A computable expression of closure to efficient causation

In this paper, we propose a mathematical expression of closure to efficient causation in terms of λ-calculus; we argue that this opens up the perspective of developing principled computer simulations of systems closed to efficient causation in an appropriate programming language. An important implication of our formulation is that, by exhibiting an expression in λ-calculus, which is a paradigmatic formalism for computability and programming, we show that there are no conceptual or principled problems in realizing a computer simulation or model of closure to efficient causation. We conclude with a brief discussion of the question whether closure to efficient causation captures all relevant properties of living systems. We suggest that it might not be the case, and that more complex definitions could indeed create crucial some obstacles to computability.     

ABPL

Computer analysis of biological systems, using approaches such as metabolic control analysis is common. A typical example is a language like Herbert Sauro's SCAMP (Sauro & Fell, 1991), which allows simulations of enzyme systems, and calculation of control coefficients and elasticities. However such systems are motivated by the underlying biochemical theory and often have limitations as programming languages which mean that they can only be applied to particular classes of problems. ABPL (a biochemical programming language) extends these ideas by adding all the facilities of a fully-fledged programming language, together with some of the capabilities of a modern computer algebra system. Syntactically it derives from the programming language LISP, while the underlying functionality is that of iMAP, the successor to SCAMP. This provides us with a computer system capable of performing most of the tasks undertaken by existing packages, but more importantly, a system which can be easily extended into new areas. Key features of the work are:

- Ability to use the language both interactively and as a batch programming language

- Ability to work both symbolically and numerically

- Ability to handle matrices and vectors

- Ability to define and manipulate reaction schemes

- Common techniques are built in to the language

- Ability to add new operations to the language

The implementation is in ANSI standard C for portability.     

An automated laboratory control system: collection and analysis of behavioral and electro-physiological data.

A system of man-machine interactive PDP-11 assembly language programs is described which presents stimuli to a subject and records and analyzes behavioral and evoked potential data. The system was designed for researchers with no knowledge of computer programming and enables the user to create complicated sequences of stimulus presentations ("trials") and sequences of successive trials ("runs"), with no new programming required. The system is written for DEC.s DECLAB 11/40 system.     

“Macroscopic pattern analysis” based on formal analogies as a scientific methodology for complex systems

Bioprocess engineering at present concentrates on the enormous problems in the environment. What is therefore needed is a sound methodology, which should be based on the interactions between the physiology of biological reaction networks and physical processes in the environment and which should be an analogy to bioreactor performance. The conventional methodology is empirically oriented, using pilot plant data for the experimental estimates of process economics, where all further details are elucidated following the mechanistic approach on the microscopic level based on assumed mechanisms (causalities). According to the new view, the new systems-based methodology uses mathematical models as approximations and includes all the interactions. Pilot plant data are needed for model falsification, using analogies on the formal macroscopic level. Bioreactor scale-up as one application is a more rapid procedure of reasonable accuracy, where both the biokinetics as well as the fluid dynamics are quantified using formal macroscopic analogies. Model consistency and plausibility are the basic criteria when using model computer simulations as a decisive aid, while experiments lose their central role and are on longer the basis for evaluating the scientific work; they are simply the basis of the researcher's intuition. Another typical feature of complex systems is that model parameters are interdependent. The final decisive fact will be the mental experiment (“thinking” with the left and right side of the brain), which can be supported using computer simulations. This evolutionary interplay between the three realities of thinking, experimenting and simulating leads to a holistic progress towards the better understanding of highly complex systems. It offers the solution to the problems, which is needed in future.     

Program Code Generator for Cardiac Electrophysiology Simulation with Automatic PDE Boundary Condition Handling

Clinical and experimental studies involving human hearts can have certain limitations. Methods such as computer simulations can be an important alternative or supplemental tool. Physiological simulation at the tissue or organ level typically involves the handling of partial differential equations (PDEs). Boundary conditions and distributed parameters, such as those used in pharmacokinetics simulation, add to the complexity of the PDE solution. These factors can tailor PDE solutions and their corresponding program code to specific problems. Boundary condition and parameter changes in the customized code are usually prone to errors and time-consuming. We propose a general approach for handling PDEs and boundary conditions in computational models using a replacement scheme for discretization. This study is an extension of a program generator that we introduced in a previous publication. The program generator can generate code for multi-cell simulations of cardiac electrophysiology. Improvements to the system allow it to handle simultaneous equations in the biological function model as well as implicit PDE numerical schemes. The replacement scheme involves substituting all partial differential terms with numerical solution equations. Once the model and boundary equations are discretized with the numerical solution scheme, instances of the equations are generated to undergo dependency analysis. The result of the dependency analysis is then used to generate the program code. The resulting program code are in Java or C programming language. To validate the automatic handling of boundary conditions in the program code generator, we generated simulation code using the FHN, Luo-Rudy 1, and Hund-Rudy cell models and run cell-to-cell coupling and action potential propagation simulations. One of the simulations is based on a published experiment and simulation results are compared with the experimental data. We conclude that the proposed program code generator can be used to generate code for physiological simulations and provides a tool for studying cardiac electrophysiology.     

