636 research outputs found
Process Calculi Abstractions for Biology
Several approaches have been proposed to model biological systems by means of the formal techniques and tools available in computer science. To mention just a few of them, some representations are inspired by Petri Nets theory, and some other by stochastic processes. A most recent approach consists in interpreting the living entities as terms of process calculi where the behavior of the represented systems can be inferred by applying syntax-driven rules. A comprehensive picture of the state of the art of the process calculi approach to biological modeling is still missing. This paper goes in the direction of providing such a picture by presenting a comparative survey of the process calculi that have been used and proposed to describe the behavior of living entities. This is the preliminary version of a paper that was published in Algorithmic Bioprocesses. The original publication is available at http://www.springer.com/computer/foundations/book/978-3-540-88868-
Combining Machine Learning and Formal Methods for Complex Systems Design
During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements.
In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above.
In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study.During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements.
In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above.
In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study
Computational Complexity of Atomic Chemical Reaction Networks
Informally, a chemical reaction network is "atomic" if each reaction may be
interpreted as the rearrangement of indivisible units of matter. There are
several reasonable definitions formalizing this idea. We investigate the
computational complexity of deciding whether a given network is atomic
according to each of these definitions.
Our first definition, primitive atomic, which requires each reaction to
preserve the total number of atoms, is to shown to be equivalent to mass
conservation. Since it is known that it can be decided in polynomial time
whether a given chemical reaction network is mass-conserving, the equivalence
gives an efficient algorithm to decide primitive atomicity.
Another definition, subset atomic, further requires that all atoms are
species. We show that deciding whether a given network is subset atomic is in
, and the problem "is a network subset atomic with respect to a
given atom set" is strongly -.
A third definition, reachably atomic, studied by Adleman, Gopalkrishnan et
al., further requires that each species has a sequence of reactions splitting
it into its constituent atoms. We show that there is a to decide whether a given network is reachably atomic, improving
upon the result of Adleman et al. that the problem is . We
show that the reachability problem for reachably atomic networks is
-.
Finally, we demonstrate equivalence relationships between our definitions and
some special cases of another existing definition of atomicity due to Gnacadja
Investigating the role of the fusogen eff-1 and natural genetic variation in Caenorhabditis elegans seam cell development
Robustness is the ability of biological systems to produce invariant phenotypes despite perturbations. Development is especially robust to internal perturbations, like stochastic gene expression or mutations, and external perturbations, such as changes in environmental factors including temperature and nutrition. The highly invariant developmental patterning in Caenorhabditis elegans offers an ideal system to study the genetic and molecular mechanisms underlying developmental robustness. This work describes an experimental paradigm to discover the mechanistic basis and consequences of developmental robustness using the C. elegans seam cells as a model. Seam cells are lateral epidermal cells that are stem cell-like in their ability to produce differentiated cells and maintain proliferative potential. Through a forward genetic screen, I describe a novel role for the fusogen gene eff-1, which was previously known to drive cell fusion events, in the robustness of seam cell patterning. Furthermore, I show that eff-1 is not required for differentiation of seam cells, therefore I demonstrate that fusion is uncoupled from the differentiation programme. In another set of experiments, I show for the first time that the terminal number of seam cells in C. elegans is robust to standing genetic variation. A consequence of developmental robustness is the acquisition of cryptic genetic variation that does not modify the phenotype under normal conditions but manifests phenotypically upon perturbation. I demonstrate that the genetic background affects seam cell number at a higher developmental temperature of 25 C or upon mutations in the GATA transcription factor and target of the Wnt pathway, egl-18. CB4856 (Hawaii) suppressed the effect of temperature on the seam cell number compared to the lab reference N2 (United Kingdom), as well as lowered the expressivity of egl-18 mutations. Multiple regions of the genome were found to interact epistatically to modify egl-18 mutation expressivity, suggesting that a complex genetic architecture underlies seam cell development. Taken together, this work increases our knowledge on the robustness of seam cell patterning to various sources of variation.Open Acces
Learning Spatio-Temporal Specifications for Dynamical Systems
Learning dynamical systems properties from data provides important insights
that help us understand such systems and mitigate undesired outcomes. In this
work, we propose a framework for learning spatio-temporal (ST) properties as
formal logic specifications from data. We introduce SVM-STL, an extension of
Signal Signal Temporal Logic (STL), capable of specifying spatial and temporal
properties of a wide range of dynamical systems that exhibit time-varying
spatial patterns. Our framework utilizes machine learning techniques to learn
SVM-STL specifications from system executions given by sequences of spatial
patterns. We present methods to deal with both labeled and unlabeled data. In
addition, given system requirements in the form of SVM-STL specifications, we
provide an approach for parameter synthesis to find parameters that maximize
the satisfaction of such specifications. Our learning framework and parameter
synthesis approach are showcased in an example of a reaction-diffusion system.Comment: 12 pages, submitted to L4DC 202
Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks
Throughout history, the development of artificial intelligence, particularly
artificial neural networks, has been open to and constantly inspired by the
increasingly deepened understanding of the brain, such as the inspiration of
neocognitron, which is the pioneering work of convolutional neural networks.
Per the motives of the emerging field: NeuroAI, a great amount of neuroscience
knowledge can help catalyze the next generation of AI by endowing a network
with more powerful capabilities. As we know, the human brain has numerous
morphologically and functionally different neurons, while artificial neural
networks are almost exclusively built on a single neuron type. In the human
brain, neuronal diversity is an enabling factor for all kinds of biological
intelligent behaviors. Since an artificial network is a miniature of the human
brain, introducing neuronal diversity should be valuable in terms of addressing
those essential problems of artificial networks such as efficiency,
interpretability, and memory. In this Primer, we first discuss the
preliminaries of biological neuronal diversity and the characteristics of
information transmission and processing in a biological neuron. Then, we review
studies of designing new neurons for artificial networks. Next, we discuss what
gains can neuronal diversity bring into artificial networks and exemplary
applications in several important fields. Lastly, we discuss the challenges and
future directions of neuronal diversity to explore the potential of NeuroAI
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond
Graph neural networks (GNNs) have demonstrated significant promise in
modelling relational data and have been widely applied in various fields of
interest. The key mechanism behind GNNs is the so-called message passing where
information is being iteratively aggregated to central nodes from their
neighbourhood. Such a scheme has been found to be intrinsically linked to a
physical process known as heat diffusion, where the propagation of GNNs
naturally corresponds to the evolution of heat density. Analogizing the process
of message passing to the heat dynamics allows to fundamentally understand the
power and pitfalls of GNNs and consequently informs better model design.
Recently, there emerges a plethora of works that proposes GNNs inspired from
the continuous dynamics formulation, in an attempt to mitigate the known
limitations of GNNs, such as oversmoothing and oversquashing. In this survey,
we provide the first systematic and comprehensive review of studies that
leverage the continuous perspective of GNNs. To this end, we introduce
foundational ingredients for adapting continuous dynamics to GNNs, along with a
general framework for the design of graph neural dynamics. We then review and
categorize existing works based on their driven mechanisms and underlying
dynamics. We also summarize how the limitations of classic GNNs can be
addressed under the continuous framework. We conclude by identifying multiple
open research directions
Artificial Neurogenesis: An Introduction and Selective Review
International audienceIn this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this " meta-design " for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations ; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchange-ability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature
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