Efficient modeling, simulation and coarse-graining of biological complexity with NFsim

Managing the overwhelming numbers of molecular states and interactions is a fundamental obstacle to building predictive models of biological systems. Here we introduce the Network-Free Stochastic Simulator (NFsim), a general-purpose modeling platform that overcomes the combinatorial nature of molecular interactions. Unlike standard simulators that represent molecular species as variables in equations, NFsim uses a biologically intuitive representation: objects with binding and modification sites acted on by reaction rules. During simulations, rules operate directly on molecular objects to produce exact stochastic results with performance that scales independently of the reaction network size. Reaction rates can be defined as arbitrary functions of molecular states to provide powerful coarse-graining capabilities, for example to merge Boolean and kinetic representations of biological networks. NFsim enables researchers to simulate many biological systems that were previously inaccessible to general-purpose software, as we illustrate with models of immune system signaling, microbial signaling, cytoskeletal assembly and oscillating gene expression.     

Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.     

Expert System for Modeling and Simulating the Action of Electric and Electromagnetic Fields on Biological Membranes

The biological effects and applications in the developing technology involving electric and electromagnetic fields are as promising as they are diverse. Their effects, leading to remission in certain patients, can be obtained through electroporation, electrochemotherapy, electrotherapy, electroimmunotherapy, and gene electrotherapy. The main therapeutic uses of electromagnetic fields (EMF) are the introduction of chemical or organic substances into opportunely opened cells (electro-chimiotherapy) and the stimulation of specific elements of the immune system (electro-immunotherapy). Their benefits can be modeled by the use of expert systems, constructed to mimic human reasoning. As well as testing new therapies, such systems can analyze and synthesize existing data, and provide a new pedagogical device, and can be implemented on the Internet network. These techniques can be performed conjointly with other therapies like X-ray therapy, neutrotherapy and, in certain conditions, will optimize their effects. Some mathematical models, representing the electromagnetic field's action on cellular membranes, have been elaborated by means of the SADT method (a structured hierarchy modular method) and implanted into the expert system SEI4. This expert system simulates the immune system's behavior when facing electromagnetic fields, in the face of immunodeficient illness such as some cancers or AIDS.     

"Immunetworks", intersecting circuits and dynamics

This paper proposes a study of biological regulation networks based on a multi-level strategy. Given a network, the first structural level of this strategy consists in analysing the architecture of the network interactions in order to describe it. The second dynamical level consists in relating the patterns found in the architecture to the possible dynamical behaviours of the network. It is known that circuits are the patterns that play the most important part in the dynamics of a network in the sense that they are responsible for the diversity of its asymptotic behaviours. Here, we pursue further this idea and argue that beyond the influence of underlying circuits, intersections of circuits also impact significantly on the dynamics of a network and thus need to be payed special attention to. For some genetic regulation networks involved in the control of the immune system (“immunetworks”), we show that the small number of attractors can be explained by the presence, in the underlying structures of these networks, of intersecting circuits that “inter-lock”.     

Psychoneuroimmunology: The problem of the situatedness of illness and the conceptualization of healing

Psychoneuroimmunology claims to go beyond narrow biological perspectives of illness to consider behavioral components as an integral part of health and disease. The conceptualization of the embodiment of behavioral dimensions and how they may be represented in terms of interaction between the central nervous system and the immune system are therefore central theoretical issues. Psychoneuroimmunology is thus an arena in which questions about the body and person in context should come to the fore. There are multiple approaches in the psychoneuroimmunological literature, including those which attempt to address in some fashion the issue of the situatedness of illness. It is argued here that the problem of the representation of situatedness is the primary axis of tension in current research and writing in psychoneuroimmunology. Diverse attempts to represent extremely complex (and non-linear) relationships between behavioral and biological dimensions of immune system functioning drive a number of researchers, though they operate under disciplinary, institutional, and funding constraints in the U.S. which tend to work against the development of competing or radical models within psychoneuroimmunology itself.     

On simulating strongly-interacting, stochastic population models

Simulating strongly-interacting biological populations over many orders-of-magnitude can be challenging even on fast modern computers. Here we describe a regime-switching technique that can speed up simulations from stochastic models by large factors, while retaining third-order accuracy at each time-step. We apply it to an elementary model of the immune response to an infectious organism.     

