The next step in systems biology: simulating the temporospatial dynamics of molecular network

Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective

Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.

Trade-offs, Pleiotropy, and Shared Molecular Pathways: A Unified View of Constraints on Adaptation

The concept of trade-offs permeates our thinking about adaptive evolution because they are exhibited at every level of biological organization, from molecular and cellular processes to organismal and ecological functions. Trade-offs inevitably arise because different traits do not occur in isolation, but instead are imbedded within complex, integrated systems that make up whole organisms. The genetic and mechanistic underpinning of trade-offs can be found in the pleiotropic nodes that occur in the biological pathways shared between traits. Yet, often trade-offs are only understood as statistical correlations, limiting the ability to evaluate the interplay between how selection and constraint interact during adaptive evolution. Here, we first review the classic paradigms in which physiologists and evolutionary biologists have studied trade-offs and highlight the ways in which network and molecular pathway approaches unify these paradigms. We discuss how these approaches allow researchers to evaluate why trade-offs arise and how selection can act to overcome trait correlations and evolutionary constraints. We argue that understanding how the conserved molecular pathways are shared between different traits and functions provides a conceptual framework for evolutionary biologists, physiologists, and molecular biologists to meaningfully work together toward the goal of understanding why correlations and trade-offs occur between traits. We briefly highlight the melanocortin system and the hormonal control of osmoregulation as two case studies where an understanding of shared molecular pathways reveals why trade-offs occur between seemingly unrelated traits. While we recognize that applying such approaches poses challenges and limitations particularly in the context of natural populations, we advocate for the view that focusing on the biological pathways responsible for trade-offs provides a unified conceptual context accessible to a broad range of integrative biologists.

Truncated BAM receptors interfere the apical meristematic activity in a dominant negative manner when ectopically expressed in Arabidopsis

Small, secreted signaling peptides that are perceived by receptor-like kinases (RLKs) constitute an important regulatory mechanism in plant organ formation and stem cell maintenance. However, functional redundancy at the level of both ligand and receptor families often makes it difficult to clearly discern the role of individual members by a genetic approach. Here, we show that driven by a constitutive CaMV 35S promoter, a truncated BAM protein (BAMΔ) that lacks either the signal peptide (SP) or the cytoplasmic kinase (Ki) domain could cause defective shoot apical meristem (SAM) maintenance, which phenotypically resembled the triple bam mutant. Such a dominant-negative effect could also be achieved when the same transgene was driven by the native AtBAM1 promoter, but not by the CLV1 promoter. When introduced into a clv1-4 background, BAMΔ proteins abolished the typical clv phenotype by suppressing the transcriptional level of clv1-4. In addition to a clear reduction in root length and a decreased number of meristematic cells, the 35S:BAMΔ transgenic seedlings exhibited considerable resistance to CLE40p- but not to CLV3p-mediated root growth inhibition, implying that BAMs play key roles in the regulation of proximal meristem activity in root through CLE40 peptide. Findings present here not only provide evidence that truncated BAM proteins are strongly dominant negative in regulating apical meristem development but also propose that expression of a truncated version of plant LRR receptor kinase could potentially be used as a powerful tool to reveal its in vivo function in signal transduction.

Effects of ionizing radiation on cell-to-cell communication

Cell-to-cell signaling has become a significant issue in radiation biology due to experimental evidence, accumulated primarily since the early 1990s, of radiation-induced bystander effects. Several candidate mediators involved in cell-to-cell communication have been investigated and proposed as being responsible for this phenomenon, but the current investigation techniques (both theoretical and experimental) of the mechanisms involved, due to the particular set-up of each experiment, result in experimental data that often are not directly comparable. In this study, a comprehensive approach was adopted to describe cell-to-cell communication (focusing on cytokine signaling) and its modulation by external agents such as ionizing radiation. The aim was also to provide integrated theoretical instruments and experimental data to help in understanding the peculiarities of in vitro experiments. Theoretical/modeling activities were integrated with experimental measurements by (1) redesigning a cybernetic model (proposed in its original form in the 1950s) to frame cell-to-cell communication processes, (2) implementing and developing a mathematical model, and (3) designing and carrying out experiments to quantify key parameters involved in intercellular signaling (focusing as a pilot study on the release and decay of IL-6 molecules and their modulation by radiation). This formalization provides an interpretative framework for understanding the intercellular signaling and in particular for focusing on the study of cell-to-cell communication in a "step-by-step" approach. Under this model, the complex phenomenon of signal transmission was reduced where possible into independent processes to investigate them separately, providing an evaluation of the role of cell communication to guarantee and maintain the robustness of the in vitro experimental systems against the effects of perturbations.

