A Logical Framework for Systems Biology

We propose a novel approach for the formal verification of biological systems based on the use of a modal linear logic. We show how such a logic can be used, with worlds as instants of time, as an unified framework to encode both biological systems and temporal properties of their dynamic behaviour. To illustrate our methodology, we consider a model of the P53/Mdm2 DNA-damage repair mechanism. We prove several properties that are important for such a model to satisfy and serve to illustrate the promise of our approach. We formalize the proofs of these properties in the Coq Proof Assistant, with the help of a Lambda Prolog prover for partial automation of the proofs

Visual setup of logical models of signaling and regulatory networks with ProMoT

BACKGROUND: The analysis of biochemical networks using a logical (Boolean) description is an important approach in Systems Biology. Recently, new methods have been proposed to analyze large signaling and regulatory networks using this formalism. Even though there is a large number of tools to set up models describing biological networks using a biochemical (kinetic) formalism, however, they do not support logical models. RESULTS: Herein we present a flexible framework for setting up large logical models in a visual manner with the software tool ProMoT. An easily extendible library, ProMoT's inherent modularity and object-oriented concept as well as adaptive visualization techniques provide a versatile environment. Both the graphical and the textual description of the logical model can be exported to different formats. CONCLUSION: New features of ProMoT facilitate an efficient set-up of large Boolean models of biochemical interaction networks. The modeling environment is flexible; it can easily be adapted to specific requirements, and new extensions can be introduced. ProMoT is freely available from

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here,we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field. Scientists from different systems biology fields have long been developing community-driven guidelines and best practices for annotation, interoperability and reusability of computational models in biology. However, the parallel work, grounded on shared needs and similar aims, of separate communities creates a need for exchange and alignment of the different efforts to harmonise best practices. Hence, members of the Consortium for Logical Models and Tools (CoLoMoTo, http://colomoto.org) and the Computational Modelling of Biological Systems community of the International Society for Computational Biology (SysMod, https:// sysmod.info/) organised aworkshop to discusscommunitydriven guidelines and efforts for the curation and annotation of computational models during [BC]2 2021. The workshop grew from a previous edition organised during [BC]2 2019 focused on logical modelling [1]. The second edition brought together scientists with various research backgrounds and from different working groups such as BioModels [2], a central repository of mathematical models of biological/biomedical processes; the Computational Modelling in Biology Network initiative (COMBINE) [3]; CoLoMoTo, [4]; SysMod, [5]; the Systems Biology Graphical Notation (SBGN) project [6]; the systems biology markup language (SBML) [7] and simulation experiment description markup language (SED-ML) [8], to exchange and expand on several key topics of common interest (Figure 1). While the modelling approaches across these communities differ, several critical points are shared, such as (i) the importance of annotations for reproducibility, (ii) the use of community standards for exchange and annotation encoding, (iii) the need to implement standards in tools and platforms to boost reusability and interoperability, (iv) the importance of transparency of modelling frameworks in publications and (v) the use of shared repositories to enhance model accessibility (Figure 2). We use the term annotation to describe ‘a computeraccessible metadata item that captures, entirely or in part, the meaning of a model, model component or data element’. We borrow this definition from [9] which is in accordance with its use in [1]. We discuss the identified needs in the following sections

Computational approaches to analyse and predict small molecule transport and distribution at cellular and subcellular levels

Quantitative structure–activity relationship (QSAR) studies and mechanistic mathematical modeling approaches have been independently employed for analysing and predicting the transport and distribution of small molecule chemical agents in living organisms. Both of these computational approaches have been useful for interpreting experiments measuring the transport properties of small molecule chemical agents, in vitro and in vivo . Nevertheless, mechanistic cell‐based pharmacokinetic models have been especially useful to guide the design of experiments probing the molecular pathways underlying small molecule transport phenomena. Unlike QSAR models, mechanistic models can be integrated from microscopic to macroscopic levels, to analyse the spatiotemporal dynamics of small molecule chemical agents from intracellular organelles to whole organs, well beyond the experiments and training data sets upon which the models are based. Based on differential equations, mechanistic models can also be integrated with other differential equations‐based systems biology models of biochemical networks or signaling pathways. Although the origin and evolution of mathematical modeling approaches aimed at predicting drug transport and distribution has occurred independently from systems biology, we propose that the incorporation of mechanistic cell‐based computational models of drug transport and distribution into a systems biology modeling framework is a logical next step for the advancement of systems pharmacology research. Copyright © 2013 John Wiley & Sons, Ltd

Intransitivity in Theory and in the Real World

This work considers reasons for and implications of discarding the assumption of transitivity, which (transitivity) is the fundamental postulate in the utility theory of Von Neumann and Morgenstern, the adiabatic accessibility principle of Caratheodory and most other theories related to preferences or competition. The examples of intransitivity are drawn from different fields, such as law, biology, game theory, economics and competitive evolutionary dynamic. This work is intended as a common platform that allows us to discuss intransitivity in the context of different disciplines. The basic concepts and terms that are needed for consistent treatment of intransitivity in various applications are presented and analysed in a unified manner. The analysis points out conditions that necessitate appearance of intransitivity, such as multiplicity of preference criteria and imperfect (i.e. approximate) discrimination of different cases. The present work observes that with increasing presence and strength of intransitivity, thermodynamics gradually fades away leaving space for more general kinetic considerations. Intransitivity in competitive systems is linked to complex phenomena that would be difficult or impossible to explain on the basis of transitive assumptions. Human preferences that seem irrational from the perspective of the conventional utility theory, become perfectly logical in the intransitive and relativistic framework suggested here. The example of competitive simulations for the risk/benefit dilemma demonstrates the significance of intransitivity in cyclic behaviour and abrupt changes in the system. The evolutionary intransitivity parameter, which is introduced in the Appendix, is a general measure of intransitivity, which is particularly useful in evolving competitive systems. Quantum preferences are also considered in the Appendix.Comment: 44 pages, 14 figures, 47 references, 6 appendice