Programming for the DNA analysis of FCM data on an IBM microcomputer

The analysis of data generated on a flow cytometer (FCM) is often performed on a computer obtained especially for dedicated use with the flow cytometer. This computer component can be expensive and also presents the FCM user with the added burden of mastering specialized programming language or of accepting the secret analytical processes of protected proprietary program routines. We believe that the evolution of more accurate and efficient FCM analyses that have the power to consider complex signal distributions can be assisted by the availability of analysis programs written in languages common to many users. DNA analysis routines written for a relatively inexpensive microcomputer (IBM PC/XT) in Basic and Pascal are described here. The routines can automatically process multiple FCM data files and can provide high-resolution graphic hardcopy. A foreground/background utilization is also described that allows the computer to be available for other uses in the laboratory.     

Controlling behavioral experiments with a new programming language (SORCA) for microcomputer systems

A new programming language SORCA has been defined and a compiler has been written for Z80-based microcomputer systems with CP/M operating system. The language was developed to control behavioral experiments by external stimuli and by time schedule in real-time. Eight binary hardware input lines are sampled cyclically by the computer and can be used to sense switches, level detectors and other binary information, while 8 binary hardware output lines, that are cyclically updated, can be used to control relays, lamps, generate tones or for other purposes. The typical reaction time (cycle time) of a SORCA-program is 500 microseconds to 1 ms. All functions can be programmed as often as necessary. Included are the basic logic functions, counters, timers, majority gates and other complex functions. Parameters can be given as constants or as a result of a step function or of a random process (with Gaussian or equal distribution). Several tasks can be performed simultaneously. In addition, results of an experiment (e.g., number of reactions or latencies) can be measured and printed out on request or automatically. The language is easy to learn and can also be used for many other control purposes.     

A New Space-Time Computer Simulation Method for Human Migration

Migration is a universal but poorly understood human behavior, and new analytic tools for studying migration are badly needed. In this article, I describe a new computer simulation method for migrating human populations, which moves simulated human actors" on a lattice of points according to simple probability rules. The method includes models of birth, death, crowding, and competition, as well as of migration. Simulations are developed for a number of specific problems, including migration into empty continents, the effect of competition and migration on the spatial distribution of populations, the effect of biased migration on urban population structure, and the distribution of city sizes determined by migration. Direct comparison of simulations with measured population distributions demonstrates the plausibility of the models. The simulations suggest that seemingly complex problems of human space-time population dynamics may be resolved into simple behavioral rules and that migration phenomena can be subjected to scientific analysis. [Key words: migration, computer simulation, random walk, urban structures]     

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Enzyme catalysis evolved in an aqueous environment. The influence of solvent dynamics on catalysis is, however, currently poorly understood and usually neglected. The study of water dynamics in enzymes and the associated thermodynamical consequences is highly complex and has involved computer simulations, nuclear magnetic resonance (NMR) experiments, and calorimetry. Water tunnels that connect the active site with the surrounding solvent are key to solvent displacement and dynamics. The protocol herein allows for the engineering of these motifs for water transport, which affects specificity, activity and thermodynamics. By providing a biophysical framework founded on theory and experiments, the method presented herein can be used by researchers without previous expertise in computer modeling or biophysical chemistry. The method will advance our understanding of enzyme catalysis on the molecular level by measuring the enthalpic and entropic changes associated with catalysis by enzyme variants with obstructed water tunnels. The protocol can be used for the study of membrane-bound enzymes and other complex systems. This will enhance our understanding of the importance of solvent reorganization in catalysis as well as provide new catalytic strategies in protein design and engineering.     

Numerically evaluated functional equivalence between chaotic dynamics in neural networks and cellular automata under totalistic rules

Chaotic dynamics in a recurrent neural network model and in two-dimensional cellular automata, where both have finite but large degrees of freedom, are investigated from the viewpoint of harnessing chaos and are applied to motion control to indicate that both have potential capabilities for complex function control by simple rule(s). An important point is that chaotic dynamics generated in these two systems give us autonomous complex pattern dynamics itinerating through intermediate state points between embedded patterns (attractors) in high-dimensional state space. An application of these chaotic dynamics to complex controlling is proposed based on an idea that with the use of simple adaptive switching between a weakly chaotic regime and a strongly chaotic regime, complex problems can be solved. As an actual example, a two-dimensional maze, where it should be noted that the spatial structure of the maze is one of typical ill-posed problems, is solved with the use of chaos in both systems. Our computer simulations show that the success rate over 300 trials is much better, at least, than that of a random number generator. Our functional simulations indicate that both systems are almost equivalent from the viewpoint of functional aspects based on our idea, harnessing of chaos.     