A review of current applications of mass spectrometry for neuroproteomics in epilepsy

The brain is unquestionably the most fascinating organ, and the hippocampus is crucial in memory storage and retrieval and plays an important role in stress response. In temporal lobe epilepsy (TLE), the seizure origin typically involves the hippocampal formation. Despite tremendous progress, current knowledge falls short of being able to explain its function. An emerging approach toward an improved understanding of the complex molecular mechanisms that underlie functions of the brain and hippocampus is neuroproteomics. Mass spectrometry has been widely used to analyze biological samples, and has evolved into an indispensable tool for proteomics research. In this review, we present a general overview of the application of mass spectrometry in proteomics, summarize neuroproteomics and systems biology-based discovery of protein biomarkers for epilepsy, discuss the methodology needed to explore the epileptic hippocampus proteome, and also focus on applications of ingenuity pathway analysis (IPA) in disease research. This neuroproteomics survey presents a framework for large-scale protein research in epilepsy that can be applied for immediate epileptic biomarker discovery and the far-reaching systems biology understanding of the protein regulatory networks. Ultimately, knowledge attained through neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of epilepsy on society.

Ins and outs of systems biology vis-à-vis molecular biology: continuation or clear cut?

The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology 'revolutionizes' molecular biology and 'transcends' its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology's use of modelling and bioinformatics, and by its scale enlargement.

Applications of genome-scale metabolic reconstructions

The availability and utility of genome-scale metabolic reconstructions have exploded since the first genome-scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high-throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis-driven discovery, (4) interrogation of multi-species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome-scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.

Modelling cellular signalling systems

Cell signalling pathways and networks are complex and often non-linear. Signalling pathways can be represented as systems of biochemical reactions that can be modelled using differential equations. Computational modelling of cell signalling pathways is emerging as a tool that facilitates mechanistic understanding of complex biological systems. Mathematical models are also used to generate predictions that may be tested experimentally. In the present chapter, the various steps involved in building models of cell signalling pathways are discussed. Depending on the nature of the process being modelled and the scale of the model, different mathematical formulations, ranging from stochastic representations to ordinary and partial differential equations are discussed. This is followed by a brief summary of some recent modelling successes and the state of future models.

Emergent decision-making in biological signal transduction networks

The complexity of biochemical intracellular signal transduction networks has led to speculation that the high degree of interconnectivity that exists in these networks transforms them into an information processing network. To test this hypothesis directly, a large scale model was created with the logical mechanism of each node described completely to allow simulation and dynamical analysis. Exposing the network to tens of thousands of random combinations of inputs and analyzing the combined dynamics of multiple outputs revealed a robust system capable of clustering widely varying input combinations into equivalence classes of biologically relevant cellular responses. This capability was nontrivial in that the network performed sharp, nonfuzzy classifications even in the face of added noise, a hallmark of real-world decision-making.

Assessing cumulative health risks from exposure to environmental mixtures - three fundamental questions

Differential exposure to mixtures of environmental agents, including biological, chemical, physical, and psychosocial stressors, can contribute to increased vulnerability of human populations and ecologic systems. Cumulative risk assessment is a tool for organizing and analyzing information to evaluate the probability and seriousness of harmful effects caused by either simultaneous and/or sequential exposure to multiple environmental stressors. In this article we focus on elucidating key challenges that must be addressed to determine whether and to what degree differential exposure to environmental mixtures contributes to increased vulnerability of exposed populations. In particular, the emphasis is on examining three fundamental and interrelated questions that must be addressed as part of the process to assess cumulative risk: a) Which mixtures are most important from a public health perspective? and b) What is the nature (i.e., duration, frequency, timing) and magnitude (i.e., exposure concentration and dose) of relevant cumulative exposures for the population of interest? c) What is the mechanism (e.g., toxicokinetic or toxicodynamic) and consequence (e.g., additive, less than additive, more than additive) of the mixture's interactive effects on exposed populations? The focus is primarily on human health effects from chemical mixtures, and the goal is to reinforce the need for improved assessment of cumulative exposure and better understanding of the biological mechanisms that determine toxicologic interactions among mixture constituents.