Structural and functional analysis of cellular networks with CellNetAnalyzer

BACKGROUND: Mathematical modelling of cellular networks is an integral part of Systems Biology and requires appropriate software tools. An important class of methods in Systems Biology deals with structural or topological (parameter-free) analysis of cellular networks. So far, software tools providing such methods for both mass-flow (metabolic) as well as signal-flow (signalling and regulatory) networks are lacking. RESULTS: Herein we introduce CellNetAnalyzer, a toolbox for MATLAB facilitating, in an interactive and visual manner, a comprehensive structural analysis of metabolic, signalling and regulatory networks. The particular strengths of CellNetAnalyzer are methods for functional network analysis, i.e. for characterising functional states, for detecting functional dependencies, for identifying intervention strategies, or for giving qualitative predictions on the effects of perturbations. CellNetAnalyzer extends its predecessor FluxAnalyzer (originally developed for metabolic network and pathway analysis) by a new modelling framework for examining signal-flow networks. Two of the novel methods implemented in CellNetAnalyzer are discussed in more detail regarding algorithmic issues and applications: the computation and analysis (i) of shortest positive and shortest negative paths and circuits in interaction graphs and (ii) of minimal intervention sets in logical networks. CONCLUSION: CellNetAnalyzer provides a single suite to perform structural and qualitative analysis of both mass-flow- and signal-flow-based cellular networks in a user-friendly environment. It provides a large toolbox with various, partially unique, functions and algorithms for functional network analysis.CellNetAnalyzer is freely available for academic use

LGEM+^\text{+}: a first-order logic framework for automated improvement of metabolic network models through abduction

Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation. We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure. We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.Comment: 15 pages, one figure, two tables, two algorithm

ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra

Background: Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, with the goal to gain a better understanding of the system. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. Although there exist sophisticated algorithms to determine the dynamics of discrete models, their implementations usually require labor-intensive formatting of the model formulation, and they are oftentimes not accessible to users without programming skills. Efficient analysis methods are needed that are accessible to modelers and easy to use. Method: By converting discrete models into algebraic models, tools from computational algebra can be used to analyze their dynamics. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Results: A method for efficiently identifying attractors, and the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness, i.e., while the number of nodes in a biological network may be quite large, each node is affected only by a small number of other nodes, and robustness, i.e., small number of attractors

The Dead Hands of Group Selection and Phenomenology -- A Review of Individuality and Entanglement by Herbert Gintis 357p (2017)(review revised 2019)

Since Gintis is a senior economist and I have read some of his previous books with interest, I was expecting some more insights into behavior. Sadly, he makes the dead hands of group selection and phenomenology into the centerpieces of his theories of behavior, and this largely invalidates the work. Worse, since he shows such bad judgement here, it calls into question all his previous work. The attempt to resurrect group selection by his friends at Harvard, Nowak and Wilson, a few years ago was one of the major scandals in biology in the last decade, and I have recounted the sad story in my article ‘Altruism, Jesus and the End of the World—how the Templeton Foundation bought a Harvard Professorship and attacked Evolution, Rationality and Civilization -- A review of E.O. Wilson 'The Social Conquest of Earth' (2012) and Nowak and Highfield ‘SuperCooperators’ (2012).’ Unlike Nowak, Gintis does not seem to be motivated by religious fanaticism, but by the strong desire to generate an alternative to the grim realities of human nature, made easy by the (near universal) lack of understanding of basic human biology and blank slateism of behavioral scientists, other academics, and the general public. Gintis rightly attacks (as he has many times before) economists, sociologists and other behavioral scientists for not having a coherent framework to describe behavior. Of course, the framework needed to understand behavior is an evolutionary one. Unfortunately, he fails to provide one himself (according to his many critics and I concur), and the attempt to graft the rotten corpse of group selection onto whatever economic and psychological theories he has generated in his decades of work, merely invalidates his entire project. Although Gintis makes a valiant effort to understand and explain the genetics, like Wilson and Nowak, he is far from an expert, and like them, the math just blinds him to the biological impossibilities and of course this is the norm in science. As Wittgenstein famously noted on the first page of Culture and Value “There is no religious denomination in which the misuse of metaphysical expressions has been responsible for so much sin as it has in mathematics.” It has always been crystal clear that a gene that causes behavior which decreases its own frequency cannot persist, but this is the core of the notion of group selection. Furthermore, it has been well known and often demonstrated that group selection just reduces to inclusive fitness (kin selection), which, as Dawkins has noted, is just another name for evolution by natural selection. Like Wilson, Gintis has worked in this arena for about 50 years and still has not grasped it, but after the scandal broke, it took me only 3 days to find, read and understand the most relevant professional work, as detailed in my article. It is mind boggling to realize that Gintis and Wilson were unable to accomplish this in nearly half a century. I discuss the errors of group selection and phenomenology that are the norm in academia as special cases of the near universal failure to understand human nature that are destroying America and the world. Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Human Behavior (2019), and Suicidal Utopian Delusions in the 21st Century 4th ed (2019

Computational challenges of systems biology

Progress in the study of biological systems such as the heart, brain, and liver will require computer scientists to work closely with life scientists and mathematicians. Computer science will play a key role in shaping the new discipline of systems biology and addressing the significant computational challenges it poses