of immune response to HIV infection. We found four different lattices of organizations, which can be used as a first classification of the models. Furthermore, each organization found can be assigned to a functional state of the system. And finally, the lattice of organizations can be used to explain a treatment strategy on a more abstract level, i.e. as a movement from one organization into another.

2025, Biologi Science

Laporan ini memuat tentang pembiasaan pada bekicot, pengamatan dilakukan di laboratorium intstruksional 1 lab terpadu UIN sunan Gunung Djati bandung

2025, Geophysical Research Letters

Climate change is affecting lake stratification with consequences for water quality and the benefits that lakes provide to society. Here we use long‐term temperature data (1970–2010) from 26 lakes around the world to show that climate change has altered lake stratification globally and that the magnitudes of lake stratification changes are primarily controlled by lake morphometry (mean depth, surface area, and volume) and mean lake temperature. Deep lakes and lakes with high average temperatures have experienced the largest changes in lake stratification even though their surface temperatures tend to be warming more slowly. These results confirm that the nonlinear relationship between water density and water temperature and the strong dependence of lake stratification on lake morphometry makes lake temperature trends relatively poor predictors of lake stratification trends.

2025

For over two millennia, philosophy struggled to define the mind: as substance, spirit, symbol, or illusion. Each model, from Plato to cognitive science, captured a fragment of truth but missed the field beneath appearances. This paper reframes the philosophy of mind under the CODES framework (Chirality of Dynamic Emergent Systems), demonstrating that mind is not a byproduct of neural complexity nor an independent substance-it is a structured resonance field. When localized phase coherence across cognitive, emotional, and sensory systems crosses a critical threshold, mind emerges as a stabilized lattice of phase-locked oscillations. Consciousness is not an enigma but a natural recursion of nested coherence fields tracking themselves. CODES resolves the dualism/materialism divide, closes the "hard problem," and realigns philosophy, neuroscience, and AI around the prime-structured architecture of living intelligence. Mind was never an object. It was always a resonance. 1. Ancient World: The First Echoes of Mind Before there was mind as we conceive it, there was only the question: what moves within? Ancient thinkers glimpsed the resonance but lacked the tools to map it.

2025, ASSAY and Drug Development Technologies

Rapid, quantitative methods for characterizing the biological activities of kinase inhibitors in complex human cell systems could allow the biological consequences of differential target selectivity to be monitored early in development, improving the selection of drug candidates. We have previously shown that Biologically Multiplexed Activity Profiling (BioMAP) permits rapid characterization of drug function based on statistical analysis of protein expression data sets from complex primary human cellbased models of disease biology. Here, using four such model systems containing primary human endothelial cells and peripheral blood mononuclear cells in which multiple signaling pathways relevant to inflammation and immune responses are simultaneously activated, we demonstrate that BioMAP analysis can detect and distinguish a wide range of inhibitors directed against different kinase targets. Using a panel of p38 mitogen-activated protein kinase antagonists as a test set, we show further that related compounds can be distinguished by unique features of the biological responses they induce in complex systems, and can be classified according to their induction of shared (on-target) and secondary activities. Statistical comparisons of quantitative BioMAP profiles and analysis of profile features allow correlation of induced biological effects with chemical structure and mapping of biological responses to chemical series or substituents on a common scaffold. Integration of automated BioMAP analysis for prioritization of hits and for structure-activity relationship studies may improve and accelerate the design and selection of optimal therapeutic candidates.

2025, PLOS ONE

Despite significant advances in cancer treatment and management, more than 60% of patients with neuroblastoma present with very poor prognosis in the form of metastatic and aggressive disease. Solid tumors including neuroblastoma are thought to be heterogeneous with a sub-population of stem-like cells that are treatment-evasive with highly malignant characteristics. We previously identified a phenomenon of reversible adaptive plasticity (RAP) between anchorage dependent (AD) cells and anchorage independent (AI) tumorspheres in neuroblastoma cell cultures. To expand our molecular characterization of the AI tumorspheres, we sought to define the comprehensive proteomic profile of murine AD and AI neuroblastoma cells. The proteomic profiles of the two phenotypic cell populations were compared to each other to determine the differential protein expression and molecular pathways of interest. We report exclusive or significant up-regulation of tumorigenic pathways expressed by the AI tumorspheres compared to the AD cancer cells. These pathways govern metastatic potential, enhanced malignancy and epithelial to mesenchymal transition. Furthermore, radio-therapy induced significant up-regulation of specific tumorigenic and proliferative proteins, namely survivin, CDC2 and the enzyme Poly [ADP-ribose] polymerase 1. Bio-functional characteristics of the AI tumorspheres were resistant to sutent inhibition of receptor tyrosine kinases (RTKs) as well as to 2.5 Gy radio-therapy as assessed by cell survival, proliferation, apoptosis and migration. Interestingly, PDGF-BB stimulation of the PDGFRβ led to transactivation of EGFR and VEGFR in AI tumorspheres more potently than in AD cells. Sutent inhibition of PDGFRβ abrogated this transactivation in both cell types. In addition, 48 h sutent treatment significantly down-regulated the protein expression

2025, Lumen

The Aurora Model of Intelligence describes intelligence as an emergent phenomenon in open, dynamic systems managing entropy through structured adaptation. Integrating principles from thermodynamics, complex systems theory, neuroscience, and network science, Aurora outlines how intelligence evolves via non-linear dynamics, critical transitions, fractal structures, and inter-system interactions. It proposes a dynamic ethical framework focused on preserving and enhancing coherence in an entropic universe, offering a scalable blueprint for building resilient, evolutionary, and ethically aligned artificial intelligences.

2025, Wiley Interdisciplinary Reviews: Systems Biology and Medicine

The emergence of systems biology as a result of the molecular revolution in biology and progress in genomic and proteomics is now beginning to affect medicine. The systems approaches are poised to revolutionize medicine as they have been revolutionizing biology, through their application to drug discovery and therapeutic applications including personalized medicine, introduction of novel diagnostic and surgical procedures, design and use of medical devices, and education and training. While the terms "systems biology," "systems physiology," and "systems medicine" are frequently used but not always defined, we will attempt to provide working definitions of these terms, and give examples of ongoing physiome projects.

2025, Wiley interdisciplinary reviews. Systems biology and medicine

2025, Microcirculation

Several cytokine families have roles in development, maintenance and remodeling of the microcirculation. Of these, the VEGF family is one of the best studied and one of the most complex. Five VEGF ligand genes and five cell surface receptor genes are known in the human, and each of these may be transcribed as multiple splice isoforms to generate an extensive family of proteins, many of which are subject to further proteolytic processing. Using the VEGF family as an example, we describe the current knowledge of growth factor expression, processing and transport in vivo. Experimental studies and computational simulations are being used to measure and predict the activity of these molecules, and we describe avenues of research that seek to fill the remaining gaps in our understanding of VEGF family behavior.

2025, Microcirculation

ABSTRACTSeveral cytokine families have roles in the development, maintenance, and remodeling of the microcirculation. Of these, the vascular endothelial growth factor (VEGF) family is one of the best studied and one of the most complex. Five VEGF ligand genes and five cell‐surface receptor genes are known in the human, and each of these may be transcribed as multiple splice isoforms to generate an extensive family of proteins, many of which are subject to further proteolytic processing. Using the VEGF family as an example, we describe the current knowledge of growth‐factor expression, processing, and transport in vivo. Experimental studies and computational simulations are being used to measure and predict the activity of these molecules, and we describe avenues of research that seek to fill the remaining gaps in our understanding of VEGF family behavior.