Biological soil crusts (biocrusts) as a model system in community,landscape and ecosystem ecology

Model systems have had a profound influence on the development of ecological theory and general principles. Compared to alternatives, the most effective models share some combination of the following characteristics: simpler, smaller, faster, general, idiosyncratic or manipulable. We argue that biological soil crusts (biocrusts) have unique combinations of these features that should be more widely exploited in community, landscape and ecosystem ecology. In community ecology, biocrusts are elucidating the importance of biodiversity and spatial pattern for maintaining ecosystem multifunctionality due to their manipulability in experiments. Due to idiosyncrasies in their modes of facilitation and competition, biocrusts have led to new models on the interplay between environmental stress and biotic interactions and on the maintenance of biodiversity by competitive processes. Biocrusts are perhaps one of the best examples of micro-landscapes—real landscapes that are small in size. Although they exhibit varying patch heterogeneity, aggregation, connectivity and fragmentation, like macro-landscapes, they are also compatible with well-replicated experiments (unlike macro-landscapes). In ecosystem ecology, a number of studies are imposing small-scale, low cost manipulations of global change or state factors in biocrust micro-landscapes. The versatility of biocrusts to inform such disparate lines of inquiry suggests that they are an especially useful model system that can enable researchers to see ecological principles more clearly and quickly.     

A Single-Parameter Model of the Immune Response to Bacterial Invasion

The human immune response to bacterial pathogens is a remarkably complex process, involving many different cell types, chemical signals, and extensive lines of communication. Mathematical models of this system have become increasingly high-dimensional and complicated, as researchers seek to capture many of the major dynamics. In this paper, we argue that, in some important instances, preference should be given to low-dimensional models of immune response, as opposed to their high-dimensional counterparts. One such model is analyzed and shown to reflect many of the key phenomenological properties of the immune response in humans. Notably, this model includes a single parameter that, when combined with a single set of reference parameter values, may be used to quantify the overall immunocompetence of individual hosts.     

Exact epidemic models on graphs using graph-automorphism driven lumping

The dynamics of disease transmission strongly depends on the properties of the population contact network. Pair-approximation models and individual-based network simulation have been used extensively to model contact networks with non-trivial properties. In this paper, using a continuous time Markov chain, we start from the exact formulation of a simple epidemic model on an arbitrary contact network and rigorously derive and prove some known results that were previously mainly justified based on some biological hypotheses. The main result of the paper is the illustration of the link between graph automorphisms and the process of lumping whereby the number of equations in a system of linear differential equations can be significantly reduced. The main advantage of lumping is that the simplified lumped system is not an approximation of the original system but rather an exact version of this. For a special class of graphs, we show how the lumped system can be obtained by using graph automorphisms. Finally, we discuss the advantages and possible applications of exact epidemic models and lumping.     

Immune memory, immune oblivion: a lesson from Funes the memorious

We commonly think of the immune system as having a memory. However, memory is always accompanied by a complementary process of oblivion. Is there immune oblivion? In this theoretical paper, I address this question and suggest that oblivion is an integral aspect of memorization. In this context, I suggest that immune memory is an orchestration of reversible and irreversible processes of biological computation through feedback loops. Drawing on the linguistic metaphor, I inquire into the implications of this idea for a better understanding of immune memory and immune deficiency among the elderly.     

Stress-induced immune dysfunction: implications for health

Folk wisdom has long suggested that stressful events take a toll on health. The field of psychoneuroimmunology (PNI) is now providing key mechanistic evidence about the ways in which stressors--and the negative emotions that they generate--can be translated into physiological changes. PNI researchers have used animal and human models to learn how the immune system communicates bidirectionally with the central nervous and endocrine systems and how these interactions impact on health.     

The stress response and immune system share,borrow, and reconfigure their physiological network elements: Evidence from the insects

The classic biomedical view is that stress hormone effects on the immune system are largely pathological, especially if the stress is chronic. However, more recent interpretations have focused on the potential adaptive function of these effects. This paper examines stress response-immune system interactions from a physiological network perspective, using insects because of their simpler physiology. For example, stress hormones can reduce disease resistance, yet activating an immune response results in the release of stress hormones in both vertebrates and invertebrates. From a network perspective, this phenomenon is consistent with the ‘sharing’ of the energy-releasing ability of stress hormones by both the stress response and the immune system. Stress-induced immunosuppression is consistent with the stress response ‘borrowing’ molecular components from the immune system to increase the capacity of stress-relevant physiological processes (i.e. a trade off). The insect stress hormones octopamine and adipokinetic hormone can also ‘reconfigure’ the immune system to help compensate for the loss of some of the immune system's molecular resources (e.g. apolipophorin III). This view helps explain seemingly maladaptive interactions between the stress response and immune system. The adaptiveness of stress hormone effects on individual immune components may be apparent only from the perspective of the whole organism. These broad principles will apply to both vertebrates and invertebrates.     