VirtualLeaf: an open-source framework for cell-based modeling of plant tissue growth and development

Plant organs, including leaves and roots, develop by means of a multilevel cross talk between gene regulation, patterned cell division and cell expansion, and tissue mechanics. The multilevel regulatory mechanisms complicate classic molecular genetics or functional genomics approaches to biological development, because these methodologies implicitly assume a direct relation between genes and traits at the level of the whole plant or organ. Instead, understanding gene function requires insight into the roles of gene products in regulatory networks, the conditions of gene expression, etc. This interplay is impossible to understand intuitively. Mathematical and computer modeling allows researchers to design new hypotheses and produce experimentally testable insights. However, the required mathematics and programming experience makes modeling poorly accessible to experimental biologists. Problem-solving environments provide biologically intuitive in silico objects ("cells", "regulation networks") required for setting up a simulation and present those to the user in terms of familiar, biological terminology. Here, we introduce the cell-based computer modeling framework VirtualLeaf for plant tissue morphogenesis. The current version defines a set of biologically intuitive C++ objects, including cells, cell walls, and diffusing and reacting chemicals, that provide useful abstractions for building biological simulations of developmental processes. We present a step-by-step introduction to building models with VirtualLeaf, providing basic example models of leaf venation and meristem development. VirtualLeaf-based models provide a means for plant researchers to analyze the function of developmental genes in the context of the biophysics of growth and patterning. VirtualLeaf is an ongoing open-source software project (http://virtualleaf.googlecode.com) that runs on Windows, Mac, and Linux.     

The cortical column, a new processing unit for cortex-like networks

We propose in this paper a new connectionnist model which is the result of a collaboration between computer science and neurobiology researchers. This model is based upon a cortical-like network close to cortical function simulation. We present its functional and architectural characteristics together with the very encouraging results obtained for the first simulations on the visual and auditory cortical functions in speech and character recognition.     

A flexible forward simulator for populations subject to selection and demography

This article introduces a new forward population genetic simulation program that can efficiently generate samples from populations with complex demographic histories under various models of natural selection. The program (SFS_CODE) is highly flexible, allowing the user to simulate realistic genomic regions with several loci evolving according to a variety of mutation models (from simple to context-dependent), and allows for insertions and deletions. Each locus can be annotated as either coding or non-coding, sex-linked or autosomal, selected or neutral, and have an arbitrary linkage structure (from completely linked to independent). AVAILABILITY: The source code (written in the C programming language) is available at http://sfscode.sourceforge.net, and a web server (http://cbsuapps.tc.cornell.edu/sfscode.aspx) allows the user to perform simulations using the high-performance computing cluster hosted by the Cornell University Computational Biology Service Unit.     

Armadillo 1.1: an original workflow platform for designing and conducting phylogenetic analysis and simulations

In this paper we introduce Armadillo v1.1, a novel workflow platform dedicated to designing and conducting phylogenetic studies, including comprehensive simulations. A number of important phylogenetic and general bioinformatics tools have been included in the first software release. As Armadillo is an open-source project, it allows scientists to develop their own modules as well as to integrate existing computer applications. Using our workflow platform, different complex phylogenetic tasks can be modeled and presented in a single workflow without any prior knowledge of programming techniques. The first version of Armadillo was successfully used by professors of bioinformatics at Université du Quebec à Montreal during graduate computational biology courses taught in 2010-11. The program and its source code are freely available at: .     

Software development of the EMI head scanner.

A method has been developed to run the general purpose operating system RDOS on the same disc of the head scanner computer as is used for scanner software and data. This made it possible to develop additional software in high level programming language for image processing, based on original image data on the disc. All new images produced by the program are stored on the disc in the same format as the original images. This makes it possible to handle processed images exactly as the original ones and to do multiple operations. The following processing has been included in the program so far: subtraction, smoothing, density profiles, vertical reconstructions, magnification and labelling. A set of operator commands has been developed which are very similar to the ordinary commands for the scanner, which makes the program to appear being a direct extension of the standard scanner software.     