Proteome informatics II: bioinformatics for comparative proteomics

The present review attempts to cover the most recent initiatives directed towards representing, storing, displaying and processing protein-related data suited to undertake "comparative proteomics" studies. Data interpretation is brought into focus. Efforts invested into analysing and interpreting experimental data increasingly express the need for adding meaning. This trend is perceptible in work dedicated to determining ontologies, modelling interaction networks, etc. In parallel, technical advances in computer science are spurred by the development of the Web and the growing need to channel and understand massive volumes of data. Biology benefits from these advances as an application of choice for many generic solutions. Some examples of bioinformatics solutions are discussed and directions for on-going and future work conclude the review.

Iterative approach to model identification of biological networks

BACKGROUND

Recent advances in molecular biology techniques provide an opportunity for developing detailed mathematical models of biological processes. An iterative scheme is introduced for model identification using available system knowledge and experimental measurements.

RESULTS

The scheme includes a state regulator algorithm that provides estimates of all system unknowns (concentrations of the system components and the reaction rates of their inter-conversion). The full system information is used for estimation of the model parameters. An optimal experiment design using the parameter identifiability and D-optimality criteria is formulated to provide "rich" experimental data for maximizing the accuracy of the parameter estimates in subsequent iterations. The importance of model identifiability tests for optimal measurement selection is also considered. The iterative scheme is tested on a model for the caspase function in apoptosis where it is demonstrated that model accuracy improves with each iteration. Optimal experiment design was determined to be critical for model identification.

CONCLUSION

The proposed algorithm has general application to modeling a wide range of cellular processes, which include gene regulation networks, signal transduction and metabolic networks.

Cellular Automata With Object-Oriented Features for Parallel Molecular Network Modeling

Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

Folding Pathway Mediated by an Intramolecular Chaperone: Intrinsically Unstructured Propeptide Modulates Stochastic Activation of Subtilisin

Several secreted proteases are synthesized with N-terminal propeptides that function as intramolecular chaperones (IMCs) and direct the folding of proteases to their native functional states. Using subtilisin E as our model system, we had earlier established that (i) release and degradation of the IMC from its complex with the protease upon completion of folding is the rate-determining step to protease maturation and, (ii) IMC of SbtE is an extremely charged, intrinsically unstructured polypeptide that adopts an alpha-beta structure only in the presence of the protease. Here, we explore the mechanism of IMC release and the intricate relationship between IMC structure and protease activation. We establish that the release of the first IMC from its protease domain is a non-deterministic event that subsequently triggers an activation cascade through trans-proteolysis. By in silico simulation of the protease maturation pathway through application of stochastic algorithms, we further analyze the sub-stages of the release step. Our work shows that modulating the structure of the IMC domain through external solvent conditions can vary both the time and randomness of protease activation. This behavior of the protease can be correlated to varying the release-rebinding equilibrium of IMC, through simulation. Thus, a delicate balance underlies IMC structure, release, and protease activation. Proteases are ubiquitous enzymes crucial for fundamental cellular processes and require deterministic activation mechanisms. Our work on SbtE establishes that through selection of an intrinsically unstructured IMC domain, nature appears to have selected for a viable deterministic handle that controls a fundamentally random event. While this outlines an important mechanism for regulation of protease activation, it also provides a unique approach to maintain industrially viable subtilisins in extremely stable states that can be activated at will.

Computational Approach to Systems Biology: From Fraction to Integration and Beyond

Systems biology is an approach to understanding the workings of whole biological systems. The various methods used for systems analyses range from experimental to computational. In this paper, we describe basic concepts of systems biology, modeling challenges that arise from the massively parallel interaction among components in biological systems, and what lies beyond integration of modular knowledge.