Beyond phytohaemagglutinin: assessing vertebrate immune function across ecological contexts

1. Over the past decade, there has been a substantial increase in interest in the immune system and the role it plays in the regulation of disease susceptibility, giving rise to the field of eco-immunology. 2. Eco-immunology aims to understand changes in host immune responses in the broader framework of an organism's evolutionary, ecological and life-history contexts. 3. The immune system, however, is complex and multifaceted and can be intimidating for the nonimmunologist interested in incorporating immunological questions into their research. Which immune responses should one measure and what is the biological significance of these measures? 4. The focus of this review is to describe a wide range of eco-immunology techniques, from the simple to the sophisticated, with the goal of providing researchers with a range of options to consider incorporating in their own research programs. 5. These techniques were chosen because they provide relatively straightforward, biologically meaningful assessments of immune function, many of which can be performed across a range of ecological contexts (i.e. field vs. laboratory) and in a wide range of vertebrate animals without relying on species-specific reagents. 6. By incorporating assessments of immune function into their specific research questions, animal ecologists will gain a more comprehensive understanding of organism-environment interactions.     

COVID-19: systemic pathology and its implications for therapy

Responding to the coronavirus disease 2019 (COVID-19) pandemic has been an unexpected and unprecedented global challenge for humanity in this century. During this crisis, specialists from the laboratories and frontline clinical personnel have made great efforts to prevent and treat COVID-19 by revealing the molecular biological characteristics and epidemic characteristics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, SARS-CoV-2 has severe consequences for public health, including human respiratory system, immune system, blood circulation system, nervous system, motor system, urinary system, reproductive system and digestive system. In the review, we summarize the physiological and pathological damage of SARS-CoV-2 to these systems and its molecular mechanisms followed by clinical manifestation. Concurrently, the prevention and treatment strategies of COVID-19 will be discussed in preclinical and clinical studies. With constantly unfolding and expanding scientific understanding about COVID-19, the updated information can help applied researchers understand the disease to build potential antiviral drugs or vaccines, and formulate creative therapeutic ideas for combating COVID-19 at speed.     

Artificial Immune Systems-Based Classification Model for Code-Mixed Social Media Data

The main focus of the paper is to propose an artificial immune systems-based classification model for code-mixed social media data. The artificial immune systems are computational models inspired by the biological immune system. In this paper, artificial immune systems are used to optimize the initial parameters of Long short-term memory (LSTM) model. The proposed artificial immune systems-based LSTM model is then used for the classification of code-mixed data. The classification of Hindi-English code-mixed data into Hindi, English, and ambiguous words is done. Popular word embedding features were used for the representation of each word. The word embedding features and character embedding features have been used. The proposed method helps in identifying the word context by extracting the intent of user for using the ambiguous word in code-mixed sentence. Extensive experiments reveal that the artificial immune systems-based classification model outperforms competitive models especially when there are some ambiguous words in the social media data.     

Antiidiotypic networks

Jerne envisions the immune system as a web of V domains that constitutes an idiotypic network. He thinks that regulatory processes governed by idiotypic interactions can explain the generation of the various immune states. We discuss a few models that furnish information about the possible configuration of this immune network: closed or open ended. It appears that closed configurations only can generate stable immune states. Moreover, we cite some experimental data in favor of the network hypothesis. We show how they can lead to propose the structure of functional regulatory circuits whose cellular and molecular interactions are mediated by idiotypic recognition processes.     

Some threshold and stability results for epidemic models with a density-dependent death rate.

Most classical models for infectious diseases assume that the birth and death rates of individuals and the meeting rates between susceptible and infected individuals do not depend on the total number of individuals in the population. While these assumptions are valid in some situations they are less valid in others. For example, for diseases in animal an insects populations competition for scarce resources might well mean that the death rate depends on the number of individuals. The present paper examines two epidemic models where the death rate is density dependent. For each model the possible equilibrium levels of disease incidence are determined and the stability of these equilibrium levels to small perturbations is discussed. The biological interpretation of these results is presented together with the results of some numerical simulations.