Setup of a scientific computing environment for computational biology: Simulation of a genome-scale metabolic model of Escherichia coli as an example

Computational analysis of biological data is becoming increasingly important, especially in this era of big data. Computational analysis of biological data allows efficiently deriving biological insights for given data, and sometimes even counterintuitive ones that may challenge the existing knowledge. Among experimental researchers without any prior exposure to computer programming, computational analysis of biological data has often been considered to be a task reserved for computational biologists. However, thanks to the increasing availability of user-friendly computational resources, experimental researchers can now easily access computational resources, including a scientific computing environment and packages necessary for data analysis. In this regard, we here describe the process of accessing Jupyter Notebook, the most popular Python coding environment, to conduct computational biology. Python is currently a mainstream programming language for biology and biotechnology. In particular, Anaconda and Google Colaboratory are introduced as two representative options to easily launch Jupyter Notebook. Finally, a Python package COBRApy is demonstrated as an example to simulate 1) specific growth rate of Escherichia coli as well as compounds consumed or generated under a minimal medium with glucose as a sole carbon source, and 2) theoretical production yield of succinic acid, an industrially important chemical, using E. coli. This protocol should serve as a guide for further extended computational analyses of biological data for experimental researchers without computational background.     

Construction,visualization, and analysis of biological network models in Dynetica

Mathematical modeling has become an increasingly important aspect of biological research. Computer simulations help to improve our understanding of complex systems by testing the validity of proposed mechanisms and generating experimentally testable hypotheses. However, significant overhead is generated by the creation, debugging, and perturbation of these computational models and their parameters, especially for researchers who are unfamiliar with programming or numerical methods. Dynetica 2.0 is a user-friendly dynamic network simulator designed to expedite this process. Models are created and visualized in an easy-to-use graphical interface, which displays all of the species and reactions involved in a graph layout. System inputs and outputs, indicators, and intermediate expressions may be incorporated into the model via the versatile “expression variable” entity. Models can also be modular, allowing for the quick construction of complex systems from simpler components. Dynetica 2.0 supports a number of deterministic and stochastic algorithms for performing time-course simulations. Additionally, Dynetica 2.0 provides built-in tools for performing sensitivity or dose response analysis for a number of different metrics. Its parameter searching tools can optimize specific objectives of the time course or dose response of the system. Systems can be translated from Dynetica 2.0 into MATLAB code or the Systems Biology Markup Language (SBML) format for further analysis or publication. Finally, since it is written in Java, Dynetica 2.0 is platform independent, allowing for easy sharing and collaboration between researchers.     

Algorithmic and complexity results for decompositions of biological networks into monotone subsystems

A useful approach to the mathematical analysis of large-scale biological networks is based upon their decompositions into monotone dynamical systems. This paper deals with two computational problems associated to finding decompositions which are optimal in an appropriate sense. In graph-theoretic language, the problems can be recast in terms of maximal sign-consistent subgraphs. The theoretical results include polynomial-time approximation algorithms as well as constant-ratio inapproximability results. One of the algorithms, which has a worst-case guarantee of 87.9% from optimality, is based on the semidefinite programming relaxation approach of Goemans-Williamson [Goemans, M., Williamson, D., 1995. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM 42 (6), 1115-1145]. The algorithm was implemented and tested on a Drosophila segmentation network and an Epidermal Growth Factor Receptor pathway model, and it was found to perform close to optimally.     

Towards aspect-oriented functional--structural plant modelling

Background and Aims

Functional–structural plant models (FSPMs) are used to integrate knowledge and test hypotheses of plant behaviour, and to aid in the development of decision support systems. A significant amount of effort is being put into providing a sound methodology for building them. Standard techniques, such as procedural or object-oriented programming, are not suited for clearly separating aspects of plant function that criss-cross between different components of plant structure, which makes it difficult to reuse and share their implementations. The aim of this paper is to present an aspect-oriented programming approach that helps to overcome this difficulty.

Methods

The L-system-based plant modelling language L+C was used to develop an aspect-oriented approach to plant modelling based on multi-modules. Each element of the plant structure was represented by a sequence of L-system modules (rather than a single module), with each module representing an aspect of the element''s function. Separate sets of productions were used for modelling each aspect, with context-sensitive rules facilitated by local lists of modules to consider/ignore. Aspect weaving or communication between aspects was made possible through the use of pseudo-L-systems, where the strict-predecessor of a production rule was specified as a multi-module.

Key Results

The new approach was used to integrate previously modelled aspects of carbon dynamics, apical dominance and biomechanics with a model of a developing kiwifruit shoot. These aspects were specified independently and their implementation was based on source code provided by the original authors without major changes.

Conclusions

This new aspect-oriented approach to plant modelling is well suited for studying complex phenomena in plant science, because it can be used to integrate separate models of individual aspects of plant development and function, both previously constructed and new, into clearly organized, comprehensive FSPMs. In a future work, this approach could be further extended into an aspect-oriented programming language for FSPMs.